Journal articles: 'Semantic knowledge representation'

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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 (September 4, 2007): 293–327. http://dx.doi.org/10.1007/s11042-007-0161-4.

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Liu, Haoyan, Lei Fang, Jian-Guang Lou, and Zhoujun Li. "Leveraging Web Semantic Knowledge in Word Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6746–53. http://dx.doi.org/10.1609/aaai.v33i01.33016746.

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Much recent work focuses on leveraging semantic lexicons like WordNet to enhance word representation learning (WRL) and achieves promising performance on many NLP tasks. However, most existing methods might have limitations because they require high-quality, manually created, semantic lexicons or linguistic structures. In this paper, we propose to leverage semantic knowledge automatically mined from web structured data to enhance WRL. We first construct a semantic similarity graph, which is referred as semantic knowledge, based on a large collection of semantic lists extracted from the web using several pre-defined HTML tag patterns. Then we introduce an efficient joint word representation learning model to capture semantics from both semantic knowledge and text corpora. Compared with recent work on improving WRL with semantic resources, our approach is more general, and can be easily scaled with no additional effort. Extensive experimental results show that our approach outperforms the state-of-the-art methods on word similarity, word sense disambiguation, text classification and textual similarity tasks.

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Usery, E. Lynn. "A Semantic Representation of Map Projections Knowledge." Abstracts of the ICA 1 (July 19, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-376-2019.

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<p><strong>Abstract.</strong> A body of knowledge for cartography requires representing knowledge of the specific sub topics in the field. Map projections is a fundamental part of the knowledge base for cartography and a wealth of material exists on knowledge of map projections. Semantic organization of such knowledge is of primary importance to the access and use of map projections knowledge. This project builds a semantic representation for the fundamental parts of map projection knowledge. The semantics capture the concepts and relations between these concepts providing the user an easy method to access the knowledge and apply it to specific problems. The semantics represent classes of projections and the properties associated with those classes as well as the appropriate use. Such a representation can be accessed by humans or machines to arrive at appropriate selection and use of map projection theory.</p>

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Hou, Wenfeng, Qing Liu, and Longbing Cao. "Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge." Applied Sciences 10, no. 14 (July 16, 2020): 4893. http://dx.doi.org/10.3390/app10144893.

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Short text is widely seen in applications including Internet of Things (IoT). The appropriate representation and classification of short text could be severely disrupted by the sparsity and shortness of short text. One important solution is to enrich short text representation by involving cognitive aspects of text, including semantic concept, knowledge, and category. In this paper, we propose a named Entity-based Concept Knowledge-Aware (ECKA) representation model which incorporates semantic information into short text representation. ECKA is a multi-level short text semantic representation model, which extracts the semantic features from the word, entity, concept and knowledge levels by CNN, respectively. Since word, entity, concept and knowledge entity in the same short text have different cognitive informativeness for short text classification, attention networks are formed to capture these category-related attentive representations from the multi-level textual features, respectively. The final multi-level semantic representations are formed by concatenating all of these individual-level representations, which are used for text classification. Experiments on three tasks demonstrate our method significantly outperforms the state-of-the-art methods.

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Abburu, Sunitha, and G. Suresh Babu. "Indian Music Instruments Semantic Knowledge Representation." International Journal of Computer Applications 71, no. 15 (June 26, 2013): 1–5. http://dx.doi.org/10.5120/12431-8540.

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Mallat, Souheyl, Emna Hkiri, Mohsen Maraoui, and Mounir Zrigui. "Semantic Network Formalism for Knowledge Representation." International Journal on Semantic Web and Information Systems 11, no. 4 (October 2015): 64–85. http://dx.doi.org/10.4018/ijswis.2015100103.

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In this paper, the authors propose formalism for representing a knowledge base (KB) by network. The objective is to achieve a high coverage of this base. This type of network is similar to the semantic network with the difference that the arcs are quantified by a value indicating the semantic proximity between the concepts. This semantic proximity presents taxonomic relations, synonyms, and non-taxonomic relations (contextual relations). This latter are discovered based on the association rules model. This model is based on (i) indexing method (ii) the French lexical database EuroWordNet (EWNF) and (iii) the Apriori algorithm. The contextual relations are the latent relations buried in the KB, carried by the semantic context. Evaluating our representation formalism shows better result about 80% of coverage of the KB.

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Jian, Liu, Huang Haisong, and Pan Weijie. "Knowledge Model Based on Graphical Semantic Perception." Cybernetics and Information Technologies 15, no. 6 (December 1, 2015): 16–28. http://dx.doi.org/10.1515/cait-2015-0064.

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Abstract Based on knowledge representation in product design, we propose a knowledge model of the product design process based on graphical semantic perception. The ontology semantic of the product is analyzed together with the product design process knowledge. Then the basic element model for knowledge representation of the product design process is built. With the concepts of the extension origin point basic element and extension vector thus defined, knowledge representation, consisting of growth, convergence and optimization is realized. On this basis, the model for case base clustering based on graphical semantics is built. Feasibility verification is performed with the case of appearance design of a machine tool. The highlight of the proposed method lies in the combination of the formalized and quantitative approach for product appearance design.

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LOUWERSE, MAX, ZHIQIANG CAI, XIANGEN HU, MATTHEW VENTURA, and PATRICK JEUNIAUX. "COGNITIVELY INSPIRED NLP-BASED KNOWLEDGE REPRESENTATIONS: FURTHER EXPLORATIONS OF LATENT SEMANTIC ANALYSIS." International Journal on Artificial Intelligence Tools 15, no. 06 (December 2006): 1021–39. http://dx.doi.org/10.1142/s0218213006003090.

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Natural-language based knowledge representations borrow their expressiveness from the semantics of language. One such knowledge representation technique is Latent semantic analysis (LSA), a statistical, corpus-based method for representing knowledge. It has been successfully used in a variety of applications including intelligent tutoring systems, essay grading and coherence metrics. The advantage of LSA is that it is efficient in representing world knowledge without the need for manual coding of relations and that it has in fact been considered to simulate aspects of human knowledge representation. An overview of LSA applications will be given, followed by some further explorations of the use of LSA. These explorations focus on the idea that the power of LSA can be amplified by considering semantic fields of text units instead of pairs of text units. Examples are given for semantic networks, category membership, typicality, spatiality and temporality, showing new evidence for LSA as a mechanism for knowledge representation. The results of such tests show that while the mechanism behind LSA is unique, it is flexible enough to replicate results in different corpora and languages.

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Sapkota, Krishna. "Towards Semantic Knowledge Mapping: An Extension of Compendium with Semantic Knowledge Representation." International Journal of Artificial Intelligence & Applications 3, no. 5 (September 30, 2012): 1–12. http://dx.doi.org/10.5121/ijaia.2012.3501.

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WANG, PATRICK SHEN-PEI. "KNOWLEDGE PATTERN REPRESENTATION OF CHINESE CHARACTERS." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 01 (March 1988): 161–79. http://dx.doi.org/10.1142/s0218001488000121.

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This article discusses some intelligence aspects of Chinese characters. Some basic concepts of two-dimensional pattern representation and artificial intelligence such as semantic networks, forward chaining, deduction and the resolution principle are used to analyze and interpret the syntactic structure, representation, semantics and evolution of Chinese characters. The concept of degrees of ambiguity and the principle of new characters are investigated. It is found that Chinese characters are actually not only artistically elegant and culturally rich but also semantically meaningful and intelligently sound. Finally some topics for future research such as intelligent pattern recognition for Chinese characters, automatic learning and translation, and knowledge-based Chinese language understanding are discussed.

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FELFERNIG, ALEXANDER, GERHARD FRIEDRICH, DIETMAR JANNACH, MARKUS STUMPTNER, and MARKUS ZANKER. "Configuration knowledge representations for Semantic Web applications." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17, no. 1 (February 2003): 31–50. http://dx.doi.org/10.1017/s0890060403171041.

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Today's economy exhibits a growing trend toward highly specialized solution providers cooperatively offering configurable products and services to their customers. This paradigm shift requires the extension of current standalone configuration technology with capabilities of knowledge sharing and distributed problem solving. In this context a standardized configuration knowledge representation language with formal semantics is needed in order to support knowledge interchange between different configuration environments. Languages such as Ontology Inference Layer (OIL) and DARPA Agent Markup Language (DAML+OIL) are based on such formal semantics (description logic) and are very popular for knowledge representation in the Semantic Web. In this paper we analyze the applicability of those languages with respect to configuration knowledge representation and discuss additional demands on expressivity. For joint configuration problem solving it is necessary to agree on a common problem definition. Therefore, we give a description logic based definition of a configuration problem and show its equivalence with existing consistency-based definitions, thus joining the two major streams in knowledge-based configuration (description logics and predicate logic/constraint based configuration).

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Chen, Ping, Wei Ding, and Chengmin Ding. "A Lexical Knowledge Representation Model for Natural Language Understanding." International Journal of Software Science and Computational Intelligence 1, no. 4 (October 2009): 17–35. http://dx.doi.org/10.4018/jssci.2009062502.

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Knowledge representation is essential for semantics modeling and intelligent information processing. For decades researchers have proposed many knowledge representation techniques. However, it is a daunting problem how to capture deep semantic information effectively and support the construction of a large-scale knowledge base efficiently. This article describes a new knowledge representation model, SenseNet, which provides semantic support for commonsense reasoning and natural language processing. SenseNet is formalized with a Hidden Markov Model. An inference algorithm is proposed to simulate human-like natural language understanding procedure. A new measurement, confidence, is introduced to facilitate the natural language understanding. The authors present a detailed case study of applying SenseNet to retrieving compensation information from company proxy filings.

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Tsuboi, Yusei, Zuwairie Ibrahim, and Osamu Ono. "DNA computing approach to semantic knowledge representation." International Journal of Hybrid Intelligent Systems 2, no. 1 (June 14, 2005): 1–12. http://dx.doi.org/10.3233/his-2005-2101.

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Lee, Edward T. "Pictorial knowledge representation using pictorial semantic networks." Robotica 6, no. 2 (April 1988): 155–60. http://dx.doi.org/10.1017/s0263574700003970.

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SUMMARYClassifications of pictures and pictorial knowledge are presented. Pictorial knowledge is divided into three classes – angular pictorial knowledge, side pictorial knowledge, and angular and side pictorial knowledge. A block diagram of these three pictorial knowledge classes and a pictorial knowledge transformation module is also presented with illustrative examples. Pictorial semantic networks which in terms of pictorial nodes, property nodes, “is a” links, “has property” links, and “if and only if” links are introduced. Transitivity, generalization, specialization, inheritance hierarchy, and knowledge transformation properties are stated and illustrated by examples. Triangular, quadrangular, and polygonal knowledge representation using pictorial semantic networks are presented. The concepts of deducible property nodes are also presented with illustrative examples. Additional facts can be established from pictorial semantic networks. Thus, pictorial semantic networks are a useful way to represent pictorial knowledge in domains that use well-established taxonomies to simplify problem solving in pictorial information systems. Pictorial semantic networks offer what appears to be a fertile field for future study. The results may have useful applications in knowledge representation, expert systems, artificial intelligence, knowledge - based systems, pictorial information systems and related areas.

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Zhuhadar, Leyla, Olfa Nasraoui, Robert Wyatt, and Rong Yang. "Visual knowledge representation of conceptual semantic networks." Social Network Analysis and Mining 1, no. 3 (November 25, 2010): 219–29. http://dx.doi.org/10.1007/s13278-010-0008-2.

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Elizarov, A. M., A. V. Kirillovich, E. K. Lipachev, O. A. Nevzorova, V. D. Solovyev, and N. G. Zhiltsov. "Mathematical knowledge representation: semantic models and formalisms." Lobachevskii Journal of Mathematics 35, no. 4 (October 2014): 348–54. http://dx.doi.org/10.1134/s1995080214040143.

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Kusuma, I. Made Cantiawan Giri, and Cokorda Rai Adi Pramartha. "Ontology Development for Motorcycle Semantic Knowledge Representation." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, no. 2 (November 22, 2020): 177. http://dx.doi.org/10.24843/jlk.2020.v09.i02.p03.

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Transportation is currently a basic necessity in supporting daily life, starting from supporting economic activities and various other things. One of the most commonly used means of transportation is a motorcycle because it is very practical to use. However, the development of motorcycles is currently very fast, confusing the community in choosing a motorcycle that suits their needs. The solution used to overcome this problem can be overcome by using the concept of semantic ontology. The method of building the ontology model used is METHONTOLOGY. This method is one of the methods of building an ontology model that can reuse the built ontology for further system development. The motorcycle ontology development model generates 16 classes, 13 object properties, 11 data properties, and 69 individuals. The ontology evaluation process by performing SPARQL queries also provides appropriate results.

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Sarica, Serhad, and Jianxi Luo. "DESIGN KNOWLEDGE REPRESENTATION WITH TECHNOLOGY SEMANTIC NETWORK." Proceedings of the Design Society 1 (July 27, 2021): 1043–52. http://dx.doi.org/10.1017/pds.2021.104.

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AbstractEngineers often need to discover and learn designs from unfamiliar domains for inspiration or other particular uses. However, the complexity of the technical design descriptions and the unfamiliarity to the domain make it hard for engineers to comprehend the function, behavior, and structure of a design. To help engineers quickly understand a complex technical design description new to them, one approach is to represent it as a network graph of the design-related entities and their relations as an abstract summary of the design. While graph or network visualizations are widely adopted in the engineering design literature, the challenge remains in retrieving the design entities and deriving their relations. In this paper, we propose a network mapping method that is powered by Technology Semantic Network (TechNet). Through a case study, we showcase how TechNet’s unique characteristic of being trained on a large technology-related data source advantages itself over common-sense knowledge bases, such as WordNet and ConceptNet, for design knowledge representation.

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Zaitchik, Deborah, and Gregg E. A. Solomon. "Putting semantics back into the semantic representation of living things." Behavioral and Brain Sciences 24, no. 3 (June 2001): 496–97. http://dx.doi.org/10.1017/s0140525x01414154.

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The authors' model reduces the literature on conceptual representation to a single node: “encyclopedic knowledge.” The structure of conceptual knowledge is not so trivial. By ignoring the phenomena central to reasoning about living things, the authors base their dismissal of semantic systems on inadequate descriptive ground. A better descriptive account is available in the conceptual development literature. Neuropsychologists could import the insights and tasks from cognitive development to improve their studies.

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Zhang, Jun, Xiangfeng Luo, Xiang He, and Chuanliang Cai. "Text Semantic Mining Model Based on the Algebra of Human Concept Learning." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 2 (April 2011): 80–96. http://dx.doi.org/10.4018/jcini.2011040105.

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Dealing with the large-scale text knowledge on the Web has become increasingly important with the development of the Web, yet it confronts with several challenges, one of which is to find out as much semantics as possible to represent text knowledge. As the text semantic mining process is also the knowledge representation process of text, this paper proposes a text knowledge representation model called text semantic mining model (TSMM) based on the algebra of human concept learning, which both carries rich semantics and is constructed automatically with a lower complexity. Herein, the algebra of human concept learning is introduced, which enables TSMM containing rich semantics. Then the formalization and the construction process of TSMM are discussed. Moreover, three types of reasoning rules based on TSMM are proposed. Lastly, experiments and the comparison with current text representation models show that the given model performs better than others.

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Dhombres, Ferdinand, and Jean Charlet. "Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook." Yearbook of Medical Informatics 29, no. 01 (August 2020): 163–68. http://dx.doi.org/10.1055/s-0040-1702010.

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Objective: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2019. Methods: A comprehensive and standardized review of the biomedical informatics literature was performed to select the most interesting papers of KRM published in 2019, based on PubMed and ISI Web Of Knowledge queries. Results: Four best papers were selected among 1,189 publications retrieved, following the usual International Medical Informatics Association Yearbook reviewing process. In 2019, research areas covered by pre-selected papers were represented by the design of semantic resources (methods, visualization, curation) and the application of semantic representations for the integration/enrichment of biomedical data. Besides new ontologies and sound methodological guidance to rethink knowledge bases design, we observed large scale applications, promising results for phenotypes characterization, semantic-aware machine learning solutions for biomedical data analysis, and semantic provenance information representations for scientific reproducibility evaluation. Conclusion: In the KRM selection for 2019, research on knowledge representation demonstrated significant contributions both in the design and in the application of semantic resources. Semantic representations serve a great variety of applications across many medical domains, with actionable results.

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McGregor, Karla K., Robyn M. Newman, Renée M. Reilly, and Nina C. Capone. "Semantic Representation and Naming in Children With Specific Language Impairment." Journal of Speech, Language, and Hearing Research 45, no. 5 (October 2002): 998–1014. http://dx.doi.org/10.1044/1092-4388(2002/081).

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When 16 children with SLI (mean age=6;2) and 16 normally developing age-mates named age-appropriate objects, the SLI cohort made more naming errors. For both cohorts, semantic misnaming and indeterminate responses were the predominant error types. The contribution of limited semantic representation to these naming errors was explored. Each participant drew and defined each item from his or her semantic and indeterminate error pools and each item from his or her correctly named pool. When compared, the drawings and definitions of items from the error pools were poorer, suggesting limited semantic knowledge. The profiles of information included in definitions of items from the correct pool and the error pools were highly similar, suggesting that representations associated with misnaming differed quantitatively, but not qualitatively, from those associated with correct naming. Eleven members of the SLI cohort also participated in a forced-choice recognition task. Performance was significantly lower on erroneous targets than on correctly named targets. When performance was compared across all three post-naming tasks (drawing, defining, recognition), the participants evinced sparse semantic knowledge for roughly half of all semantic misnaming and roughly one third of all indeterminate responses. In additional cases, representational gaps were evident. This study demonstrates that the degree of knowledge represented in the child's semantic lexicon makes words more or less vulnerable to retrieval failure and that limited semantic knowledge contributes to the frequent naming errors of children with SLI.

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Zhong, Dong, Yi-An Zhu, Lanqing Wang, Junhua Duan, and Jiaxuan He. "A Cognition Knowledge Representation Model Based on Multidimensional Heterogeneous Data." Complexity 2020 (December 28, 2020): 1–17. http://dx.doi.org/10.1155/2020/8812459.

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The information in the working environment of industrial Internet is characterized by diversity, semantics, hierarchy, and relevance. However, the existing representation methods of environmental information mostly emphasize the concepts and relationships in the environment and have an insufficient understanding of the items and relationships at the instance level. There are also some problems such as low visualization of knowledge representation, poor human-machine interaction ability, insufficient knowledge reasoning ability, and slow knowledge search speed, which cannot meet the needs of intelligent and personalized service. Based on this, this paper designs a cognitive information representation model based on a knowledge graph, which combines the perceptual information of industrial robot ontology with semantic description information such as functional attributes obtained from the Internet to form a structured and logically reasoned cognitive knowledge graph including perception layer and cognition layer. Aiming at the problem that the data sources of the knowledge base for constructing the cognitive knowledge graph are wide and heterogeneous, and there are entity semantic differences and knowledge system differences among different data sources, a multimodal entity semantic fusion model based on vector features and a system fusion framework based on HowNet are designed, and the environment description information such as object semantics, attributes, relations, spatial location, and context acquired by industrial robots and their own state information are unified and standardized. The automatic representation of robot perceived information is realized, and the universality, systematicness, and intuition of robot cognitive information representation are enhanced, so that the cognition reasoning ability and knowledge retrieval efficiency of robots in the industrial Internet environment can be effectively improved.

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McGregor, Karla K., Rena M. Friedman, Renée M. Reilly, and Robyn M. Newman. "Semantic Representation and Naming in Young Children." Journal of Speech, Language, and Hearing Research 45, no. 2 (April 2002): 332–46. http://dx.doi.org/10.1044/1092-4388(2002/026).

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Children's semantic representations and semantic naming errors were the focus of this study. In Experiment 1, 25 normally developing children (mean age=5 years 4 months) named, drew, and defined 20 age-appropriate objects. The results suggested that functional and physical properties are core aspects of object representations in the semantic lexicon and that these representations are often organized and accessed according to a taxonomic hierarchy. Results of a new procedure, comparative picture naming/picture drawing, suggested that the degree of knowledge in the semantic lexicon makes words more or less vulner-able to retrieval failure. Most semantic naming errors were associated with limited semantic knowledge, manifested as either lexical gaps or fragile representations. Comparison of definitions for correctly named and semantically misnamed objects provided converging evidence for this conclusion. In Experiment 2, involving 16 normally developing children (mean age=5 years 5 months), the comparative picture naming/picture drawing results were replicated with a stimulus set that allowed a priori matching of the visual complexity of items drawn from correct and semantic error pools. Discussion focuses on the dynamic nature of semantic representations and the relation between semantic representation and naming during a period of slow mapping. The value of comparative picture naming/ picture drawing as a new method for exploring children's semantic representa-tions is emphasized.

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Poggi, Agostino, and Michele Tomaiuolo. "Multilanguage Semantic Interoperability in Distributed Applications." Journal of Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/182525.

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JOSI is a software framework that tries to simplify the development of such kinds of applications both by providing the possibility of working on models for representing such semantic information and by offering some implementations of such models that can be easily used by software developers without any knowledge about semantic models and languages. This software library allows the representation of domain models through Java interfaces and annotations and then to use such a representation for automatically generating an implementation of domain models in different programming languages (currently Java and C++). Moreover, JOSI supports the interoperability with other applications both by automatically mapping the domain model representations into ontologies and by providing an automatic translation of each object obtained from the domain model representations in an OWL string representation.

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Petridis, K., S. Bloehdorn, C. Saathoff, N. Simou, S. Dasiopoulou, V. Tzouvaras, S. Handschuh, Y. Avrithis, Y. Kompatsiaris, and S. Staab. "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.

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Garcia-Crespo, A., B. Ruiz-Mezcua, J. L. Lopez-Cuadrado, and I. Gonzalez-Carrasco. "Semantic model for knowledge representation in e-business." Knowledge-Based Systems 24, no. 2 (March 2011): 282–96. http://dx.doi.org/10.1016/j.knosys.2010.09.006.

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Berta, Mauro, Luca Caneparo, Alfonso Montuori, and Davide Rolfo. "Semantic urban modelling: Knowledge representation of urban space." Environment and Planning B: Planning and Design 43, no. 4 (October 23, 2015): 610–39. http://dx.doi.org/10.1177/0265813515609820.

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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.

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Yowe, Samson Cornelius Gele, and I. Gede Santi Astawa. "Ontology-based Approach for Laptop Semantic Knowledge Representation." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, no. 4 (May 29, 2021): 485. http://dx.doi.org/10.24843/jlk.2021.v09.i04.p05.

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The rapid development of technology requires everyone to adapt. It is the same as the demands of work and school so that everyone should be more able to handle this problem. It is inevitable that the use of laptops today is not something that is a step. Almost all age groups use laptops to do school work, complete office work, or as a medium of entertainment. With so many types of laptops, some people are confused about choosing a laptop. The use of ontology as an information representation technique is a solution to this problem. Ontology can present information or knowledge sources semantically and organize various information resources in a systematic and structured manner. In the development of this ontology will be made using the methontology method. Methontology is one of the ontology model development methodologies which has advantages related to a detailed description of each activity. In addition, methontology also has other advantages, namely the development of ontology that are now made usable for further system development. Therefore, this study is proposed to build an ontology model that represents knowledge about laptops.

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BIEMANN, CHRIS, STEFANO FARALLI, ALEXANDER PANCHENKO, and SIMONE PAOLO PONZETTO. "A framework for enriching lexical semantic resources with distributional semantics." Natural Language Engineering 24, no. 2 (January 15, 2018): 265–312. http://dx.doi.org/10.1017/s135132491700047x.

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AbstractWe present an approach to combining distributional semantic representations induced from text corpora with manually constructed lexical semantic networks. While both kinds of semantic resources are available with high lexical coverage, our aligned resource combines the domain specificity and availability of contextual information from distributional models with the conciseness and high quality of manually crafted lexical networks. We start with a distributional representation of induced senses of vocabulary terms, which are accompanied with rich context information given by related lexical items. We then automatically disambiguate such representations to obtain a full-fledged proto-conceptualization, i.e. a typed graph of induced word senses. In a final step, this proto-conceptualization is aligned to a lexical ontology, resulting in a hybrid aligned resource. Moreover, unmapped induced senses are associated with a semantic type in order to connect them to the core resource. Manual evaluations against ground-truth judgments for different stages of our method as well as an extrinsic evaluation on a knowledge-based Word Sense Disambiguation benchmark all indicate the high quality of the new hybrid resource. Additionally, we show the benefits of enriching top-down lexical knowledge resources with bottom-up distributional information from text for addressing high-end knowledge acquisition tasks such as cleaning hypernym graphs and learning taxonomies from scratch.

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Przelaskowski, Artur. "Semantic Sparse Representation of Disease Patterns." International Journal of Electronics and Telecommunications 56, no. 3 (September 1, 2010): 273–80. http://dx.doi.org/10.2478/v10177-010-0036-x.

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Semantic Sparse Representation of Disease PatternsSparse data representation is discussed in a context of useful fundamentals led to semantic content description and extraction of information. Disease patterns as semantic information extracted from medical images were underlined because of discussed application of computer-aided diagnosis. Compressive sensing rules were adjusted to the requirements of diagnostic pattern recognition. Proposed methodology of sparse disease patterns considers accuracy of sparse representation to estimate target content for detailed analysis. Semantics of sparse representation were modeled by morphological content analysis. Subtle or hidden components were extracted and displayed to increase information completeness. Usefulness of sparsity was verified for computer-aided diagnosis of stroke based on brain CT scans. Implemented method was based on selective and sparse representation of subtle hypodensity to improve diagnosis. Visual expression of disease signatures was fixed to radiologist requirements, domain knowledge and experimental analysis issues. Diagnosis assistance suitability was proven by experimental subjective rating and automatic recognition.

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Popping, Roel, and Inge Strijker. "Representation and integration of sociological knowledge using knowledge graphs." Social Science Information 36, no. 4 (December 1997): 731–47. http://dx.doi.org/10.1177/053901897036004006.

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The representation and integration of sociological knowledge using knowledge graphs, a specific kind of semantic network, is discussed. Knowledge is systematically searched; this reveals inconsistencies, reducing superfluous research and knowledge, and showing gaps in a theory. This representation is conceivable under certain conditions, which are discussed. A graph for sociological theories about labour markets is presented.

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Di Sciascio, E., F. M. Donini, and M. Mongiello. "Structured Knowledge Representation for Image Retrieval." Journal of Artificial Intelligence Research 16 (April 1, 2002): 209–57. http://dx.doi.org/10.1613/jair.902.

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We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete client-server image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval.

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Dworschak, Fabian, Patricia Kügler, Benjamin Schleich, and Sandro Wartzack. "Integrating the Mechanical Domain into Seed Approach." Proceedings of the Design Society: International Conference on Engineering Design 1, no. 1 (July 2019): 2587–96. http://dx.doi.org/10.1017/dsi.2019.265.

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AbstractData-driven technologies have found their way into all areas of engineering. In product development they can accelerate the customization to individualized requirements. Therefore, they need a database that exceeds common product data management systems. The creation of this database proves to be challenging because in addition to explicit standards and regulations the product design contains implicit knowledge of product developers. Hence, this paper presents an approach for the semantic integration of the engineering design (SeED). The goal is an automated design of an ontology, which represents the product design in detail.SeED fulfils two tasks. First, the ontology provides a machine-processable representation of the products design, which enables all kind of data-driven technologies. Among other representations, the ontology contains formal logics and semantics. Accordingly, it is a more comprehensible solution for product developers and knowledge engineers. Second, the detailed representation enables discovering of intrinsic knowledge, e.g. design patterns in product generations. Consequently, SeED is a novel approach for efficient semantic integration of the product design.

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JADIDINEJAD, A. H., F. MAHMOUDI, and M. R. MEYBODI. "Clique-based semantic kernel with application to semantic relatedness." Natural Language Engineering 21, no. 5 (April 14, 2015): 725–42. http://dx.doi.org/10.1017/s135132491500008x.

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AbstractThe emergence of knowledge repositories in a variety of domains provides a valuable opportunity for semantic interpretation of high dimensional datasets. Previous researches investigate the use of concept instead of word as a core semantic feature for incorporating semantic knowledge from an ontology into the representation model of documents. On the other hand, in machine learning and information retrieval, data objects are represented as a flat feature vector. The inconsistency between the structural nature of the knowledge repositories and the flat representation of features in machine learning leads researchers to neglect the structure of the knowledge base and leverage concepts as isolated semantic features, which is known as bag-of-concepts. Although, using concepts has some advantages over words, by neglecting the relation between concepts, the problem of vocabulary mismatch remains in force. In this paper, a novel semantic kernel is proposed which is capable of incorporating the relatedness between conceptual features. This kernel leverages clique theory to map data objects to a novel feature space wherein complex data objects will be comparable. The proposed kernel is relevant to all applications which have a prior knowledge about the relatedness between features. We concentrate on representing text documents and words using Wikipedia and WordNet, respectively. The experimental results over a set of benchmark datasets have revealed that the proposed kernel significantly improves the representation of both words and texts in the application of semantic relatedness.

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Song, Linfeng, Daniel Gildea, Yue Zhang, Zhiguo Wang, and Jinsong Su. "Semantic Neural Machine Translation Using AMR." Transactions of the Association for Computational Linguistics 7 (November 2019): 19–31. http://dx.doi.org/10.1162/tacl_a_00252.

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It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.

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Chen, Xiao Ying, and Bin He. "Knowledge Representation Method of Product Principle Solution Based on Semantic Network Model." Applied Mechanics and Materials 34-35 (October 2010): 1865–69. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.1865.

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Product design is a problem-solving activity based on knowledge. This paper is devoted to presenting a systematic knowledge representation method of principle solution based on semantic network model. For the expression of product knowledge, the semantic object, constraints and their relationships among the expression of the semantic object network model are proposed step by step. Then the principle solution representation model based on semantic network model is put forwards. The knowledge representation of a car is given as an example, which demonstrates that this method is obviously helpful for knowledge-based design system and product innovation.

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Mori, Angelo Rossi, Elena Galeazzi, Aldo Gangemi, Domenico M. Pisanelli, and Anna M. Thornton. "Semantic Standards for the Representation of Medical Records." Medical Decision Making 11, no. 4_suppl (December 1991): S76—S80. http://dx.doi.org/10.1177/0272989x9101104s15.

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Physicians developed their sublanguage (a system to represent medical concepts and their relations) to store and transmit general medical knowledge and patient-related information. Adequate formalisms are needed to obtain a standard representation of semantics of medical expressions for computer use. Comparison of the semantic contents of two expressions is possible only if a unique canonical form is defined; the transmission of medical facts or patient-related information is really meaningful only by defining a set of primitives (semantic categories and links) and the domains of values (concepts). These primitives must be harmonized to yield a “common core subset” of semantic categories and links. This subset provides a common basis; a procedure to register extension sets of primitives must also be defined, to comply with specific representation needs of specialties and classes of application software.

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McCray, A. T., and S. J. Nelson. "The Representation of Meaning in the UMLS." Methods of Information in Medicine 34, no. 01/02 (1995): 193–201. http://dx.doi.org/10.1055/s-0038-1634592.

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Abstract:The UMLS knowledge sources provide detailed information about biomedical naming systems and databases. The Metathesaurus contains biomedical terminology from an increasing number of biomedical thesauri, and the Semantic Network provides a structure that encompasses and unifies the thesauri that are included in the Metathesaurus. This paper addresses some fundamental principles underlying the design and development of the Metathesaurus and Semantic Network. It begins with a description of the formal properties of thesauri, including the Metathesaurus, and the formal properties of the Semantic Network. It continues with consideration of the principle of semantic locality and how this is reflected in the UMLS knowledge sources. The paper concludes with a discussion of the issues involved in attempting to re-use knowledge and the potential for re-use of the UMLS knowledge sources.

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Cantador, Iván, Pablo Castells, and Alejandro Bellogín. "An Enhanced Semantic Layer for Hybrid Recommender Systems." International Journal on Semantic Web and Information Systems 7, no. 1 (January 2011): 44–78. http://dx.doi.org/10.4018/jswis.2011010103.

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Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources.

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Kamthan, Pankaj. "A framework for knowledge representation in semantic mobile applications." International Journal of Web and Grid Services 2, no. 4 (2006): 406. http://dx.doi.org/10.1504/ijwgs.2006.011712.

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Capitani, E., R. Barbarotto, and M. Laiacona. "Gender differences and the brain representation of semantic knowledge." Brain and Language 95, no. 1 (October 2005): 56–57. http://dx.doi.org/10.1016/j.bandl.2005.07.022.

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Tah, Joseph H. M., and Henry F. Abanda. "Sustainable building technology knowledge representation: Using Semantic Web techniques." Advanced Engineering Informatics 25, no. 3 (August 2011): 547–58. http://dx.doi.org/10.1016/j.aei.2011.02.006.

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Niu, Guanglin, Yongfei Zhang, Bo Li, Peng Cui, Si Liu, Jingyang Li, and Xiaowei Zhang. "Rule-Guided Compositional Representation Learning on Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2950–58. http://dx.doi.org/10.1609/aaai.v34i03.5687.

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Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Moreover, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.

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JÓZEFOWSKA, JOANNA, AGNIESZKA ŁAWRYNOWICZ, and TOMASZ ŁUKASZEWSKI. "The role of semantics in mining frequent patterns from knowledge bases in description logics with rules." Theory and Practice of Logic Programming 10, no. 3 (May 2010): 251–89. http://dx.doi.org/10.1017/s1471068410000098.

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AbstractWe propose a new method for mining frequent patterns in a language that combines both Semantic Web ontologies and rules. In particular, we consider the setting of using a language that combines description logics (DLs) with DL-safe rules. This setting is important for the practical application of data mining to the Semantic Web. We focus on the relation of the semantics of the representation formalism to the task of frequent pattern discovery, and for the core of our method, we propose an algorithm that exploits the semantics of the combined knowledge base. We have developed a proof-of-concept data mining implementation of this. Using this we have empirically shown that using the combined knowledge base to perform semantic tests can make data mining faster by pruning useless candidate patterns before their evaluation. We have also shown that the quality of the set of patterns produced may be improved: the patterns are more compact, and there are fewer patterns. We conclude that exploiting the semantics of a chosen representation formalism is key to the design and application of (onto-)relational frequent pattern discovery methods.

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Volkova, Galina D., Olga V. Novoselova, Elena G. Semyachkova, and Tatiana B. Turbeyeva. "Method of Mapping for Semantic Static Constructions into Syntactic Constructions in the Design of Information-Active Systems." EPJ Web of Conferences 224 (2019): 06005. http://dx.doi.org/10.1051/epjconf/201922406005.

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The design of information-active systems provides the formation of the model representation of automated tasks, which is invariant to the environment and means of software and hardware implementation. The syntactic (info-logical) model representation of applied problems will be adequate to initial requirements only if they provide meaningful unity. It is determined by the initial formation of a knowledge model or conceptual representation of applied tasks. The conjugation of semantic and syntactic static constructions is based on the regularity of mapping in the framework of the methodology of intellectual labor automation. The formal description of connections (mapping) of semantic (conceptual) and syntactic (info-logical) representations on the basis of the regularity of mapping allows limiting the set of possible relations and connections in verbal syntactical constructions for representation of subject tasks and providing completeness of the formalized (syntactic) representations at the expense of their semantic addition.

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Mu, Shanlei, Yaliang Li, Wayne Xin Zhao, Siqing Li, and Ji-Rong Wen. "Knowledge-Guided Disentangled Representation Learning for Recommender Systems." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–26. http://dx.doi.org/10.1145/3464304.

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In recommender systems, it is essential to understand the underlying factors that affect user-item interaction. Recently, several studies have utilized disentangled representation learning to discover such hidden factors from user-item interaction data, which shows promising results. However, without any external guidance signal, the learned disentangled representations lack clear meanings, and are easy to suffer from the data sparsity issue. In light of these challenges, we study how to leverage knowledge graph (KG) to guide the disentangled representation learning in recommender systems. The purpose for incorporating KG is twofold, making the disentangled representations interpretable and resolving data sparsity issue. However, it is not straightforward to incorporate KG for improving disentangled representations, because KG has very different data characteristics compared with user-item interactions. We propose a novel K nowledge-guided D isentangled R epresentations approach ( KDR ) to utilizing KG to guide the disentangled representation learning in recommender systems. The basic idea, is to first learn more interpretable disentangled dimensions (explicit disentangled representations) based on structural KG, and then align implicit disentangled representations learned from user-item interaction with the explicit disentangled representations. We design a novel alignment strategy based on mutual information maximization. It enables the KG information to guide the implicit disentangled representation learning, and such learned disentangled representations will correspond to semantic information derived from KG. Finally, the fused disentangled representations are optimized to improve the recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model in terms of both performance and interpretability.

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Carlson, Thomas A., Ryan A. Simmons, Nikolaus Kriegeskorte, and L. Robert Slevc. "The Emergence of Semantic Meaning in the Ventral Temporal Pathway." Journal of Cognitive Neuroscience 26, no. 1 (January 2014): 120–31. http://dx.doi.org/10.1162/jocn_a_00458.

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In the ventral visual pathway, early visual areas encode light patterns on the retina in terms of image properties, for example, edges and color, whereas higher areas encode visual information in terms of objects and categories. At what point does semantic knowledge, as instantiated in human language, emerge? We examined this question by studying whether semantic similarity in language relates to the brain's organization of object representations in inferior temporal cortex (ITC), an area of the brain at the crux of several proposals describing how the brain might represent conceptual knowledge. Semantic relationships among words can be viewed as a geometrical structure with some pairs of words close in their meaning (e.g., man and boy) and other pairs more distant (e.g., man and tomato). ITC's representation of objects similarly can be viewed as a complex structure with some pairs of stimuli evoking similar patterns of activation (e.g., man and boy) and other pairs evoking very different patterns (e.g., man and tomato). In this study, we examined whether the geometry of visual object representations in ITC bears a correspondence to the geometry of semantic relationships between word labels used to describe the objects. We compared ITC's representation to semantic structure, evaluated by explicit ratings of semantic similarity and by five computational measures of semantic similarity. We show that the representational geometry of ITC—but not of earlier visual areas (V1)—is reflected both in explicit behavioral ratings of semantic similarity and also in measures of semantic similarity derived from word usage patterns in natural language. Our findings show that patterns of brain activity in ITC not only reflect the organization of visual information into objects but also represent objects in a format compatible with conceptual thought and language.

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RIBARIĆ, SLOBODAN. "KNOWLEDGE REPRESENTATION SCHEME BASED ON PETRI NET THEORY." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 04 (December 1988): 691–700. http://dx.doi.org/10.1142/s0218001488000431.

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An original knowledge representation scheme named KRP based on Petri net theory is proposed. The formal description of the scheme, and the inference procedure similar to "intersection search" in semantic networks, are given.

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Journal articles on the topic 'Semantic knowledge representation'

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