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Journal articles on the topic 'Semantic Annotation'
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1
Zenonas, Theodosiou, and Tsapatsoulis Nicolas. "Image annotation: the effects of content, lexicon and annotation method." International Journal of Multimedia Information Retrieval 9 (March 1, 2020): 191–203. https://doi.org/10.1007/s13735-020-00193-z.
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Image annotation is the process of assigning metadata to images, allowing effective retrieval by text-based search techniques. Despite the lots of eorts in automatic multimedia analysis, automatic semantic annotation of multimedia is still inefficient due to the problems in modelling high level semantic terms. In this paper we examine the factors affecting the quality of annotations collected through crowdsourcing platforms. An image dataset was manually annotated utilizing: (i) a vocabulary consists of pre-selected set of keywords,(ii) an hierarchical vocabulary, and (iii) free keywords. The results show that the annotation quality is affected by the image content itself and the used lexicon. As we expected while annotation using the hierarchical vocabulary is more representative, the use of free keywords leads to increased invalid annotation. Finally it is shown that images requiring annotations that are not directly related to their content (i.e. annotation using abstract concepts), lead to accrue annotator inconsistency revealing in that way the diculty in annotating such kind of images is not limited to automatic annotation, but it is generic problem of annotation.
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Abderrahman, Chekry, Oriche Aziz, and Khaldi Mohamed. "Semantic Annotation of Resources of Distance Learning Based Intelligent Agents." International Journal of Engineering Pedagogy (iJEP) 4, no. 1 (2014): 69. http://dx.doi.org/10.3991/ijep.v4i1.2845.
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This paper presents a system based on intelligent agents for the semantic annotation of learning resources taking into account the context of training. Semantic annotations systems rarely treat existing semantic annotations in the field of distance education (e-learning), most researchers in the field of education limits annotations to specific cases (teacher annotation, learner annotation, annotation of electronic documents etc.) these annotations are edited by users with an annotation tools, by cons in our approach, we propose a semantic annotation system based on intelligent agents that manage semantic annotations of educational resources, these annotations are guided by domain ontologies and ontology applications. We believe that the original resource annotations, a storehouse of learning objects standardized by LOM profile, these learning objects are managed using an ontology learning.
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Wang, Shu, and Phillip C. Y. Sheu. "Computational Annotations: SCDL-NL as a Structured Annotation Language." International Journal of Semantic Computing 09, no. 04 (2015): 503–21. http://dx.doi.org/10.1142/s1793351x15500117.
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In this paper, we categorize “semantics” into “taxonomical semantics”, “syntactical semantics” and “formal semantics”. We propose a declarative meta-language SCDL-NL as the foundation of a general annotation language in which “taxonomical and syntactical semantic” information of a sentence can be clearly defined. Since pure natural language is too complicated to be used as a general annotation language, the annotation language imposes some restrictions on the English grammar so that it can be easily translated into SCDL-NL to facilitate information retrieval.
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Kors, Jan A., Simon Clematide, Saber A. Akhondi, Erik M. van Mulligen, and Dietrich Rebholz-Schuhmann. "A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC." Journal of the American Medical Informatics Association 22, no. 5 (2015): 948–56. http://dx.doi.org/10.1093/jamia/ocv037.
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Abstract Objective To create a multilingual gold-standard corpus for biomedical concept recognition. Materials and methods We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and cross-language consistency checks were carried out to arrive at the final annotations. Results The number of final annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are similar to those between individual annotators and the gold standard. The automatically generated harmonized annotation set for each language performed equally well as the best annotator for that language. Discussion The use of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance of automatic annotation techniques. Conclusion To our knowledge, this is the first gold-standard corpus for biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety of semantic groups that are being covered, and the diversity of text genres that were annotated.
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Wiktorin, Thomas, Daniel Grigutsch, Felix Erdfelder, et al. "Collaborative Semantic Annotation Tooling (CoAT) to Improve Efficiency and Plug-and-Play Semantic Interoperability in the Secondary Use of Medical Data: Concept, Implementation, and First Cross-Institutional Experiences." Applied Sciences 14, no. 2 (2024): 820. http://dx.doi.org/10.3390/app14020820.
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The cross-institutional secondary use of medical data benefits from structured semantic annotation, which ideally enables the matching and merging of semantically related data items from different sources and sites. While numerous medical terminologies and ontologies, as well as some tooling, exist to support such annotation, cross-institutional data usage based on independently annotated datasets is challenging for multiple reasons: the annotation process is resource intensive and requires a combination of medical and technical expertise since it often requires judgment calls to resolve ambiguities resulting from the non-uniqueness of potential mappings to various levels of ontological hierarchies and relational and representational systems. The divergent resolution of such ambiguities can inhibit joint cross-institutional data usage based on semantic annotation since data items with related content from different sites will not be identifiable based on their respective annotations if different choices were made without further steps such as ontological inference, which is still an active area of research. We hypothesize that a collaborative approach to the semantic annotation of medical data can contribute to more resource-efficient and high-quality annotation by utilizing prior annotational choices of others to inform the annotation process, thus both speeding up the annotation itself and fostering a consensus approach to resolving annotational ambiguities by enabling annotators to discover and follow pre-existing annotational choices. Therefore, we performed a requirements analysis for such a collaborative approach, defined an annotation workflow based on the requirement analysis results, and implemented this workflow in a prototypical Collaborative Annotation Tool (CoAT). We then evaluated its usability and present first inter-institutional experiences with this novel approach to promote practically relevant interoperability driven by use of standardized ontologies. In both single-site usability evaluation and the first inter-institutional application, the CoAT showed potential to improve both annotation efficiency and quality by seamlessly integrating collaboratively generated annotation information into the annotation workflow, warranting further development and evaluation of the proposed innovative approach.
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Liu, Zheng. "An Efficient Web Image Annotation Ranking Algorithm." Advanced Materials Research 108-111 (May 2010): 81–87. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.81.
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Existing image annotation approaches mainly concentrate on achieving annotation results. Annotation order has not been taken into account carefully. As orderly annotation list could enhance the performance of image retrieval system, it is of great importance to rank annotations. This paper presents an algorithm to rank Web image annotating results. For an annotated Web image, we firstly partition the image by a region growing method. Secondly, relevance degree between two annotations is estimated through considering both semantic similarity and image content. Next, the regions of unlabeled image to be ranked serve as queries and annotations are used as the data points to be ranked. And then, manifold-ranking algorithm is executed to get the ordered annotation list. Experiments conducted on real-world Web images through NDCG metric demonstrate the effectiveness of the proposed approach.
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Li, Tianyi, Ping Wang, Tian Shi, Yali Bian, and Andy Esakia. "Task as Context: A Sensemaking Perspective on Annotating Inter-Dependent Event Attributes with Non-Experts." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 11, no. 1 (2023): 78–90. http://dx.doi.org/10.1609/hcomp.v11i1.27550.
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This paper explores the application of sensemaking theory to support non-expert crowds in intricate data annotation tasks. We investigate the influence of procedural context and data context on the annotation quality of novice crowds, defining procedural context as completing multiple related annotation tasks on the same data point, and data context as annotating multiple data points with semantic relevance. We conducted a controlled experiment involving 140 non-expert crowd workers, who generated 1400 event annotations across various procedural and data context levels. Assessments of annotations demonstrate that high procedural context positively impacts annotation quality, although this effect diminishes with lower data context. Notably, assigning multiple related tasks to novice annotators yields comparable quality to expert annotations, without costing additional time or effort. We discuss the trade-offs associated with procedural and data contexts and draw design implications for engaging non-experts in crowdsourcing complex annotation tasks.
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XUE, NIANWEN, and MARTHA PALMER. "Adding semantic roles to the Chinese Treebank." Natural Language Engineering 15, no. 1 (2009): 143–72. http://dx.doi.org/10.1017/s1351324908004865.
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AbstractWe report work on adding semantic role labels to the Chinese Treebank, a corpus already annotated with phrase structures. The work involves locating all verbs and their nominalizations in the corpus, and semi-automatically adding semantic role labels to their arguments, which are constituents in a parse tree. Although the same procedure is followed, different issues arise in the annotation of verbs and nominalized predicates. For verbs, identifying their arguments is generally straightforward given their syntactic structure in the Chinese Treebank as they tend to occupy well-defined syntactic positions. Our discussion focuses on the syntactic variations in the realization of the arguments as well as our approach to annotating dislocated and discontinuous arguments. In comparison, identifying the arguments for nominalized predicates is more challenging and we discuss criteria and procedures for distinguishing arguments from non-arguments. In particular we focus on the role of support verbs as well as the relevance of event/result distinctions in the annotation of the predicate-argument structure of nominalized predicates. We also present our approach to taking advantage of the syntactic structure in the Chinese Treebank to bootstrap the predicate-argument structure annotation of verbs. Finally, we discuss the creation of a lexical database of frame files and its role in guiding predicate-argument annotation. Procedures for ensuring annotation consistency and inter-annotator agreement evaluation results are also presented.
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Zakharova, O. V. "Main Aspects of Big Data Semantic Annotation." PROBLEMS IN PROGRAMMING, no. 4 (December 2020): 022–33. http://dx.doi.org/10.15407/pp2020.04.022.
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Semantic annotations, due to their structure, are an integral part of the effective solution of big data problems. However, the problem of defining semantic annotations is not trivial. Manual annotation is not acceptable for big data due to their size and heterogeneity, as well as the complexity and cost of the annotation process, the automatic annotation task for big data has not yet decision. So, resolving the problem of semantic annotation requires modern mixed approaches, which would be based on and using the existing theoretical apparatus, namely methods and models of machine learning, statistical learning, working with content of different types and formats, natural language processing, etc. It also should provide solutions for main annotation tasks: discovering and extracting entities and relationships from content of any type and defining semantic annotations based on existing sources of knowledge (dictionaries, ontologies, etc.). The obtained annotations must be accurate and provide a further opportunity to solve application problems with the annotated data. Note that the big data contents are very different, as a result, their properties that should be annotated are very different too. This requires different metadata to describe the data. It leads to large number of different metadata standards for data of different types or formats appears. However, to effectively solve the annotation problem, it is necessary to have a generalized description of the metadata types, and we have to consider metadata specificity within this description. The purpose of this work is to define the general classification of metadata and determinate common aspects and approaches to big data semantic annotation.
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Wu, Aihua. "Ranking Biomedical Annotations with Annotator’s Semantic Relevancy." Computational and Mathematical Methods in Medicine 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/258929.
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Biomedical annotation is a common and affective artifact for researchers to discuss, show opinion, and share discoveries. It becomes increasing popular in many online research communities, and implies much useful information. Ranking biomedical annotations is a critical problem for data user to efficiently get information. As the annotator’s knowledge about the annotated entity normally determines quality of the annotations, we evaluate the knowledge, that is, semantic relationship between them, in two ways. The first is extracting relational information from credible websites by mining association rules between an annotator and a biomedical entity. The second way is frequent pattern mining from historical annotations, which reveals common features of biomedical entities that an annotator can annotate with high quality. We propose a weighted and concept-extended RDF model to represent an annotator, a biomedical entity, and their background attributes and merge information from the two ways as the context of an annotator. Based on that, we present a method to rank the annotations by evaluating their correctness according to user’s vote and the semantic relevancy between the annotator and the annotated entity. The experimental results show that the approach is applicable and efficient even when data set is large.
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Hoskere, Vedhus, Fouad Amer, Doug Friedel, et al. "InstaDam: Open-Source Platform for Rapid Semantic Segmentation of Structural Damage." Applied Sciences 11, no. 2 (2021): 520. http://dx.doi.org/10.3390/app11020520.
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The tremendous success of automated methods for the detection of damage in images of civil infrastructure has been fueled by exponential advances in deep learning over the past decade. In particular, many efforts have taken place in academia and more recently in industry that demonstrate the success of supervised deep learning methods for semantic segmentation of damage (i.e., the pixel-wise identification of damage in images). However, in graduating from the detection of damage to applications such as inspection automation, efforts have been limited by the lack of large open datasets of real-world images with annotations for multiple types of damage, and other related information such as material and component types. Such datasets for structural inspections are difficult to develop because annotating the complex and amorphous shapes taken by damage patterns remains a tedious task (requiring too many clicks and careful selection of points), even with state-of-the art annotation software. In this work, InstaDam—an open source software platform for fast pixel-wise annotation of damage—is presented. By utilizing binary masks to aid user input, InstaDam greatly speeds up the annotation process and improves the consistency of annotations. The masks are generated by applying established image processing techniques (IPTs) to the images being annotated. Several different tunable IPTs are implemented to allow for rapid annotation of a wide variety of damage types. The paper first describes details of InstaDam’s software architecture and presents some of its key features. Then, the benefits of InstaDam are explored by comparing it to the Image Labeler app in Matlab. Experiments are conducted where two employed student annotators are given the task of annotating damage in a small dataset of images using Matlab, InstaDam without IPTs, and InstaDam. Comparisons are made, quantifying the improvements in annotation speed and annotation consistency across annotators. A description of the statistics of the different IPTs used for different annotated classes is presented. The gains in annotation consistency and efficiency from using InstaDam will facilitate the development of datasets that can help to advance research into automation of visual inspections.
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Hoskere, Vedhus, Fouad Amer, Doug Friedel, et al. "InstaDam: Open-Source Platform for Rapid Semantic Segmentation of Structural Damage." Applied Sciences 11, no. 2 (2021): 520. http://dx.doi.org/10.3390/app11020520.
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The tremendous success of automated methods for the detection of damage in images of civil infrastructure has been fueled by exponential advances in deep learning over the past decade. In particular, many efforts have taken place in academia and more recently in industry that demonstrate the success of supervised deep learning methods for semantic segmentation of damage (i.e., the pixel-wise identification of damage in images). However, in graduating from the detection of damage to applications such as inspection automation, efforts have been limited by the lack of large open datasets of real-world images with annotations for multiple types of damage, and other related information such as material and component types. Such datasets for structural inspections are difficult to develop because annotating the complex and amorphous shapes taken by damage patterns remains a tedious task (requiring too many clicks and careful selection of points), even with state-of-the art annotation software. In this work, InstaDam—an open source software platform for fast pixel-wise annotation of damage—is presented. By utilizing binary masks to aid user input, InstaDam greatly speeds up the annotation process and improves the consistency of annotations. The masks are generated by applying established image processing techniques (IPTs) to the images being annotated. Several different tunable IPTs are implemented to allow for rapid annotation of a wide variety of damage types. The paper first describes details of InstaDam’s software architecture and presents some of its key features. Then, the benefits of InstaDam are explored by comparing it to the Image Labeler app in Matlab. Experiments are conducted where two employed student annotators are given the task of annotating damage in a small dataset of images using Matlab, InstaDam without IPTs, and InstaDam. Comparisons are made, quantifying the improvements in annotation speed and annotation consistency across annotators. A description of the statistics of the different IPTs used for different annotated classes is presented. The gains in annotation consistency and efficiency from using InstaDam will facilitate the development of datasets that can help to advance research into automation of visual inspections.
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Hussein, Shereen A., Howida Youssry Abd El Naby, and Aliaa A. A. Youssif. "Review: Automatic Semantic Image Annotation." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 12 (2016): 7290–97. http://dx.doi.org/10.24297/ijct.v15i12.4357.
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There are many approaches for automatic annotation in digital images. Nowadays digital photography is a common technology for capturing and archiving images because of the digital cameras and storage devices reasonable price. As amount of the digital images increase, the problem of annotating a specific image becomes a critical issue. Automated image annotation is creating a model capable of assigning terms to an image in order to describe its content. There are many image annotation techniques that seek to find the correlation between words and image features such as color, shape, and texture to provide an automatically correct annotation words to images which provides an alternative to the time consuming work of manual image annotation. This paper aims to cover a review on different Models (MT, CRM, CSD-Prop, SVD-COS and CSD-SVD) for automating the process of image annotation as an intermediate step in image retrieval process using Corel 5k images.
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Koseska-Toszewa, Violetta, and Roman Roszko. "On Semantic Annotation in Clarin-PL Parallel Corpora." Cognitive Studies | Études cognitives, no. 15 (December 31, 2015): 211–36. http://dx.doi.org/10.11649/cs.2015.016.
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On Semantic Annotation in Clarin-PL Parallel CorporaIn the article, the authors present a proposal for semantic annotation in Clarin-PL parallel corpora: Polish-Bulgarian-Russian and Polish-Lithuanian ones. Semantic annotation of quantification is a novum in developing sentence level semantics in multilingual parallel corpora. This is why our semantic annotation is manual. The authors hope it will be interesting to IT specialists working on automatic processing of the given natural languages. Semantic annotation defined the way it is defined here will make contrastive studies of natural languages more efficient, which in turn will help verify the results of those studies, and will certainly improve human and machine translations.
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Ispirova, Gordana, Gjorgjina Cenikj, Matevž Ogrinc, et al. "CafeteriaFCD Corpus: Food Consumption Data Annotated with Regard to Different Food Semantic Resources." Foods 11, no. 17 (2022): 2684. http://dx.doi.org/10.3390/foods11172684.
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Besides the numerous studies in the last decade involving food and nutrition data, this domain remains low resourced. Annotated corpuses are very useful tools for researchers and experts of the domain in question, as well as for data scientists for analysis. In this paper, we present the annotation process of food consumption data (recipes) with semantic tags from different semantic resources—Hansard taxonomy, FoodOn ontology, SNOMED CT terminology and the FoodEx2 classification system. FoodBase is an annotated corpus of food entities—recipes—which includes a curated version of 1000 instances, considered a gold standard. In this study, we use the curated version of FoodBase and two different approaches for annotating—the NCBO annotator (for the FoodOn and SNOMED CT annotations) and the semi-automatic StandFood method (for the FoodEx2 annotations). The end result is a new version of the golden standard of the FoodBase corpus, called the CafeteriaFCD (Cafeteria Food Consumption Data) corpus. This corpus contains food consumption data—recipes—annotated with semantic tags from the aforementioned four different external semantic resources. With these annotations, data interoperability is achieved between five semantic resources from different domains. This resource can be further utilized for developing and training different information extraction pipelines using state-of-the-art NLP approaches for tracing knowledge about food safety applications.
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Hao, Tianyong, Chunshen Zhu, Yuanyuan Mu, and Gang Liu. "A user-oriented semantic annotation approach to knowledge acquisition and conversion." Journal of Information Science 43, no. 3 (2016): 393–411. http://dx.doi.org/10.1177/0165551516642688.
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Semantic annotation on natural language texts labels the meaning of an annotated element in specific contexts, and thus is an essential procedure for domain knowledge acquisition. An extensible and coherent annotation method is crucial for knowledge engineers to reduce human efforts to keep annotations consistent. This article proposes a comprehensive semantic annotation approach supported by a user-oriented markup language named UOML to enhance annotation efficiency with the aim of building a high quality knowledge base. UOML is operable by human annotators and convertible to formal knowledge representation languages. A pattern-based annotation conversion method named PAC is further proposed for knowledge exchange by utilizing automatic pattern learning. We designed and implemented a semantic annotation platform Annotation Assistant to test the effectiveness of the approach. By applying this platform in a long-term international research project for more than three years aiming at high quality knowledge acquisition from a classical Chinese poetry corpus containing 52,621 Chinese characters, we effectively acquired 150,624 qualified annotations. Our test shows that the approach has improved operational efficiency by 56.8%, on average, compared with text-based manual annotation. By using UOML, PAC achieved a conversion error ratio of 0.2% on average, significantly improving the annotation consistency compared with baseline annotations. The results indicate the approach is feasible for practical use in knowledge acquisition and conversion.
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Liu, Fagui, Ping Li, and Dacheng Deng. "Device-Oriented Automatic Semantic Annotation in IoT." Journal of Sensors 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/9589064.
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Semantic technologies are the keys to address the problem of information interaction between assorted, heterogeneous, and distributed devices in the Internet of Things (IoT). Semantic annotation of IoT devices is the foundation of IoT semantics. However, the large amount of devices has led to the inadequacy of the manual semantic annotation and stressed the urgency into the research of automatic semantic annotation. To overcome these limitations, a device-oriented automatic semantic annotation method is proposed to annotate IoT devices’ information. The processes and corresponding algorithms of the automatic semantic annotation method are presented in detail, including the information extraction, text classification, property information division, semantic label selection, and information integration. Experiments show that our method is effective for the automatic semantic annotation to IoT devices’ information. In addition, compared to a typical rule-based method, the comparison experiment demonstrates that our approach outperforms this baseline method with respect to the precision and F-measure.
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Ye, Andre, Quan Ze Chen, and Amy Zhang. "Confidence Contours: Uncertainty-Aware Annotation for Medical Semantic Segmentation." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 11, no. 1 (2023): 186–97. http://dx.doi.org/10.1609/hcomp.v11i1.27559.
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Medical image segmentation modeling is a high-stakes task where understanding of uncertainty is crucial for addressing visual ambiguity. Prior work has developed segmentation models utilizing probabilistic or generative mechanisms to infer uncertainty from labels where annotators draw a singular boundary. However, as these annotations cannot represent an individual annotator's uncertainty, models trained on them produce uncertainty maps that are difficult to interpret. We propose a novel segmentation representation, Confidence Contours, which uses high- and low-confidence ``contours’’ to capture uncertainty directly, and develop a novel annotation system for collecting contours. We conduct an evaluation on the Lung Image Dataset Consortium (LIDC) and a synthetic dataset. From an annotation study with 30 participants, results show that Confidence Contours provide high representative capacity without considerably higher annotator effort. We also find that general-purpose segmentation models can learn Confidence Contours at the same performance level as standard singular annotations. Finally, from interviews with 5 medical experts, we find that Confidence Contour maps are more interpretable than Bayesian maps due to representation of structural uncertainty.
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Suhrbier, Lutz, Okka Tschöpe, Walter Berendsohn, and Anton Güntsch. "Integrating AnnoSys into your specimen data portal." Biodiversity Information Science and Standards 1 (August 15, 2017): e20313. https://doi.org/10.3897/tdwgproceedings.1.20313.
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Access to AnnoSys from your portal makes it possible to (1) annotate and to (2) show existing annotations for specimen data records. To this end, weblinks from the page displaying the individual specimen record to AnnoSys are incorporated into your website. In the current (XML-based) system, the portal should provide a link called "annotate" or similar which redirects users to AnnoSys in order to create annotations based on the record data actually shown in your portal. Of course, to enable annotation, an access point for the record to be annotated has to be transmitted with the request. AnnoSys can then download the referred specimen data record. After successfully transferring the data, the user will be redirected to the AnnoSys Annotation Editor first and if they start editing an annotation to the AnnoSys user login/registration dialog subsequently to. At present, AnnoSys supports ABCD 2.06, ABCD 2.1 or SimpleDarwinCore in XML formats. If you already use a BioCASe provider to deliver ABCD data from your collection database, then this is sufficient, but you can also provide any other URL that provides the record in one of the supported formats, and AnnoSys will download the data from that URL. In order to show existing annotations, you request AnnoSys to show them by querying the AnnoSys repository for the ID of the specimen. Currently, the triple ID in use in the GBIF and BioCASE networks is used to identify a specimen. An information request directed to AnnoSys for a certain triple ID will return a JSON response with: hasAnnotation: true/false indicating if there are any annotations available size: number of currently available annotations annotations: a list of relevant annotation metadata you may want to show in your portal, such asannotator (name of the annotator)time (creation time in ms since 01.01.1970)motivation (type) of the annotationrepositoryURI: link to the RDF-Data of the annotationrecordURIs: list of URIs to the original record data (XML) the annotation is based on You can either directly link to the AnnoSys interface to display the existing annotation, using the repositoryURI and recordURIs element values from the JSON response, or you can display selected metadata about already available annotations directly in your portal. For full documentation including examples see the AnnoSys wiki.
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Ghashghaei, Mehrnaz, Ebrahim Bagheri, John Cuzzola, Ali A. Ghorbani, and Zeinab Noorian. "Semantic Disambiguation and Linking of Quantitative Mentions in Textual Content." International Journal of Semantic Computing 10, no. 01 (2016): 121–42. http://dx.doi.org/10.1142/s1793351x16500021.
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Semantic annotation techniques provide the basis for linking textual content with concepts in well grounded knowledge bases. In spite of their many application areas, current semantic annotation systems have some limitations. One of the prominent limitations of such systems is that none of the existing semantic annotator systems are able to identify and disambiguate quantitative (numerical) content. In textual documents such as Web pages, specially technical contents, there are many quantitative information such as product specifications that need to be semantically qualified. In this paper, we propose an approach for annotating quantitative values in short textual content. In our approach, we identify numeric values in the text and link them to an existing property in a knowledge base. Based on this mapping, we are then able to find the concept that the property is associated with, whereby identifying both the concept and the specific property of that concept that the numeric value belongs to. Results obtained from the developed gold standard dataset show that the proposed automated semantic annotation platform is quite effective in detecting and disambiguating numerical content, and connecting them to associated properties on the external knowledge base. Our experiments show that our proposed approach is able to reach an accuracy of over 70% for semantically annotating quantitative content.
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Pan, Zhiyi, Nan Zhang, Wei Gao, Shan Liu, and Ge Li. "Point Cloud Semantic Segmentation with Sparse and Inhomogeneous Annotations." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6354–62. https://doi.org/10.1609/aaai.v39i6.32680.
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Utilizing uniformly distributed sparse annotations, weakly supervised learning alleviates the heavy reliance on fine-grained annotations in point cloud semantic segmentation tasks. However, few works discuss the inhomogeneity of sparse annotations, albeit it is common in real-world scenarios. Therefore, this work introduces the probability density function into the gradient sampling approximation method to qualitatively analyze the impact of annotation sparsity and inhomogeneity under weakly supervised learning. Based on our analysis, we propose an Adaptive Annotation Distribution Network (AADNet) capable of robust learning on arbitrarily distributed sparse annotations. Specifically, we propose a label-aware point cloud downsampling strategy to increase the proportion of annotations involved in the training stage. Furthermore, we design the multiplicative dynamic entropy as the gradient calibration function to mitigate the gradient bias caused by non-uniformly distributed sparse annotations and explicitly reduce the epistemic uncertainty. Without any prior restrictions and additional information, our proposed method achieves comprehensive performance improvements at multiple label rates and different annotation distributions.
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Tschöpe, Okka, Lutz Suhrbier, Anton Güntsch, and Walter Berendsohn. "AnnoSys – an online tool for sharing annotations to enhance data quality." Biodiversity Information Science and Standards 1 (August 15, 2017): e20315. https://doi.org/10.3897/tdwgproceedings.1.20315.
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AnnoSys is a web-based open-source information system that enables users to correct and enrich specimen data published in data portals, thus enhancing data quality and documenting research developments over time. This brings the traditional annotation workflows for specimens to the Internet, as annotations become visible to researchers who subsequently observe the annotated specimen. During its first phase, the AnnoSys project developed a fully functional prototype of an annotation data repository for complex and cross-linked XML-standardized data in the ABCD (Access to biological collection data Berendsohn 2007- and Darwin Core (DwC - Wieczorek et al. 2012) standards, including back-end server functionality, web services and an on-line user interface Tschoepe et al. 2013. Annotation data are stored using the Open Annotation Data Model Sanderson et al. 2013 and an RDF-database Suhrbier et al. 2017. Public access to the annotations and the corresponding copy of the original record is provided via Linked Data, REST and SPARQL web services. AnnoSys can easyly be integrated into portals providing specimen data (see Suhrbier & al., this session). As a result, the individual specimen page then includes two links, one providing access to existing annotations stored in the AnnoSys repository, the other linking to the AnnoSys annotation Editor for annotation input. AnnoSys is now integrated into a dozen specimen portals, including the Global Biodiversity Information Facility GBIF and the Global Genome Biodiversity Network GGBN. In contrast to conventional, site-based annotation systems, annotations regarding a specimen are accessible from all portals providing access to the specimen's data, independent of which portal has originally been used as a starting point for the annotation. Apart from that, users can query the data in the AnnoSys portal or create a subscription to get notified about annotations using criteria referring to the data record. For example, a specialist for a certain family of organisms, working on a flora or fauna of a certain country, may subscribe to that family name and the country. The subscriber is notified by email about any annotations that fulfil these criteria. Other possible subscription and filter criteria include the name of collector, identifer or annotator, catalogue or accession numbers, and collection name or code. For curators a special curatorial workflow supports their handling of annotations, for example confirming a correction according to the annotation in the underlying primary database. User feedback on the currently available system has led to a significantly simplified version of the user interface, which is currently undergoing testing and final implementation. Moreover, the current, second project phase aims at extending the generic qualities of AnnoSys to allow processing of additional data formats, including RDF data with machine readable semantic concepts, and thus opening up the data gathered through AnnoSys for the Semantic Web. We developed a semantic concept driven annotation management, including the specification of a selector concept for RDF data and a repository for original records extended to RDF and other formats. Based on DwC RDF terms and the ABCD ontology, which deconstructs the ABCD XML-schema into individually addressable RDF-resources, we built an "AnnoSys ontology". The AnnoSys-2 system is currently in the testing phase and will be released in 2018. In future research (see Suhrbier, this volume), we will examine the use of AnnoSys for taxon-level data as well as its integration with image annotation systems. BGBM Berlin is committed to sustain AnnoSys beyond the financed project phase.
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Wu, Xiao Ying, Yun Juan Liang, Li Li, and Li Juan Ma. "Semantic Fusion of Image Annotation." Advanced Materials Research 268-270 (July 2011): 1386–89. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1386.
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In this paper, improve the image annotation with semantic meaning, and name the new algorithm for semantic fusion of image annotation, that is a image is given to be labeled, use of training data set, the word set, and a collection of image area and other information to establish the probability model ,estimates the joint probability by word and given image areas.The probability value as the size, combined with keywords relevant table that integrates lexical semantics to extract keywords as the most representative image semantic annotation results. The algorithm can effectively use large-scale training data with rich annotation, so as to achieve better recall and precision than the existing automatic image annotation ,and validate the algorithm in the Corel data set.
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Chen, Jinju, and Shiyan Ou. "Development and application of the semantic annotation framework for digital images." Electronic Library 39, no. 6 (2021): 824–45. http://dx.doi.org/10.1108/el-07-2021-0131.
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Purpose The purpose of this paper is to semantically annotate the content of digital images with the use of Semantic Web technologies and thus facilitate retrieval, integration and knowledge discovery. Design/Methodology/Approach After a review and comparison of the existing semantic annotation models for images and a deep analysis of the characteristics of the content of images, a multi-dimensional and hierarchical general semantic annotation framework for digital images was proposed. On this basis, taking histories images, advertising images and biomedical images as examples, by integrating the characteristics of images in these specific domains with related domain knowledge, the general semantic annotation framework for digital images was customized to form a domain annotation ontology for the images in a specific domain. The application of semantic annotation of digital images, such as semantic retrieval, visual analysis and semantic reuse, were also explored. Findings The results showed that the semantic annotation framework for digital images constructed in this paper provided a solution for the semantic organization of the content of images. On this basis, deep knowledge services such as semantic retrieval, visual analysis can be provided. Originality/Value The semantic annotation framework for digital images can reveal the fine-grained semantics in a multi-dimensional and hierarchical way, which can thus meet the demand for enrichment and retrieval of digital images.
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Zhang, Shuai, Guang Hong, and Bing Xu. "An Improved Semantic Annotation Method." Applied Mechanics and Materials 198-199 (September 2012): 495–99. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.495.
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Semantic annotation is the fundament for the progress and realization of semantic web, meanwhile, provides formatted description for the knowledge in web pages and its semantic meaning in the field. A method for semantic annotation to webpage was presented under the instruction of domain ontology in this paper. By Edit distance and Wordnet distance and from the two aspects the semantic meaning of the word, the semantic correlation degree was measured, then the mapping relation of webpage and ontology was built. Moreover, after the semantic annotating to the WebPages, the ontology was expanded effectively by the annotation results, to domanialize the ontology. At the end, experimental results show the tagging method bases on the weight coefficient acquired form Edit distance, wordnet distance and extended ontology concept is provided with the best performance and the method is effective and applicable.
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Guo, Yu Tang, and Chang Gang Han. "Automatic Image Annotation Using Semantic Subspace Graph Spectral Clustering Algorithm." Advanced Materials Research 271-273 (July 2011): 1090–95. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1090.
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Due to the existing of the semantic gap, images with the same or similar low level features are possibly different on semantic level. How to find the underlying relationship between the high-level semantic and low level features is one of the difficult problems for image annotation. In this paper, a new image annotation method based on graph spectral clustering with the consistency of semantics is proposed with detailed analysis on the advantages and disadvantages of the existed image annotation methods. The proposed method firstly cluster image into several semantic classes by semantic similarity measurement in the semantic subspace. Within each semantic class, images are re-clustered with visual features of region Then, the joint probability distribution of blobs and words was modeled by using Multiple-Bernoulli Relevance Model. We can annotate a unannotated image by using the joint distribution. Experimental results show the the effectiveness of the proposed approach in terms of quality of the image annotation. the consistency of high-level semantics and low level features is efficiently achieved.
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Koseska, Violetta. "Semantics, contrastive linguistics and parallel corpora." Cognitive Studies | Études cognitives, no. 14 (September 4, 2014): 85–100. http://dx.doi.org/10.11649/cs.2014.009.
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Semantics, contrastive linguistics and parallel corporaIn view of the ambiguity of the term “semantics”, the author shows the differences between the traditional lexical semantics and the contemporary semantics in the light of various semantic schools. She examines semantics differently in connection with contrastive studies where the description must necessary go from the meaning towards the linguistic form, whereas in traditional contrastive studies the description proceeded from the form towards the meaning. This requirement regarding theoretical contrastive studies necessitates construction of a semantic interlanguage, rather than only singling out universal semantic categories expressed with various language means. Such studies can be strongly supported by parallel corpora. However, in order to make them useful for linguists in manual and computer translations, as well as in the development of dictionaries, including online ones, we need not only formal, often automatic, annotation of texts, but also semantic annotation - which is unfortunately manual. In the article we focus on semantic annotation concerning time, aspect and quantification of names and predicates in the whole semantic structure of the sentence on the example of the “Polish-Bulgarian-Russian parallel corpus”.
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Valkeapää, Onni, Olli Alm, and Eero Hyvönen. "An Adaptable Framework for Ontology-based Content Creation on the Semantic Web." JUCS - Journal of Universal Computer Science 13, no. (12) (2007): 1835–53. https://doi.org/10.3217/jucs-013-12-1835.
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Creation of rich, ontology-based metadata is one of the major challenges in developing the Semantic Web. Emerging applications utilizing semantic web techniques, such as semantic portals, cannot be realized if there are no proper tools to provide metadata for them. This paper discusses how to make provision of metadata easier and cost-effective by an annotation framework comprising of annotation editor combined with shared ontology services. We have developed an annotation system Saha supporting distributed collaboration in creating annotations, and hiding the complexity of the annotation schema and the domain ontologies from the annotators. Saha adapts flexibly to different metadata schemas, which makes it suitable for different applications. Support for using ontologies is based on ontology services, such as concept searching and browsing, concept URI fetching, semantic autocompletion and linguistic concept extraction. The system is being tested in various practical semantic portal projects.
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Klints, Agute, Madara Stāde, Lauma Pretkalniņa, and Laura Rituma. "Lingvistiskais eksperiments: marķētāju vienprātība latviešu valodas WordNet semantisko saišu veidošanā." Vārds un tā pētīšanas aspekti: rakstu krājums = The Word: Aspects of Research: conference proceedings, no. 27 (November 3, 2023): 125–38. http://dx.doi.org/10.37384/vtpa.2023.27.125.
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This article describes the experiment for determining inter-annotator agreement in the semantic annotation of the Latvian WordNet, which was carried out within the project „Latvian Wordnet and Word Sense Disambiguation”. The semantic relations used in the Latvian WordNet are synonymy, near-synonymy, hyponymy, antonymy, meronymy, gradation sets and similarity (defined as „see also”). These semantic relations are determined for four main parts of speech: nouns, verbs, adjectives, and adverbs. During the experiment, 20 words were selected from the online dictionary Tēzaurs.lv, and the team of linguists individually formed semantic links between the senses of these words and other word senses in the dictionary. The obtained data was analysed using both statistical and qualitative analysis methods to determine the level of inter-annotator agreement and potential causes of differing word sense interpretation. The results of the experiment have led to the conclusion that individual annotation yields a medium level of inter-annotator agreement, whereas collective discussions improve this level significantly, even though certain cases remained where the annotators could not reach a consensus, illustrating the subjective nature of the semantic analysis. The inter-annotator agreement levels for adjectives and adverbs are lower, possibly due to the lack of more precise processing guidelines for these parts of speech. Overall, collective discussions during annotation appear to improve the methodology of semantic annotation significantly.
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Al-Bukhitan, Saeed, Tarek Helmy, and Mohammed Al-Mulhem. "Semantic Annotation Tool for Annotating Arabic Web Documents." Procedia Computer Science 32 (2014): 429–36. http://dx.doi.org/10.1016/j.procs.2014.05.444.
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McGillivray, Barbara, Daria Kondakova, Annie Burman, et al. "A new corpus annotation framework for Latin diachronic lexical semantics." Journal of Latin Linguistics 21, no. 1 (2022): 47–105. http://dx.doi.org/10.1515/joll-2022-2007.
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Abstract We present a new corpus-based resource and methodology for the annotation of Latin lexical semantics, consisting of 2,399 annotated passages of 40 lemmas from the Latin diachronic corpus LatinISE. We also describe how the annotation was designed, analyse annotators’ styles, and present the preliminary results of a study on the lexical semantics and diachronic change of the 40 lemmas. We complement this analysis with a case study on semantic vagueness. As the availability of digital corpora of ancient languages increases, and as computational research develops new methods for large-scale analysis of diachronic lexical semantics, building lexical semantic annotation resources can shed new light on large-scale patterns in the semantic development of lexical items over time. We share recommendations for designing the annotation task that will hopefully help similar research on other less-resourced or historical languages.
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Li, Huadong, Ying Wei, Han Peng, and Wei Zhang. "DiffuPrompter: Pixel-Level Automatic Annotation for High-Resolution Remote Sensing Images with Foundation Models." Remote Sensing 16, no. 11 (2024): 2004. http://dx.doi.org/10.3390/rs16112004.
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Instance segmentation is pivotal in remote sensing image (RSI) analysis, aiding in many downstream tasks. However, annotating images with pixel-wise annotations is time-consuming and laborious. Despite some progress in automatic annotation, the performance of existing methods still needs improvement due to the high precision requirements for pixel-level annotation and the complexity of RSIs. With the support of large-scale data, some foundational models have made significant progress in semantic understanding and generalization capabilities. In this paper, we delve deep into the potential of the foundational models in automatic annotation and propose a training-free automatic annotation method called DiffuPrompter, achieving pixel-level automatic annotation of RSIs. Extensive experimental results indicate that the proposed method can provide reliable pseudo-labels, significantly reducing the annotation costs of the segmentation task. Additionally, the cross-domain validation experiments confirm the powerful effectiveness of large-scale pseudo-data in improving model generalization performance.
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Suhrbier, Lutz, Okka Tschöpe, Anton Güntsch, and Walter G. Berendsohn. "AnnoSys - future developments." Biodiversity Information Science and Standards 1 (August 15, 2017): e20317. https://doi.org/10.3897/tdwgproceedings.1.20317.
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AnnoSys (Tschöpe et al. 2013, Suhrbier et al. 2017) is a web-based open-source system for correcting and enriching biodiversity data in publicly available data portals. Users are enabled to annotate specimen data, and these annotations become visible to researchers who subsequently observe the annotated specimen. The AnnoSys search and subscription capabilities make it possible to access or receive notification of annotations of records and even records of duplicate specimens accessed in different portals. In its current second project phase, the project's technical infrastructure opens from a mixture of structured specimen data based on XML*1 and semantic information (annotations based on W3C Open Annotation*2) into a pure semantic and linked data (Heath and Bizer 2011) oriented service backend. To this end, we are implementing an AnnoSys ontology prototype providing semantic information about supported data elements, their mappings and semantic relationships with data elements from an extensible catalog of relevant biodiversity standards (e.g. ABCD*3, Darwin Core*4) as well as their annotation workflow oriented collection and organisation within so called annotation types. Furthermore, the linked data oriented service backend enables importing, exporting and transforming annotation related information into a variety of data formats and sources. Ultimately, AnnoSys will be upgraded to the new W3C Web Annotation*5 standard for representing annotations in RDF*6. These new facilities permit AnnoSys to hook into a number of annotation workflows in a way that could not have been realised before. Examples include the automatic generation of annotations from the output of data quality control services, the reporting of update or edit processes at provider databases to the AnnoSys service backend, or recording the changes made in large(r) datasets by analysing differences between download and (corrected) upload. The extension of the data domain from specimen data to taxonomic data (i.e. annotation of checklists) is another envisioned development, same as supporting the annotation of multimedia elements (e.g. the images that are inreasingly linked to specimen data records). Within our presentation, we will sketch out some of these use cases to foster the discussion of further workflow scenarios for biodiversity-related annotations.
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Berlanga, Rafael, Victoria Nebot, and María Pérez. "Tailored semantic annotation for semantic search." Journal of Web Semantics 30 (January 2015): 69–81. http://dx.doi.org/10.1016/j.websem.2014.07.007.
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PRADHAN, SAMEER S., EDUARD HOVY, MITCH MARCUS, MARTHA PALMER, LANCE RAMSHAW, and RALPH WEISCHEDEL. "ONTONOTES: A UNIFIED RELATIONAL SEMANTIC REPRESENTATION." International Journal of Semantic Computing 01, no. 04 (2007): 405–19. http://dx.doi.org/10.1142/s1793351x07000251.
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The OntoNotes project is creating a corpus of large-scale, accurate, and integrated annotation of multiple levels of the shallow semantic structure in text. Such rich, integrated annotation covering many levels will allow for richer, cross-level models enabling significantly better automatic semantic analysis. At the same time, it demands a robust, efficient, scalable mechanism for storing and accessing these complex inter-dependent annotations. We describe a relational database representation that captures both the inter- and intra-layer dependencies and provide details of an object-oriented API for efficient, multi-tiered access to this data.
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ALVAREZ, MARCO A., and CHANGHUI YAN. "A GRAPH-BASED SEMANTIC SIMILARITY MEASURE FOR THE GENE ONTOLOGY." Journal of Bioinformatics and Computational Biology 09, no. 06 (2011): 681–95. http://dx.doi.org/10.1142/s0219720011005641.
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Existing methods for calculating semantic similarities between pairs of Gene Ontology (GO) terms and gene products often rely on external databases like Gene Ontology Annotation (GOA) that annotate gene products using the GO terms. This dependency leads to some limitations in real applications. Here, we present a semantic similarity algorithm (SSA), that relies exclusively on the GO. When calculating the semantic similarity between a pair of input GO terms, SSA takes into account the shortest path between them, the depth of their nearest common ancestor, and a novel similarity score calculated between the definitions of the involved GO terms. In our work, we use SSA to calculate semantic similarities between pairs of proteins by combining pairwise semantic similarities between the GO terms that annotate the involved proteins. The reliability of SSA was evaluated by comparing the resulting semantic similarities between proteins with the functional similarities between proteins derived from expert annotations or sequence similarity. Comparisons with existing state-of-the-art methods showed that SSA is highly competitive with the other methods. SSA provides a reliable measure for semantics similarity independent of external databases of functional-annotation observations.
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Mohd, Mudasir, Rafiya Jan, and Nida Hakak. "Enhanced Bootstrapping Algorithm for Automatic Annotation of Tweets." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (2020): 35–60. http://dx.doi.org/10.4018/ijcini.2020040103.
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Annotations are critical in various text mining tasks such as opinion mining, sentiment analysis, word sense disambiguation. Supervised learning algorithms start with the training of the classifier and require manually annotated datasets. However, manual annotations are often subjective, biased, onerous, and burdensome to develop; therefore, there is a need for automatic annotation. Automatic annotators automatically annotate the data for creating the training set for the supervised classifier, but lack subjectivity and ignore semantics of underlying textual structures. The objective of this research is to develop scalable and semantically rich automatic annotation system while incorporating domain dependent characteristics of the annotation process. The authors devised an enhanced bootstrapping algorithm for the automatic annotation of Tweets and employed distributional semantic models (LSA and Word2Vec) to augment the novel Bootstrapping algorithm and tested the proposed algorithm on the 12,000 crowd-sourced annotated Tweets and achieved a 68.56% accuracy which is higher than the baseline accuracy.
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Venturi, Giulia. "Semantic annotation of Italian legal texts." Constructions and Frames 3, no. 1 (2011): 46–79. http://dx.doi.org/10.1075/cf.3.1.02ven.
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The FrameNet approach to text semantic annotation can be a reliable model to make the linguistic information and semantic content of legal texts explicit. This hypothesis is discussed and empirically demonstrated through a trial of annotating a corpus of Italian legal texts. This study aims to show that FrameNet is particularly appropriate to provide new perspectives for legal language studies and for legal knowledge representation tasks. Moreover, by relying on the output of a statistical dependency parser, the FrameNet-based annotation methodology presented here can be used successfully in the automatic semantic processing of legal texts.
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Elsaleh, Tarek, Shirin Enshaeifar, Roonak Rezvani, Sahr Thomas Acton, Valentinas Janeiko, and Maria Bermudez-Edo. "IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services." Sensors 20, no. 4 (2020): 953. http://dx.doi.org/10.3390/s20040953.
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With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.
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FERNÁNDEZ, N., J. A. FISTEUS, D. FUENTES, L. SÁNCHEZ, and V. LUQUE. "A WIKIPEDIA-BASED FRAMEWORK FOR COLLABORATIVE SEMANTIC ANNOTATION." International Journal on Artificial Intelligence Tools 20, no. 05 (2011): 847–86. http://dx.doi.org/10.1142/s0218213011000413.
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The semantic web aims at automating web data processing tasks that nowadays only humans are able to do. To make this vision a reality, the information on web resources should be described in a computer-meaningful way, in a process known as semantic annotation. In this paper, a manual, collaborative semantic annotation framework is described. It is designed to take advantage of the benefits of manual annotation systems (like the possibility of annotating formats difficult to annotate in an automatic manner) addressing at the same time some of their limitations (reduce the burden for non-expert annotators). The framework is inspired by two principles: use Wikipedia as a facade for a formal ontology and integrate the semantic annotation task with common user actions like web search. The tools in the framework have been implemented, and empirical results obtained in experiences carried out with these tools are reported.
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Pado, S., and M. Lapata. "Cross-lingual Annotation Projection for Semantic Roles." Journal of Artificial Intelligence Research 36 (November 17, 2009): 307–40. http://dx.doi.org/10.1613/jair.2863.
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This article considers the task of automatically inducing role-semantic annotations in the FrameNet paradigm for new languages. We propose a general framework that is based on annotation projection, phrased as a graph optimization problem. It is relatively inexpensive and has the potential to reduce the human effort involved in creating role-semantic resources. Within this framework, we present projection models that exploit lexical and syntactic information. We provide an experimental evaluation on an English-German parallel corpus which demonstrates the feasibility of inducing high-precision German semantic role annotation both for manually and automatically annotated English data.
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Misirli, Goksel, Matteo Cavaliere, William Waites, et al. "Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization." Bioinformatics 32, no. 6 (2015): 908–17. http://dx.doi.org/10.1093/bioinformatics/btv660.
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Abstract Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. Availability and implementation: The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo. The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf. Contact: anil.wipat@newcastle.ac.uk or vdanos@inf.ed.ac.uk
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QasimMohammedSalih, Alaa. "Towards Simple Semantic Annotation." International Journal of Computer Applications 68, no. 1 (2013): 5–9. http://dx.doi.org/10.5120/11541-6800.
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Lyon, Douglas. "Semantic Annotation for Java." Journal of Object Technology 9, no. 3 (2010): 19. http://dx.doi.org/10.5381/jot.2010.9.3.c2.
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Koseska-Toszewa, Violetta. "About Certain Semantic Annotation in Parallel Corpora." Cognitive Studies | Études cognitives, no. 13 (June 21, 2015): 67–78. http://dx.doi.org/10.11649/cs.2013.004.
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About Certain Semantic Annotation in Parallel CorporaThe semantic notation analyzed in this works is contained in the second stream of semantic theories presented here – in the direct approach semantics. We used this stream in our work on the Bulgarian-Polish Contrastive Grammar. Our semantic notation distinguishes quantificational meanings of names and predicates, and indicates aspectual and temporal meanings of verbs. It relies on logical scope-based quantification and on the contemporary theory of processes, known as “Petri nets”. Thanks to it, we can distinguish precisely between a language form and its contents, e.g. a perfective verb form has two meanings: an event or a sequence of events and states, finally ended with an event. An imperfective verb form also has two meanings: a state or a sequence of states and events, finally ended with a state. In turn, names are quantified universally or existentially when they are “undefined”, and uniquely (using the iota operator) when they are “defined”. A fact worth emphasizing is the possibility of quantifying not only names, but also the predicate, and then quantification concerns time and aspect. This is a novum in elaborating sentence-level semantics in parallel corpora. For this reason, our semantic notation is manual. We are hoping that it will raise the interest of computer scientists working on automatic methods for processing the given natural languages. Semantic annotation defined like in this work will facilitate contrastive studies of natural languages, and this in turn will verify the results of those studies, and will certainly facilitate human and machine translations.
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Xue, Ruo Juan. "An Effective Approach for Instructional Resource Database Construction with Web Images." Key Engineering Materials 439-440 (June 2010): 1361–66. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1361.
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In order to effectively utilize Web images to construct instructional resource database, a novel approach is proposed in this paper. With this approach, Web images and their semantics can be automatically downloaded, extracted and stored in resource database and the semantics can be refined by user feedback in retrieval progress. Image topic dictionary is built as the basis to extract semantics. Eight kinds of text are extracted as semantic source from Web pages. Based on image topic dictionary, image semantics can be extracted from the eight kinds of text. In order to further improve the accuracy of semantic extraction, we propose relevance feedback mechanism. Users can provide feedback to refine semantic annotation. The experimental results show that the approach is effective, in which high construction efficiency and quality can be achieved. The approach is better than manual annotation in efficiency and better than automatic annotation in accuracy. The similar methods can be applied to construct resource database of other forms of multimedia.
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Slimani, Thabet. "Semantic Annotation: The Mainstay of Semantic Web." International Journal of Computer Applications Technology and Research 2, no. 6 (2013): 763–70. http://dx.doi.org/10.7753/ijcatr0206.1025.
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Liu, Yongmei, Tanakrit Wongwitit, and Linsen Yu. "Automatic Image Annotation Based on Scene Analysis." International Journal of Image and Graphics 14, no. 03 (2014): 1450012. http://dx.doi.org/10.1142/s0219467814500120.
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Automatic image annotation is an important and challenging job for image analysis and understanding such as content-based image retrieval (CBIR). The relationship between the keywords and visual features is too complicated due to the semantic gap. We present an approach of automatic image annotation based on scene analysis. With the constrain of scene semantics, the correlation between keywords and visual features becomes simpler and clearer. Our model has two stages of process. The first stage is training process which groups training image data set into semantic scenes using the extracted semantic feature and visual scenes constructed from the calculation distances of visual features for every pairs of training images by using Earth mover's distance (EMD). Then, combine a pair of semantic and visual scene together and apply Gaussian mixture model (GMM) for all scenes. The second stage is to test and annotate keywords for test image data set. Using the visual features provided by Duygulu, experimental results show that our model outperforms probabilistic latent semantic analysis (PLSA) & GMM (PLSA&GMM) model on Corel5K database.
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Goy, Annamaria, Diego Magro, Giovanna Petrone, Claudia Picardi, Marco Rovera, and Marino Segnan. "An Integrated Support to Collaborative Semantic Annotation." Advances in Human-Computer Interaction 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7219098.
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Everybody experiences every day the need to manage a huge amount of heterogeneous shared resources, causing information overload and fragmentation problems. Collaborative annotation tools are the most common way to address these issues, but collaboratively tagging resources is usually perceived as a boring and time consuming activity and a possible source of conflicts. To face this challenge, collaborative systems should effectively support users in the resource annotation activity and in the definition of a shared view. The main contribution of this paper is the presentation and the evaluation of a set of mechanisms (personal annotations over shared resources and tag suggestions) that provide users with the mentioned support. The goal of the evaluation was to (1) assess the improvement with respect to the situation without support; (2) evaluate the satisfaction of the users, with respect to both the final choice of annotations and possible conflicts; (3) evaluate the usefulness of the support mechanisms in terms of actual usage and user perception. The experiment consisted in a simulated collaborative work scenario, where small groups of users annotated a few resources and then answered a questionnaire. The evaluation results demonstrate that the proposed support mechanisms can reduce both overload and possible disagreement.
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Su, Yila, Huimin Li, and Fei Wang. "The Semantic Annotation Based on Mongolian Place Recognition." Journal of Software 10, no. 5 (2015): 616–27. http://dx.doi.org/10.17706/jsw.10.5.616-627.
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