Siga este enlace para ver otros tipos de publicaciones sobre el tema: Semantic multimedia representation.
Artículos de revistas sobre el tema "Semantic multimedia representation"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte los 50 mejores artículos de revistas para su investigación sobre el tema "Semantic multimedia representation".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Explore artículos de revistas sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.
1
Mylonas, Phivos, Thanos Athanasiadis, Manolis Wallace, Yannis Avrithis, and Stefanos Kollias. "Semantic representation of multimedia content: Knowledge representation and semantic indexing." Multimedia Tools and Applications 39, no. 3 (2007): 293–327. http://dx.doi.org/10.1007/s11042-007-0161-4.
Texto completo
2
Zhang, Hong, Yu Huang, Xin Xu, Ziqi Zhu, and Chunhua Deng. "Latent semantic factorization for multimedia representation learning." Multimedia Tools and Applications 77, no. 3 (2017): 3353–68. http://dx.doi.org/10.1007/s11042-017-5135-6.
Texto completo
3
Duan, Yiping, Qiyuan Du, Xin Fang, et al. "Multimedia Semantic Communications: Representation, Encoding and Transmission." IEEE Network 37, no. 1 (2023): 44–50. http://dx.doi.org/10.1109/mnet.001.2200468.
Texto completo
4
Wagenpfeil, Stefan, Paul Mc Kevitt, and Matthias Hemmje. "Towards Automated Semantic Explainability of Multimedia Feature Graphs." Information 12, no. 12 (2021): 502. http://dx.doi.org/10.3390/info12120502.
Texto completoResumen
Multimedia feature graphs are employed to represent features of images, video, audio, or text. Various techniques exist to extract such features from multimedia objects. In this paper, we describe the extension of such a feature graph to represent the meaning of such multimedia features and introduce a formal context-free PS-grammar (Phrase Structure grammar) to automatically generate human-understandable natural language expressions based on such features. To achieve this, we define a semantic extension to syntactic multimedia feature graphs and introduce a set of production rules for phrases of natural language English expressions. This explainability, which is founded on a semantic model provides the opportunity to represent any multimedia feature in a human-readable and human-understandable form, which largely closes the gap between the technical representation of such features and their semantics. We show how this explainability can be formally defined and demonstrate the corresponding implementation based on our generic multimedia analysis framework. Furthermore, we show how this semantic extension can be employed to increase the effectiveness in precision and recall experiments.
5
Al-Khatib, W., Y. F. Day, A. Ghafoor, and P. B. Berra. "Semantic modeling and knowledge representation in multimedia databases." IEEE Transactions on Knowledge and Data Engineering 11, no. 1 (1999): 64–80. http://dx.doi.org/10.1109/69.755616.
Texto completo
6
Petridis, K., S. Bloehdorn, C. Saathoff, et al. "Knowledge representation and semantic annotation of multimedia content." IEE Proceedings - Vision, Image, and Signal Processing 153, no. 3 (2006): 255. http://dx.doi.org/10.1049/ip-vis:20050059.
Texto completo
7
Smith, Roger W., Dorota Kieronska, and Svetha Venkatesh. "Conceptual Representation for Multimedia Information." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 02 (1997): 303–27. http://dx.doi.org/10.1142/s0218001497000147.
Texto completoResumen
Multimedia information is now routinely available in the forms of text, pictures, animation and sound. Although text objects are relatively easy to deal with (in terms of information search and retrieval), other information bearing objects (such as sound, images, animation) are more difficult to index. Our research is aimed at developing better ways of representing multimedia objects by using a conceptual representation based on Schank's conceptual dependencies. Moreover, the representation allows for users' individual interpretations to be embedded in the system. This will alleviate the problems associated with traditional semantic networks by allowing for coexistence of multiple views of the same information. The viability of the approach is tested, and the preliminary results reported.
8
Yang, Bo, and Ali R. Hurson. "Similarity-Based Clustering Strategy for Mobile Ad Hoc Multimedia Databases." Mobile Information Systems 1, no. 4 (2005): 253–73. http://dx.doi.org/10.1155/2005/317136.
Texto completoResumen
Multimedia data are becoming popular in wireless ad hoc environments. However, the traditional content-based retrieval techniques are inefficient in ad hoc networks due to the multiple limitations such as node mobility, computation capability, memory space, network bandwidth, and data heterogeneity. To provide an efficient platform for multimedia retrieval, we propose to cluster ad hoc multimedia databases based on their semantic contents, and construct a virtual hierarchical indexing infrastructure overlaid on the mobile databases. This content-aware clustering scheme uses a semantic-aware framework as the theoretical foundation for data organization. Several novel techniques are presented to facilitate the representation and manipulation of multimedia data in ad hoc networks: 1) using concise distribution expressions to represent the semantic similarity of multimedia data, 2) constructing clusters based on the semantic relationships between multimedia entities, 3) reducing the cost of content-based multimedia retrieval through the restriction of semantic distances, and 4) employing a self-adaptive mechanism that dynamically adjusts to the content and topology changes of the ad hoc networks. The proposed scheme is scalable, fault-tolerant, and efficient in performing content-based multimedia retrieval as demonstrated in our combination of theoretical analysis and extensive experimental studies.
9
Chang, Xiaojun, Zhigang Ma, Yi Yang, Zhiqiang Zeng, and Alexander G. Hauptmann. "Bi-Level Semantic Representation Analysis for Multimedia Event Detection." IEEE Transactions on Cybernetics 47, no. 5 (2017): 1180–97. http://dx.doi.org/10.1109/tcyb.2016.2539546.
Texto completo
10
Jaitly, Shilpa, Vijay Laxmi, and Gagan Jindal. "Content-Based Image Retrieval and Feature Extraction: Analysing the Literature." International Journal for Research Publication and Seminar 15, no. 3 (2024): 357–73. http://dx.doi.org/10.36676/jrps.v15.i3.1520.
Texto completoResumen
A significant amount of multimedia data consists of digital images, and multimedia content analysis is used in many real-world computer vision applications. Multimedia information, especially photos, has become much more complicated in the last several years. Every day, millions of photos are posted to various websites, such as Instagram, Facebook, and Twitter. Finding a suitable image in an archive is a difficult research subject for the field of computer vision. Most search engines use standard text-based techniques that depend on metadata and captions in order to fetch photos. Over the past 20 years, a great deal of research has been conducted on content-based image retrieval (CBIR), picture categorization, and analysis. In image classification models and CBIR, high-level picture representations are represented as feature vectors made up of numerical values. Empirical evidence indicates a considerable disparity between picture feature representation and human visual understanding. Reducing the semantic gap between human visual understanding and picture feature representation is the aim of this study. This study aims to do a thorough analysis of the latest advancements in the domains of Content-Based picture Retrieval and picture representation. We performed a comprehensive analysis of many models for image retrieval and picture representation, encompassing the most recent advancements in semantic deep-learning methods and feature extraction. This paper provides an in-depth analysis of the key ideas and important studies related to image representation and content-based picture retrieval. In an effort to stimulate more research in this field, it also offers a preview of potential future study topics.
11
Luan, Xi Dao, Yu Xiang Xie, Yi Hong Tan, Sai Hu, Zhi Ping Chen, and Jing Wang. "Description Logic Based Objects and Space Relations Representation." Applied Mechanics and Materials 48-49 (February 2011): 366–72. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.366.
Texto completoResumen
This theme focuses on representing and reasoning high-level semantic based on concepts and their space relations. As to multimedia data, such as image and video, acquiring, representing and retrieving high-level semantic information has been a confused problem for a long time. Without the support of knowledge database, it is an impossible mission to carry out the simple synonymous retrieval, let alone retrieving the abstract semantic. This paper proposes some algorithms to translate restored concepts and their relations into a Concept Semantic Network, which is visualized by SVG finally. The paper also introduces the method of recording concepts distribution by description logic, which services users with concepts and distribution retrieval.
12
Yokota, Masao. "Aware Computing in Spatial Language Understanding Guided by Cognitively Inspired Knowledge Representation." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/184103.
Texto completoResumen
Mental image directed semantic theory (MIDST) has proposed an omnisensory mental image model and its description languageLmd. This language is designed to represent and compute human intuitive knowledge of space and can provide multimedia expressions with intermediate semantic descriptions in predicate logic. It is hypothesized that such knowledge and semantic descriptions are controlled by human attention toward the world and therefore subjective to each human individual. This paper describesLmdexpression of human subjective knowledge of space and its application to aware computing in cross-media operation between linguistic and pictorial expressions as spatial language understanding.
13
Penta, Antonio. "A multimedia semantic framework for image understanding and retrieval." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1650001. http://dx.doi.org/10.1142/s2425038416500012.
Texto completoResumen
On the grounds, ontologies have been shown to be a powerful resource for the interpretation and translation of the terminological and semantic relationships within domains of interest but it is still unclear how they can be applied in the context of multimedia data. In this paper, we describe a framework which can capture and manage semantic information related to the multimedia data by modeling in the ontology their features. In particular, the proposed ontology-based framework is organized in the following way: at the lower levels, spatial objects, colors, shapes are represented, and semantic relationships can be established among them; at the higher levels, objects with semantic properties are put into relationship among themselves as well as with the corresponding low-level objects. On this basis, we have designed an ontological system particularly suitable for image retrieval. We have also taken into account the inherent uncertainty related to the representation and detection of multimedia properties in this complex domain. Along this work, we have provided examples from the image domain; moreover, since ontologies provide a semantic means for the semantic comparison of objects and relationships across different formats, the system is easily extensible to other, heterogeneous data sources.
14
Lemos, Daniela Lucas da Silva, and Renato Rocha Souza. "Ontologies for Semantic Annotation: Proposal for an Ontological Multimedia Reference Model." KNOWLEDGE ORGANIZATION 51, no. 8 (2024): 561–81. https://doi.org/10.5771/0943-7444-2024-8-561.
Texto completoResumen
Recent years have seen considerable growth of online multimedia databases, largely due to digitization processes in different sectors of society. Knowledge organization and representation strategies were used to qualify and enrich data and metadata from different types of documents and ensure persistent and interoperable online information structures. This study aimed to propose an ontological reference model to systematically organize metadata that describes multimedia documents based on different contexts and needs. The proposed model was based on the NeOn methodology and aimed to encompass the functional and nonfunctional requirements for the construction and reuse of ontology classes obtained by merging and aligning previously analyzed multimedia ontologies. This resulted in a comprehensive conceptualization to organize multimedia metadata for application contexts that deal with the semantic annotation of information entities produced and consumed in the web of data (Semantic Web). We concluded that advances in developing conceptual reference models for representing multimedia documents are the result of interdisciplinary efforts that drive progress in the production and use of more consistent and coherent metadata aimed at facilitating the cross-referencing, interconnection and aggregation of online information sources.
15
Afef, Zwidi, Ameni Yengui, and Neji Mahmoud. "Research system of semantic information in medical videoconference based on conceptual graphs and domain ontologies." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 7, no. 2 (2013): 979–99. http://dx.doi.org/10.24297/ijmit.v7i2.703.
Texto completoResumen
The multiplication of the number of AudioVisual Documents (AVD) engendered a problem while searching for information within gigantic databases of which we are incapable to index their contents completely manually. Indeed, several complex difficulties are put by these documents because of the vertiginous increase of the quantity of the multimedia data to be treated and the specification met in the representation and the extraction of their contents in particular semantics of the fact that these documents contain three types of media (text, sound, image). AVDs can be classified in professional broadcasted videos (movies, emissions), sporting videos, video controlling, videoconference etc. In this paper, we propose a model of representation of the semantic contents of videoconferences documents in medicine based on the conceptual graphs taking into account the different modalities. This model is based on the concepts extraction and the semantic relations between them and appeals ontology domain.
16
Latif, Afshan, Aqsa Rasheed, Umer Sajid, et al. "Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review." Mathematical Problems in Engineering 2019 (August 26, 2019): 1–21. http://dx.doi.org/10.1155/2019/9658350.
Texto completoResumen
Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.
17
Lemos, Daniela Lucas da Silva, and Renato Rocha Souza. "Knowledge Organization Systems for the Representation of Multimedia Resources on the Web: A Comparative Analysis." KNOWLEDGE ORGANIZATION 47, no. 4 (2020): 300–319. http://dx.doi.org/10.5771/0943-7444-2020-4-300.
Texto completoResumen
The lack of standardization in the production, organization and dissemination of information in documentation centers and institutions alike, as a result from the digitization of collections and their availability on the internet has called for integration efforts. The sheer availability of multimedia content has fostered the development of many distinct and, most of the time, independent metadata standards for its description. This study aims at presenting and comparing the existing standards of metadata, vocabularies and ontologies for multimedia annotation and also tries to offer a synthetic overview of its main strengths and weaknesses, aiding efforts for semantic integration and enhancing the findability of available multimedia resources on the web. We also aim at unveiling the characteristics that could, should and are perhaps not being highlighted in the characterization of multimedia resources.
18
Cai, Liewu, Lei Zhu, Hongyan Zhang, and Xinghui Zhu. "DA-GAN: Dual Attention Generative Adversarial Network for Cross-Modal Retrieval." Future Internet 14, no. 2 (2022): 43. http://dx.doi.org/10.3390/fi14020043.
Texto completoResumen
Cross-modal retrieval aims to search samples of one modality via queries of other modalities, which is a hot issue in the community of multimedia. However, two main challenges, i.e., heterogeneity gap and semantic interaction across different modalities, have not been solved efficaciously. Reducing the heterogeneous gap can improve the cross-modal similarity measurement. Meanwhile, modeling cross-modal semantic interaction can capture the semantic correlations more accurately. To this end, this paper presents a novel end-to-end framework, called Dual Attention Generative Adversarial Network (DA-GAN). This technique is an adversarial semantic representation model with a dual attention mechanism, i.e., intra-modal attention and inter-modal attention. Intra-modal attention is used to focus on the important semantic feature within a modality, while inter-modal attention is to explore the semantic interaction between different modalities and then represent the high-level semantic correlation more precisely. A dual adversarial learning strategy is designed to generate modality-invariant representations, which can reduce the cross-modal heterogeneity efficiently. The experiments on three commonly used benchmarks show the better performance of DA-GAN than these competitors.
19
Yuan, Xu, Hua Zhong, Zhikui Chen, Fangming Zhong, and Yueming Hu. "Multimedia Feature Mapping and Correlation Learning for Cross-Modal Retrieval." International Journal of Grid and High Performance Computing 10, no. 3 (2018): 29–45. http://dx.doi.org/10.4018/ijghpc.2018070103.
Texto completoResumen
This article describes how with the rapid increasing of multimedia content on the Internet, the need for effective cross-modal retrieval has attracted much attention recently. Many related works ignore the latent semantic correlations of modalities in the non-linear space and the extraction of high-level modality features, which only focuses on the semantic mapping of modalities in linear space and the use of low-level artificial features as modality feature representation. To solve these issues, the authors first utilizes convolutional neural networks and topic modal to obtain a high-level semantic feature of various modalities. Sequentially, they propose a supervised learning algorithm based on a kernel with partial least squares that can capture semantic correlations across modalities. Finally, the joint model of different modalities is learnt by the training set. Extensive experiments are conducted on three benchmark datasets that include Wikipedia, Pascal and MIRFlickr. The results show that the proposed approach achieves better retrieval performance over several state-of-the-art approaches.
20
Alti, Adel, Sébastian Laborie, and Philippe Roose. "A Community-Based Semantic Social Context-Aware Driven Adaptation for Multimedia Documents." International Journal of Virtual Communities and Social Networking 7, no. 2 (2015): 31–49. http://dx.doi.org/10.4018/ijvcsn.2015040102.
Texto completoResumen
This paper presents an approach to enhance users experience through the use of recommendations and social networks for on-the-fly (at runtime) adaptation of multimedia documents. This paper presents also CSSAP, a dynamic service selection and assembly tool based on new user profiles and community profiles defined as set of semantic metadata, which context, quality of service and quality of experience parameters. The tool is based on community-aware semantic services and offer architecture, with three layers (semantic query, community management and semantic services). The most innovative characteristic of the tool is that it profits from the potential of semantic representation techniques to express context constraints and community's interests, while they may be useful to generate and manage of complex dynamic adaptation process. This tool improves assembly of relevant adaptation services for communities inferred social influence from a Facebook as virtual P2P environment. The proposed approach has been validated through a prototype for mobiles user of multimedia contents exchanges. The goal is to improve assembly of potential adaptation services and the efficiency and effectiveness of the authors' approach.
21
Kollia, Ilianna, Nikolaos Simou, Andreas Stafylopatis, and Stefanos Kollias. "SEMANTIC IMAGE ANALYSIS USING A SYMBOLIC NEURAL ARCHITECTURE." Image Analysis & Stereology 29, no. 3 (2010): 159. http://dx.doi.org/10.5566/ias.v29.p159-172.
Texto completoResumen
Image segmentation and classification are basic operations in image analysis and multimedia search which have gained great attention over the last few years due to the large increase of digital multimedia content. A recent trend in image analysis aims at incorporating symbolic knowledge representation systems and machine learning techniques. In this paper, we examine interweaving of neural network classifiers and fuzzy description logics for the adaptation of a knowledge base for semantic image analysis. The proposed approach includes a formal knowledge component, which, assisted by a reasoning engine, generates the a-priori knowledge for the image analysis problem. This knowledge is transferred to a kernel based connectionist system, which is then adapted to a specific application field through extraction and use of MPEG-7 image descriptors. Adaptation of the knowledge base can be achieved next. Combined segmentation and classification of images, or video frames, of summer holidays, is the field used to illustrate the good performance of the proposed approach.
22
ORIA, VINCENT, and M. TAMER ÖZSU. "VIEWS OR POINTS OF VIEW ON IMAGES." International Journal of Image and Graphics 03, no. 01 (2003): 55–79. http://dx.doi.org/10.1142/s0219467803000919.
Texto completoResumen
Images like other multimedia data need to be described as it is difficult to grasp their semantics from the raw data. With the emergence of standards like MPEG-7, multimedia data will be increasingly produced together with some semantic descriptors. But a description of a multimedia data is just an interpretation, a point of view on the data and different interpretations can exist for the same multimedia data. In this paper we explore the use of view techniques to define and manage different points of view on images. Views have been widely used in relational database management systems to extend modeling capabilities, and to provide logical data independence. Since our image model is defined on an object-oriented model, we will first propose a powerful object-oriented mechanism based on the distinction between class and type. The object view is used in the image view definition. The image view mechanism exploits the separation of the physical representation in an image of a real world object from the real object itself to allow different interpretations of an image region. Finally we will discuss the implementation of the image view mechanisms on the existing object models.
23
Ha, Hsin-Yu, Fausto C. Fleites, and Shu-Ching Chen. "Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion." International Journal of Multimedia Data Engineering and Management 4, no. 2 (2013): 46–64. http://dx.doi.org/10.4018/jmdem.2013040103.
Texto completoResumen
Nowadays, only processing visual features is not enough for multimedia semantic retrieval due to the complexity of multimedia data, which usually involve a variety of modalities, e.g. graphics, text, speech, video, etc. It becomes crucial to fully utilize the correlation between each feature and the target concept, the feature correlation within modalities, and the feature correlation across modalities. In this paper, the authors propose a Feature Correlation Clustering-based Multi-Modality Fusion Framework (FCC-MMF) for multimedia semantic retrieval. Features from different modalities are combined into one feature set with the same representation via a normalization and discretization process. Within and across modalities, multiple correspondence analysis is utilized to obtain the correlation between feature-value pairs, which are then projected onto the two principal components. K-medoids algorithm, which is a widely used partitioned clustering algorithm, is selected to minimize the Euclidean distance within the resulted clusters and produce high intra-correlated feature-value pair clusters. Majority vote is applied to subsequently decide which cluster each feature belongs to. Once the feature clusters are formed, one classifier is built and trained for each cluster. The correlation and confidence of each classifier are considered while fusing the classification scores, and mean average precision is used to evaluate the final ranked classification scores. Finally, the proposed framework is applied on NUS-wide Lite data set to demonstrate the effectiveness in multimedia semantic retrieval.
24
Wagenpfeil, Stefan. "Multimedia Graph Codes for Fast and Semantic Retrieval-Augmented Generation." Electronics 14, no. 12 (2025): 2472. https://doi.org/10.3390/electronics14122472.
Texto completoResumen
Retrieval-Augmented Generation (RAG) has become a central approach to enhance the factual consistency and domain specificity of large language models (LLMs) by incorporating external context at inference time. However, most existing RAG systems rely on dense vector-based similarity, which fails to capture complex semantic structures, relational dependencies, and multimodal content. In this paper, we introduce Graph Codes—a matrix-based encoding of Multimedia Feature Graphs—as an alternative retrieval paradigm. Graph Codes preserve semantic topology by explicitly encoding entities and their typed relationships from multimodal documents, enabling structure-aware and interpretable retrieval. We evaluate our system in two domains: multimodal scene understanding (200 annotated image-question pairs) and clinical question answering (150 real-world medical queries with 10,000 structured knowledge snippets). Results show that our method outperforms dense retrieval baselines in precision (+9–15%), reduces hallucination rates by over 30%, and yields higher expert-rated answer quality. Theoretically, this work demonstrates that symbolic similarity over typed semantic graphs provides a more faithful alignment mechanism than latent embeddings. Practically, it enables interpretable, modality-agnostic retrieval pipelines deployable in high-stakes domains such as medicine or law. We conclude that Graph Code-based RAG bridges the gap between structured knowledge representation and neural generation, offering a robust and explainable alternative to existing approaches.
25
Jiao, Sai-Mei, Hai-feng Wang, Kun Zhang, and Ya-qi Hu. "Neural Linguistic Steganalysis via Multi-Head Self-Attention." Journal of Electrical and Computer Engineering 2021 (April 17, 2021): 1–5. http://dx.doi.org/10.1155/2021/6668369.
Texto completoResumen
Linguistic steganalysis can indicate the existence of steganographic content in suspicious text carriers. Precise linguistic steganalysis on suspicious carrier is critical for multimedia security. In this paper, we introduced a neural linguistic steganalysis approach based on multi-head self-attention. In the proposed steganalysis approach, words in text are firstly mapped into semantic space with a hidden representation for better modeling the semantic features. Then, we utilize multi-head self-attention to model the interactions between words in carrier. Finally, a softmax layer is utilized to categorize the input text as cover or stego. Extensive experiments validate the effectiveness of our approach.
26
Gvishiani, N. B. "A MULTIMODAL ‘TEXT’: THE LINGUOPRAGMATIC PECULIARITIES OF VERBAL AND NON-VERBAL COMPONENTS INTERACTING IN DIFFERENT COMMUNICATIVE TYPES OF DISCOURSE." Voprosy Kognitivnoy Lingvistiki, no. 1 (2023): 15–17. http://dx.doi.org/10.20916/1812-3228-2022-15-17.
Texto completoResumen
The article dwells on the interaction of verbal and non-verbal components in different communicative media - painting, filmography, and art-reviews discourse. In modern art, we come across various ‘mixed media’ in creating visual or moving images, which may also include the verbal component. The power of linguistic discourse is then applied in the spheres where traditionally other modes were found to prevail. In conceptualism, the word becomes a means of reflection and in neo surrealism - it fills expressive narratives growing into illocutionary speech acts. In the article, text is considered as part of art-multimedia objects and the visual image - as incorporated into a verbal narrative. The dominant role of the verbal component is traced in the conceptual perception of art objects as well as in creating ‘potentially multimodal’ journalistic texts through concrete and abstract linguistic representation. If concrete representation is realized in referential meanings of words, abstract representation hinges on their emotive meanings. It has been observed that whatever the word’s function in art-multimedia may be, it results in broadening the word’s semantic scope and extending its conceptual potential.
27
De Masi, A. "DIGITAL DOCUMENTATION’S ONTOLOGY: CONTEMPORARY DIGITAL REPRESENTATIONS AS EXPRESS AND SHARED MODELS OF REGENERATION AND RESILIENCE IN THE PLATFORM BIM/CONTAMINATED HYBRID REPRESENTATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-M-1-2021 (August 28, 2021): 189–97. http://dx.doi.org/10.5194/isprs-archives-xlvi-m-1-2021-189-2021.
Texto completoResumen
Abstract. The study illustrates a university research project of “Digital Documentation’s Ontology”, to be activated with other universities, of an Platform (P) – Building Information Modeling (BIM) articulated on a Contaminated Hybrid Representation (diversification of graphic models); the latter, able to foresee categories of Multi-Representations that interact with each other for to favour several representations, adapted to a different information density in the digital multi-scale production, is intended as platform (grid of data and information at different scales, semantic structure from web content, data and information storage database, archive, model and form of knowledge and ontological representation shared) of: inclusive digital ecosystem development; digital regenerative synergies of representation with adaptable and resilient content in hybrid or semi-hybrid Cloud environments; phenomenological reading of the changing complexity of environmental reality; hub solution of knowledge and simulcast description of information of Cultural Heritage (CH); multimedia itineraries to enhance participatory and attractive processes for the community; factor of cohesion and sociality, an engine of local development. The methodology of P-BIM/CHR is articulated on the following ontologies: Interpretative and Codification, Morphology, Lexicon, Syntax, Metamorphosis, Metadata in the participatory system, Regeneration, Interaction and Sharing. From the point of view the results and conclusion the study allowed to highlight: a) Digital Regenerative synergies of representation; b) Smart CH Model for an interconnection of systems and services within a complex set of relationships.
28
Bogdanova, Galina, Todor Todorov, Nikolay Noev, and Stefka Kancheva. "Research on Linguistic Approaches, Used for Semantic Explanation of Bell’s Knowledge." Digital Presentation and Preservation of Cultural and Scientific Heritage 2 (September 30, 2012): 155–60. http://dx.doi.org/10.55630/dipp.2012.2.7.
Texto completoResumen
This paper presents a research of linguistic structure of Bulgarian bells knowledge. The idea of building semantic structure of Bulgarian bells appeared during the “Multimedia fund – BellKnow” project. In this project was collected a lots of data about bells, their structure, history, technical data, etc. This is the first attempt for computation linguistic explain of bell knowledge and deliver a semantic representation of that knowledge. Based on this research some linguistic components, aiming to realize different types of analysis of text objects are implemented in term dictionaries. Thus, we lay the foundation of the linguistic analysis services in these digital dictionaries aiding the research of kinds, number and frequency of the lexical units that constitute various bell objects.
29
Zhu, Xinghui, Liewu Cai, Zhuoyang Zou, and Lei Zhu. "Deep Multi-Semantic Fusion-Based Cross-Modal Hashing." Mathematics 10, no. 3 (2022): 430. http://dx.doi.org/10.3390/math10030430.
Texto completoResumen
Due to the low costs of its storage and search, the cross-modal retrieval hashing method has received much research interest in the big data era. Due to the application of deep learning, the cross-modal representation capabilities have risen markedly. However, the existing deep hashing methods cannot consider multi-label semantic learning and cross-modal similarity learning simultaneously. That means potential semantic correlations among multimedia data are not fully excavated from multi-category labels, which also affects the original similarity preserving of cross-modal hash codes. To this end, this paper proposes deep multi-semantic fusion-based cross-modal hashing (DMSFH), which uses two deep neural networks to extract cross-modal features, and uses a multi-label semantic fusion method to improve cross-modal consistent semantic discrimination learning. Moreover, a graph regularization method is combined with inter-modal and intra-modal pairwise loss to preserve the nearest neighbor relationship between data in Hamming subspace. Thus, DMSFH not only retains semantic similarity between multi-modal data, but integrates multi-label information into modal learning as well. Extensive experimental results on two commonly used benchmark datasets show that our DMSFH is competitive with the state-of-the-art methods.
30
Farhan, S., M. A. Fahiem, and H. Tauseef. "On the Design of a Content based Image Retrieval System." Nucleus 56, no. 1 (2019): 36–41. https://doi.org/10.71330/thenucleus.2019.354.
Texto completoResumen
With the abundance of multimedia content on the World Wide Web, research and learning of effectivefeature representation and similarity measures have become crucial. Image searching poses severalchallenges. Lately, many researchers have been exploring the field. Automatic annotation of imagesbased on digital content processing proves to be an encouraging direction in the field. Content basedimage retrieval system development is an emerging field. Accuracy of the results of semantic searchdepends on the understanding of searcher’s purpose, the meaning of conditions imposed in the searchquery and their mapping in the searchable data space. A visual content semantic search engine isproposed in this paper. The search engine employs digital image features for searching the imagedatabase. The presented algorithm produces promising results. The performance of our algorithm istested on an extensive set of tags and queries resulting in accurate and efficient results.
31
Zhang, Ruiping. "A Personalized Course Resource Recommendation Method Based on Deep Learning in an Online Multi-Modal Multimedia Education Cloud Platform." International Journal of Information Technologies and Systems Approach 16, no. 2 (2023): 1–14. http://dx.doi.org/10.4018/ijitsa.319344.
Texto completoResumen
Aiming at the problem that unstructured text in online multi-modal multimedia education is easy to cause error propagation, this paper proposes a personalized course resource recommendation method using deep learning in online multi-modal multimedia education cloud platform. First, the word vector of the text is obtained from the course data set by using the BERT pre-training model, and its semantic information in different contexts is analyzed. Then, the more complex representation of each word is extracted through the long short-term memory network (LSTM), in which the multi-head attention layer adds different weights to different word vector to better capture the key information in the sentence. Finally, the CRF layer is used to identify sentence entities, and the Sigmoid layer is used to extract relations, thus completing personalized course resource recommendation, which is significantly improved compared with other models. Experimental analysis shows that the algorithm is effective in personalized course resource recommendation.
32
Sancho, Pilar, Iván Martínez, and Baltasar Fernández-Manjón. "Semantic Web Technologies Applied to e-learning Personalization in
Resumen
Despite the increasing importance gained by e-learning standards in the past few years, and the unquestionable goals reached (mainly regarding interoperability among e-learning contents) current e-learning standards are yet not sufficiently aware of the context of the learner. This means that only a limited support for adaptation regarding individual characteristics is currently being provided. In this article, we propose the use of semantic metadata for Learning Object (LO) contextualization in order to adapt instruction to the learner's cognitive requirements in three different ways: background knowledge, knowledge objectives and the most suitable learning style. In our pilot e-learning platform () the context for LOs is addressed in two different ways: knowledge domain and instructional design. We propose the use of ontologies as the knowledge representation mechanism to allow the delivery of learning material that is relevant to the current situation of the learner.
33
Wu, Xiao-Ming, Xin Luo, Yu-Wei Zhan, Chen-Lu Ding, Zhen-Duo Chen, and Xin-Shun Xu. "Online Enhanced Semantic Hashing: Towards Effective and Efficient Retrieval for Streaming Multi-Modal Data." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4263–71. http://dx.doi.org/10.1609/aaai.v36i4.20346.
Texto completoResumen
With the vigorous development of multimedia equipments and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic. Thereinto, hashing has become a prevalent choice due to its retrieval efficiency and low storage cost. Although multi-modal hashing has drawn lots of attention in recent years, there still remain some problems. The first point is that existing methods are mainly designed in batch mode and not able to efficiently handle streaming multi-modal data. The second point is that all existing online multi-modal hashing methods fail to effectively handle unseen new classes which come continuously with streaming data chunks. In this paper, we propose a new model, termed Online enhAnced SemantIc haShing (OASIS). We design novel semantic-enhanced representation for data, which could help handle the new coming classes, and thereby construct the enhanced semantic objective function. An efficient and effective discrete online optimization algorithm is further proposed for OASIS. Extensive experiments show that our method can exceed the state-of-the-art models. For good reproducibility and benefiting the community, our code and data are already publicly available.
34
Sujatha, Dr K., Koulik Ghsoh, and Aneesh Anand. "Domain Adaptation and Semantic Drawing Driven Sketch-to-Photo Retrieval using Collaborative Generative Representation Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1473–80. http://dx.doi.org/10.22214/ijraset.2024.61734.
Texto completoResumen
Abstract: Sketch-based face recognition is an interesting task in vision and multimedia research yet it is quite challenging due to the great difference between face photos and sketches. In this paper we propose a novel approach for photo-sketch generation aiming to automatically transform face photos into detail-preserving personal sketches. Unlike the traditional models synthesizing sketches based on a dictionary of exemplars we develop a fully convolutional network to learn the end-to-end photosketch mapping. Our approach takes whole face photos as inputs and directly generates the corresponding sketch images with efficient inference and learning in which the architecture is stacked by only convolutional kernels of very small sizes. The exemplar-based method is most frequently used in face sketch synthesis because of its efficiency in representing the nonlinear mapping between face photos and sketches. However, the sketches synthesized by existing exemplar-based methods suffer from block artifacts and blur effects. In addition, most exemplar-based methods ignore the training sketches in the weight representation process. To improve synthesis performance, a novel joint training model is proposed in this paper, taking sketches into consideration. First, we construct the joint training photo and sketch by concatenating the original photo and its sketch with a high-pass filtered image of their corresponding sketch. Then, an offline random sampling strategy is adopted for each test photo patch to select the joint training photo and sketch patches in the neighboring region. Finally, a novel locality constraint is designed to calculate the reconstruction weight, allowing the synthesized sketches to have more detailed information
35
Stella, Massimo, Michael S. Vitevitch, and Federico Botta. "Cognitive Networks Extract Insights on COVID-19 Vaccines from English and Italian Popular Tweets: Anticipation, Logistics, Conspiracy and Loss of Trust." Big Data and Cognitive Computing 6, no. 2 (2022): 52. http://dx.doi.org/10.3390/bdcc6020052.
Texto completoResumen
Monitoring social discourse about COVID-19 vaccines is key to understanding how large populations perceive vaccination campaigns. This work reconstructs how popular and trending posts framed semantically and emotionally COVID-19 vaccines on Twitter. We achieve this by merging natural language processing, cognitive network science and AI-based image analysis. We focus on 4765 unique popular tweets in English or Italian about COVID-19 vaccines between December 2020 and March 2021. One popular English tweet contained in our data set was liked around 495,000 times, highlighting how popular tweets could cognitively affect large parts of the population. We investigate both text and multimedia content in tweets and build a cognitive network of syntactic/semantic associations in messages, including emotional cues and pictures. This network representation indicates how online users linked ideas in social discourse and framed vaccines along specific semantic/emotional content. The English semantic frame of “vaccine” was highly polarised between trust/anticipation (towards the vaccine as a scientific asset saving lives) and anger/sadness (mentioning critical issues with dose administering). Semantic associations with “vaccine,” “hoax” and conspiratorial jargon indicated the persistence of conspiracy theories and vaccines in extremely popular English posts. Interestingly, these were absent in Italian messages. Popular tweets with images of people wearing face masks used language that lacked the trust and joy found in tweets showing people with no masks. This difference indicates a negative effect attributed to face-covering in social discourse. Behavioural analysis revealed a tendency for users to share content eliciting joy, sadness and disgust and to like sad messages less. Both patterns indicate an interplay between emotions and content diffusion beyond sentiment. After its suspension in mid-March 2021, “AstraZeneca” was associated with trustful language driven by experts. After the deaths of a small number of vaccinated people in mid-March, popular Italian tweets framed “vaccine” by crucially replacing earlier levels of trust with deep sadness. Our results stress how cognitive networks and innovative multimedia processing open new ways for reconstructing online perceptions about vaccines and trust.
36
Zhai, Xiaohua, Yuxin Peng, and Jianguo Xiao. "Heterogeneous Metric Learning with Joint Graph Regularization for Cross-Media Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 1198–204. http://dx.doi.org/10.1609/aaai.v27i1.8464.
Texto completoResumen
As the major component of big data, unstructured heterogeneous multimedia content such as text, image, audio, video and 3D increasing rapidly on the Internet. User demand a new type of cross-media retrieval where user can search results across various media by submitting query of any media. Since the query and the retrieved results can be of different media, how to learn a heterogeneous metric is the key challenge. Most existing metric learning algorithms only focus on a single media where all of the media objects share the same data representation. In this paper, we propose a joint graph regularized heterogeneous metric learning (JGRHML) algorithm, which integrates the structure of different media into a joint graph regularization. In JGRHML, different media are complementary to each other and optimizing them simultaneously can make the solution smoother for both media and further improve the accuracy of the final metric. Based on the heterogeneous metric, we further learn a high-level semantic metric through label propagation. JGRHML is effective to explore the semantic relationship hidden across different modalities. The experimental results on two datasets with up to five media types show the effectiveness of our proposed approach.
37
Zabulis, Xenophon, Nikolaos Partarakis, Valentina Bartalesi, et al. "Multimodal Dictionaries for Traditional Craft Education." Multimodal Technologies and Interaction 8, no. 7 (2024): 63. http://dx.doi.org/10.3390/mti8070063.
Texto completoResumen
We address the problem of systematizing the authoring of digital dictionaries for craft education from ethnographic studies and recordings. First, we present guidelines for the collection of ethnographic data using digital audio and video and identify terms that are central in the description of crafting actions, products, tools, and materials. Second, we present a classification scheme for craft terms and a way to semantically annotate them, using a multilingual and hierarchical thesaurus, which provides term definitions and a semantic hierarchy of these terms. Third, we link ethnographic resources and open-access data to the identified terms using an online platform for the representation of traditional crafts, associating their definition with illustrations, examples of use, and 3D models. We validate the efficacy of the approach by creating multimedia vocabularies for an online eLearning platform for introductory courses to nine traditional crafts.
38
WANG, ZHIYONG, ZHERU CHI, DAGAN FENG, and AH CHUNG TSOI. "CONTENT-BASED IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK USING ADAPTIVE PROCESSING OF TREE-STRUCTURE IMAGE REPRESENTATION." International Journal of Image and Graphics 03, no. 01 (2003): 119–43. http://dx.doi.org/10.1142/s0219467803000944.
Texto completoResumen
Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.
39
Yu, Jing, Zhao Lu, Shoulin Yin, and Mirjana Ivanovic. "News recommendation model based on encoder graph neural network and bat optimization in online social multimedia art education." Computer Science and Information Systems, no. 00 (2024): 25. http://dx.doi.org/10.2298/csis231225025y.
Texto completoResumen
At present, the existing news recommendation system fails to fully consider the semantic information of news, meanwhile, the uneven popularity of news will also cause the phenomenon of long tail. Therefore, we propose a novel news recommendation model based on encoder graph neural network and Bat optimization in online social networks. Firstly, Bat optimization algorithm is used to improve the effect of news clustering. Secondly, the concept of metadata is introduced into the graph neural network, and the ontology of learning resources based on knowledge points is established to realize the correlation between news resources. Finally, the model combining Convolutional Neural Network (CNN) and attention network is used to learn the representation of news, and Gate Recurrent Unit (GRU) is used to learn the short-term preferences of users from their recent reading history. We carry out experiments on real news datasets, and compared with other advanced methods, the proposed model has better evaluation indexes.
40
MIRENKOV, NIKOLAY, ALEXANDER VAZHENIN, RENTARO YOSHIOKA, TSUKASA EBIHARA, TETSUYA HIROTOMI, and TATIANA MIRENKOVA. "SELF-EXPLANATORY COMPONENTS: A NEW PROGRAMMING PARADIGM." International Journal of Software Engineering and Knowledge Engineering 11, no. 01 (2001): 5–36. http://dx.doi.org/10.1142/s0218194001000414.
Texto completoResumen
A new multimedia programming paradigm is presented. It is based on a system of micro- and macro-icons (composite pictures) representing self-explanatory software components in a "film" format. A film is a series of color stills supported, if necessary, by text and sound. Each still is to represent a view of objects or processes. Each film is to represent a multiple view (an extended set of dynamic and/or static features) of objects or processes. A self-explanatory film means that the associated stills are organized and presented in such a way that the semantic richness of a computational scheme is clearly brought out. Icons and films are acquired in a net-accessible database. The user should not study them in advance. The film management system provides simple access to database items and modes to manipulate films. In this paper we explain where the database items are taken from and how the self-explanatory features of items are reached. We also describe how these items can be used for multimedia representation of methods and data and for programming users' algorithmic ideas. In addition, some technical details related to the film management system, rendering engines used for displaying various features of the software components, and the icon language are presented. Special attention is paid to how computational formulas can be attached to a film.
41
Lai, Jingjuan, Hanxiong Chen, and Yuzuru Fujiwara. "An information-base system based on the self-organization of concepts represented by terms." Terminology 3, no. 2 (1996): 313–34. http://dx.doi.org/10.1075/term.3.2.05lai.
Texto completoResumen
Since multimedia information is complicated inform and vast in amount, conventional database-management systems or knowledge-base-management systems are hardly appropriate to store, manage, and utilize expertise effectively. A new type of information model is developed according to an analysis of the information used by specialists for research and development, and a prototype information-management system is implemented. The system consists of three parts: (1) flexible storage without special constraints on format and representation; (2) self-organization of terms by extracting semantic relationships among them; and (3) advanced utilization functions such as analogical reasoning, inductive inference, abductive inference, as well as information retrieval, numerical calculation, and deductive inference. Thesauri which are automatically compiled and refined are used as conceptual structures of the information. Thus obtained, conceptual structures can be used for sophisticated applications, including analogical reasoning, induction, and abduction. The principle of open-world reasoning and an algorithm of analogy are developed. An example of practical application to polymer information is presented.
42
Rogushina, J. V. "Use of Ontology-based knowledge Organization Sysytems for WIKI Resources." PROBLEMS IN PROGRAMMING, no. 1 (March 2022): 023–33. http://dx.doi.org/10.15407/pp2022.01.023.
Texto completoResumen
The paper considers the theoretical foundations of knowledge organization systems (KOSs) in intelligent ontology-based applications. The aim of this study is to analyze the use of different types of KOSs to organize and improve the knowledge base of semantic Wiki resources that contain heterogeneous multimedia content of large volume and have a complex structure integrated knowledge from different domains. The dialects of the OWL ontology representation language and their expressiveness for representing special cases of ontologies used in KOSs are considered. The criteria for the classification of KOSs and sphere of their usage are analyzed. Formal model of ontology for semantic Wiki resource is proposed. This model is integrated with various implementing means for different types of relations between objects in the Semantic MediaWiki environment based on templates. Problems of access and retrieval of information in these resources and methods of their solving from the KOSs point of view are considered. The software implementation of the proposed approach with the example of the portal version of the Great Ukrainian Encyclopedia (e-VUE) is realized. The urgency of the problem intensifies by the need for national information resources in martial law situation, for which the determining factors of effective information processing are both the ability to obtain satisfaction of complex information needs and the relevance of the information obtained. This increases the importance of official government portals that integrate reliable data from various fields of knowledge and prevent possible misrepresentation (both accidental and malicious) of information in resources with open content generation.
43
Buchmann, Oliver, Maik Siegmund, Robert Kaden, and Frank Iden. "CITYTWIN – AI-based Decision Support System for Semantic Search and Analysis of Location-based Information for Urban and Site Planning." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W5-2024 (June 27, 2024): 63–69. http://dx.doi.org/10.5194/isprs-annals-x-4-w5-2024-63-2024.
Texto completoResumen
Abstract. The development of a knowledge-based decision support system for the evaluation and planning of location and urban development concepts was implemented. In order to achieve this goal, cross-domain ontologies were developed for interdisciplinary databases, which are then mapped in semantic networks. The exponential growth in computing power in the hardware sector alone can no longer solve this problem, but at the same time enables the application of new methods for storing and evaluating data. Essentially, it is no longer just about the digital recording of object properties in conventional databases, but also about the digital representation of their significance for specific questions and the linking of meanings across the boundaries of specialist domains. This information is stored in a multimedia knowledge base, together with the methods and rules for its use and the evaluations and decisions based on it. The motivation for this project is the rapidly growing amount of data, which extends across ever new specialist domains and can no longer be sufficiently integrated into the decision-making of experts using conventional methods of knowledge acquisition. After determining this data, it was linked to a georeferencing. Within the framework of the project, documents were analyzed with the help of AI and examined for semantic text corpora. This data was georeferenced. Various algorithms were used to accomplish this task, including TF-IDF, TextRank and Word2Vec.
44
Zhao, Yanhaotian. "State of the Art in the Application of Multimodal Affective Methods for Comparative Analysis of Modal Deficits." Applied and Computational Engineering 174, no. 1 (2025): 1–9. https://doi.org/10.54254/2755-2721/2025.po24716.
Texto completoResumen
The advent of multimedia technology has precipitated a paradigm shift in the realm of human-computer interaction and affective computing, thus rendering multimodal emotion recognition a pivotal domain. However, the issue of modal absence, resulting from equipment failure or environmental interference in practical applications, significantly impacts the accuracy of emotion recognition. The objective of this paper is to analyse multimodal emotion recognition methods oriented to modal absence. The focus is on comparing and analysing the advantages and disadvantages of techniques such as generative class and joint representation class. Experimental findings demonstrate the efficacy of these methods in surpassing the conventional baseline on diverse datasets, including IEMOCAP, CMU-MOSI, and others. Notably, CIF-MMIN enhances the mean accuracy by 0.92% in missing conditions while concurrently reducing the UniMF parameter by 30%, thus preserving the SOTA performance. Key challenges currently being faced by researchers in the field of multimodal emotion recognition for modal absence include cross-modal dependencies and semantic consistency, model generalisation ability, and dynamic scene adaptation. These challenges may be addressed in the future through the development of a lightweight solution that does not require full-modal pre-training, and by combining comparative learning with generative modelling to enhance semantic fidelity. The present paper provides both theoretical support and practical guidance for the development of a highly robust and efficient emotion recognition system.
45
Rogushina, J. V., and I. J. Grishanova. "Ontological methods and tools for semantic extension of the media WIKI technology." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2020): 061–73. http://dx.doi.org/10.15407/pp2020.02-03.061.
Texto completoResumen
Practical aspects of ontological approach to organization of intelligent Wiki-based information resources (IR) are considered. We analyze the main features, capabilities and limitations of MediaWiki as a technological platform for development of the Web-based information resource and suggest main directions of its refinement. We propose an abstract model of MediaWiki architecture that formalizes relations between the main components of this software environment and analyze the ways of its semantic extensions based on ontological representation of domain knowledge. An original algorithm of semantic Wiki pages matching with domain ontology is developed. We propose an ontological model of IR that formalizes its knowledge base structure and explicitly performs main features of typical information objects (TIO) of this IR. Such TIOs depend on domain specifics and purposes of IR, therefore their development has to involve domain experts and knowledge engineers. Use of ontology corresponding to the set of Wiki pages (either with semantic markup or without it) provides new IR functions associated with semantic search and navigation. Other important aspect of intelligent Wiki resource development deals with adaptation of user interface to the specifics of IR: enabling various tools of navigation, visualization and content analysis by processing of TIO features enriches IR functionality, reduces access time to information and makes usage of IR more efficient. Developing additional MediaWiki functionality with new requests to the MediaWiki API using TIO templates, extends data analysis and integration capabilities, and offers different, user-focused, IR content views expands the possibilities of data integration and proposes various user-oriented representations of IR content. Wiki resource semantization allows the use knowledge acquired from such IR by external application, or example, by search engines for intelligent Web retrieval. Domain ontologies based on various subsets of the Wiki pages and generated by them thesauri can be used by various Semantic Web applications, both independently or in general technological chain for personified retrieval focused on individual users and their tasks. Approbation of this approach is demonstrated by MAIPS retrieval system. We consider the use semantic similarity of concepts represented by Wiki-pages of IR as an additional way of intelligent navigation between these pages. Such approach allows to group Wiki pages according to user interests by different aspects of their content and structure. Wiki ontologies are considered as the basis for estimation of semantic similarity between domain concepts pertinent to user task. Such elements of Wiki ontology as classes, property values of class instances and relations between them are used as parameters for the quantitative assessment of semantic similarity of Wiki pages. We propose to use local similarity and generate the sets of semantically similar concepts (SSC) that takes into account some subset of page properties and categories defined by user needs. Such sets of SSCs can be considered as user task thesauri for other applications. In addition, we propose to enrich the basic tools of MediaWiki used for access management to the IR content with specialized software code that performs content classification that take into consideration separate namespaces, categories, templates and semantic properties of TIO acquired from Wiki markup. We demonstrate the software implementation of proposed solutions by developing of portal version of the Great Ukrainian Encyclopedia (e-VUE) that contains heterogeneous multimedia content with complex structure. We analyze the specifics of e-VUE knowledge system and develop its formalized TIO representation based on Semantic Web technologies and ontological analysis. Ontological model of e-VUE and original methods of its processing used for this project extend the functionality of the portal in the area of search, navigation, integration and protection of content based on background domain knowledge. In addition, original user interface of e-VUE is developed with an allowance for Encyclopedia knowledge specifics, substantially differs from the standard Wiki, meets the requirements, goals and objectives of this IR and provides a lot of additional features.
46
Ehab, Engy, Nahla Belal, and Yasser Omar. "Tri-FND: Multimodal Fake News Detection Using Triplet Transformer Models." Journal of Advanced Research in Applied Sciences and Engineering Technology 63, no. 1 (2025): 255–70. https://doi.org/10.37934/araset.63.1.255270.
Texto completoResumen
The prevalence of fake news accompanied by multimedia content on the internet presents a significant challenge for users attempting to discern its authenticity. Automatically identifying and classifying fake news is a crucial way for combating misinformation and maintain the integrity of information dissemination. This paper proposes a fake news detection approach that exploits multimodality's potential and integrates textual and visual data to improve the fake news classification system. The novel multimodal learning approach to fake news detection, which has been termed Tri-FND, uses triplet transformers for fake news detection. This approach utilizes state-of-the-art language and vision transformers with Contrastive Language-Image Pretraining (CLIP) to improve feature representation and textual and visual semantic alignment. This technique significantly enhances the capability of identifying fake news by analyzing both text and images. Experiments were conducted on two linguistic datasets: the English dataset is sourced from Twitter, while the Chinese dataset is sourced from Weibo. The proposed approach can achieve an overall accuracy of 0.90 on the Twitter dataset and 0.93 on the Weibo dataset.
47
Strashko, Iryna V. "PHONETIC, LEXICAL, GRAMMATICAL, COGNITIVE, AND PRAGMATIC LEVELS OF THE LINGUISTIC PERSONALITY (BASED ON THE INTERVIEW FROM THE AUTHOR’S MULTIMEDIA CORPUS)." Scientific Journal of National Pedagogical Dragomanov University. Series 9. Current Trends in Language Development, no. 25 (June 30, 2023): 79–89. http://dx.doi.org/10.31392/npu-nc.series9.2023.25.06.
Texto completoResumen
The paper focuses on the analysis of the means of representation of the informant’s linguistic personality at phonetic, lexical, grammatical, cognitive, and pragmatic levels in the oral discourse. The material of the study is a transcript of an audio recording of one interview from the author’s multimedia corpus “Everyone has their own war”. The interview was recorded in the Ukrainian language in one of the most emotionally, psychologically, and physically difficult moments of the informant’s life. Despite a certain limitation of language material, the peculiarities of the speech manifestations of the linguistic personality of the informant, a twenty-nine age widow (a woman and a mother), are representative since she describes her life and the life of her family after the full-scale invasion on February 24 and until May 2022.
 The analysis of the informant’s linguistic personality shows that the verbal and semantic specificity is determined by the volume of lexical items, the peculiarities of nominating speech objects and the choice of means for their characteristic, as well as the style of speech. The informant’s speech is characterized by violations of literary norms: it is full of adapted and unadapted lexical and morphological units of the Russian language, and improper pronunciation of words, which in general correlates with her cultural and educational level. The informant’s vocabulary is pragmatically functional and determined by the level of education, social status, type of employment and living conditions. It clearly reflects the essence and content of 
 the linguistic personality. The vocabulary of the everyday sphere prevails, onyms (toponyms, anthroponyms, ergonyms) and a small amount of military lexicon are also registered.
 Emotional and evaluative interjections with a positive or negative assessment are representatives of the emotional, functional, and semantic sphere of the informant’s speech. The connotative coloration is provided, in particular, by the verbal characterization of the occupiers, which includes ethnonymic nicknames, including those based on appearance, language, and behaviour.
 In terms of content and values, the discursive activity of the informant, represented by referential semantic elements, is determined by extralinguistic factors and it correlates with universal values.
 The motivational and pragmatic aspect of linguistic personality is grounded on the desire to speak out, and includes life or situational goals, which are reflected in the discourse. It is manifested, in particular, in the manner of speech, in the choice of markers used to organize and control the discursive coherence. The analysis of the pragmatic markers included their functions, the specifics of their use and frequency.
48
Vishniakou, U. A., and A. P. Kovalev. "ONLINE-SERVICES AND INFORMATION TECHNOLOGIES IN DISTANCE LEARNING." «System analysis and applied information science», no. 4 (February 8, 2018): 66–71. http://dx.doi.org/10.21122/2309-4923-2017-4-66-71.
Texto completoResumen
The article deals with the analysis of distance learning (DL) methods, approaches, technologies, tools, the use as known online services so and developing the new ones. The terminology in area of DL is discussed and differences between correspondence course and DL are done. The development tendencies of distance learning are analyzed. Their technical and organization components are done. The course programs for DL are realizing by software which functions are shown. The typical lines of DL, their advances and lacks are conceded. As DL advances are self activity, individuality, independence and so on. As DL lacks are insufficiently individual, psychological, practical aspects, writing forms of DL and so on.Technologies and organization of DL including IT are discussed. The tutor activity is divided on two stages: decision of methodological, organizational problems and realization of distance courses. The various kind of online services in DL such as chats, web, TV, video conferences multimedia, robot learning, web-services are shown. Such IT for DL as CD, net, TV, satellite, cloud are discussed.The models of integration decisions for DL development such as Remote Procedure Calls (RPS), Enterprise Application Integration (EAL), Web-Services (WS), Enterprise Service Bus (ESB) are proposed. The content of e-learning online services including intellectual technologies and cloud computing are done. As new one integration method for DL is Semantic Web and Web-service (SWWS) with knowledge representation support on ontology base and knowledge processing on agents support are representation.
49
Scianna, A., and M. La Guardia. "GLOBE BASED 3D GIS SOLUTIONS FOR VIRTUAL HERITAGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W10 (September 12, 2018): 171–77. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w10-171-2018.
Texto completoResumen
<p><strong>Abstract.</strong> During the last years, many solutions have been proposed for 3D Virtual Heritage representations. Recently, also new technologies for online gaming evolved, based on javascript libraries (WebGL), used to create and publish virtual interactive environments. They are based on recent Web browser’s functionalities, surpassing some limitations of VRML technologies. On the side of geospatial information, technology has evolved from desktop GIS to 2D WebGIS and globe applications. The use of globe applications is, today, very diffused due to its immediate and at the same time impressive representation of the earth surface and territories. These technologies have been, also, applied to Virtual Heritage 3D reconstructions, to improve the fruition of Cultural Heritage (CH), with the achievement of interesting results. The topic of this paper is the experimentation on the fusion between globe based and gaming technologies (in our case WebGL) that allow achieving a more user-centric and powerful solution useful for publishing 3D geospatial information of CH on Web. This choice allows obtaining GIS oriented 3D models, typical of globe applications, and, at the same time, a more immersive exploration of CH and its surrounding environment. In particular, it also gives complementary text and multimedia information on the history, architectural features of each cultural good, based on querying of semantic information. The test field of the research is the construction of the 3D GIS virtual globe model of the Manfredonic Castle of Mussomeli (Sicily-Italy), developed for PON-NEPTIS EU Project, to compare open-source technologies and commercial proprietary applications.</p>
50
Petrenko, M. G., O. V. Palagin, M. O. Boyko, and S. M. Matveyshyn. "Knowledge-Oriented Tool Complex for Developing Databases of Scientific Publications and Taking into account Semantic Web Technology." Control Systems and Computers, no. 3 (299) (2022): 11–28. http://dx.doi.org/10.15407/csc.2022.03.011.
Texto completoResumen
Introduction. The development of theories, methods, and algorithms for the discovery and formation of new knowledge has always occupied one of the central places for any researcher, especially if he is actively working on the creation of new scientific publications. It is known that there is no universal language for the formal description of concepts (knowledge) and systemology of transdisciplinary scientific research. And therefore, scientists face a number of priority problems, including the problem of significantly accelerating the receipt by a researcher of the cognitively structured information he needs from his sources. The tool complex for processing databases of scientific publications is oriented in this way to a researcher who has published from several tens to hundreds of scientific papers. We are not aware of search engines that could provide such information to a researcher in the shortest possible time. The toolkit implements Information Retrieval and Knowledge Discovery in Databases technologies with an emphasis on Semantic Web and cognitive graphics technologies and tools. The development of such a tool complex involves three stages: at the first stage, tools for implementing the complex, methods and algorithms for the interaction of the “User – Knowledge Engineer – Remote Endpoint” system and filling it with data are created; the second stage, the tasks of multimedia representation of figurative-conceptual structures are solved, which are described in scientific documents, and at the third stage — the solution of the problem of extracting new knowledge. Purpose. The purpose of our research was to further develop a tool complex for processing databases of scientific publications, which allows a scientist to significantly speed up the receipt of the necessary cognitively structured information from his sources. Methods. The methods and models used in the work are based on the information technologies of the Semantic Web and ontological engineering. Results. A tool complex for processing databases of scientific publications based on a remote endpoint based on the Apachi Jena Fuseki server, basic UML diagrams of functioning and examples of executing user requests have been developed. Conclusion. The article introduced and described the architectural and structural organization of the tool complex, which includes a local network from the user’s PC and the PC of the administrator-knowledge engineer and a remote endpoint based on the Apachi Jena Fuseki server, the main UML diagrams of the tool complex functioning and examples of executing user requests.