1. Articles in category: Semantic

    4009-4032 of 4095 « 1 2 ... 165 166 167 168 169 170 171 »
    1. A Within-Frame Ontological Extension on FrameNet: Application in Predicate Chain Analysis and Question Answering

      An ontological extension on the frames in FrameNet is presented in this paper. The general conceptual relations between frame elements, in conjunction with existing characteristics of this lexical resource, suggest more sophisticated semantic analysis of lexical chains (e.g. predicate chains) exploited in many text understanding applications. In particular, we have investigated its benefit for meaning-aware question answering when combined with an inference strategy. The proposed knowledge representation mechanism on the frame elements of FrameNet has been shown to have an impact on answering natural language questions on the basis of our case analysis. Content Type Book ChapterDOI 10.1007 ...
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    2. Using Clustering for Web Information Extraction

      This paper introduces an approach that achieves automated data extraction from semi-structured Web pages by clustering. Both HTML tags and the textual features of text tokens are considered for similarity comparison. The first clustering process groups similar text tokens into the same text clusters, and the second clustering process groups similar data tuples into tuple clusters. A tuple cluster is a strong candidate of a repetitive data region. Content Type Book ChapterDOI 10.1007/978-3-540-76928-6_43Authors Le Phong Bao Vuong, School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, PO Box 600, Wellington New ZealandXiaoying Gao, School of Mathematics ...
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    3. A Knowledge-Based Approach to Named Entity Disambiguation in News Articles

      Named entity disambiguation has been one of the main challenges to research in Information Extraction and development of Semantic Web. Therefore, it has attracted much research effort, with various methods introduced for different domains, scopes, and purposes. In this paper, we propose a new approach that is not limited to some entity classes and does not require well-structured texts. The novelty is that it exploits relations between co-occurring entities in a text as defined in a knowledge base for disambiguation. Combined with class weighting and coreference resolution, our knowledge-based method outperforms KIM system in this problem. Implemented algorithms and conducted ...
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    4. Learning Implicit User Interests Using Ontology and Search History for Personalization

      The key for providing a robust context for personalized information retrieval is to build a library which gathers the long term and the short term user’s interests and then using it in the retrieval process in order to deliver results that better meet the user’s information needs. In this paper, we present an enhanced approach for learning a semantic representation of the underlying user’s interests using the search history and a predefined ontology. The basic idea is to learn the user’s interests by collecting evidence from his search history and represent them conceptually using the concept ...
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    5. OntoGame: Towards Overcoming the Incentive Bottleneck in Ontology Building

      Despite significant advancement in ontology learning, building ontologies remains a task that highly depends on human intelligence, both as a source of domain expertise and for producing a consensual conceptualization. This means that individuals need to contribute time, and sometimes other resources, to an ontology project. Now, we can observe a sharp contrast in user interest in two branches of Web activity: While the “Web 2.0” movement lives from an unprecedented amount of contributions from Web users, we witness a substantial lack of user involvement in ontology projects for the Semantic Web. We assume that one cause of the ...
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    6. Automatic Annotation in Data Integration Systems

      CWSD (Combined Word Sense Disambiguation) is an algorithm for the automatic annotation of structured and semi-structured data sources. Instead of being targeted to textual data sources like most of the traditional WSD algorithms, CWSD can exploit knowledge from the structure of data sources together with the lexical knowledge associated with schema elements (terms in the following). We integrated CWSD in the MOMIS system (Mediator EnvirOment forMultiple Information Sources) [1], which is an framework designed for the integration of data sources, where the lexical annotation of terms was performed manually by the user. CWSD combines a structural disambiguation algorithm, that starts ...
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    7. Taxonomy Construction Using Compound Similarity Measure

      Taxonomy learning is one of the major steps in ontology learning process. Manual construction of taxonomies is a time-consuming and cumbersome task. Recently many researchers have focused on automatic taxonomy learning, but still quality of generated taxonomies is not satisfactory. In this paper we have proposed a new compound similarity measure. This measure is based on both knowledge poor and knowledge rich approaches to find word similarity. We also used Neural Network model for combination of several similarity methods. We have compared our method with simple syntactic similarity measure. Our measure considerably improves the precision and recall of automatic generated ...
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    8. Semantic Matching Based on Enterprise Ontologies

      Semantic Web technologies have in recent years started to also find their way into the world of commercial enterprises. Enterprise ontologies can be used as a basis for determining the relevance of information with respect to the enterprise. The interests of individuals can be expressed by means of the enterprise ontology. The main contribution of our approach is the integration of point set distance measures with a modified semantic distance measure for pair-wise concept distance calculation. Our combined measure can be used to determine the intra-ontological distance between sub-ontologies. Content Type Book ChapterDOI 10.1007/978-3-540-76848-7_76Authors Andreas Billig, Jönköping University ...
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      Mentions: Heidelberg
    9. Labeling Data Extracted from the Web

      We consider finding descriptive labels for anonymous, structured datasets, such as those produced by state-of-the-art Web wrappers. We give a probabilistic model to estimate the affinity between attributes and labels, and describe a method that uses a Web search engine to populate the model. We discuss a method for finding good candidate labels for unlabeled datasets. Ours is the first unsupervised labeling method that does not rely on mining the HTML pages containing the data. Experimental results with data from 8 different domains show that our methods achieve high accuracy even with very few search engine accesses. Content Type Book ...
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    10. Automatic Feeding of an Innovation Knowledge Base Using a Semantic Representation of Field Knowledge

      In this paper, by considering a particular application field, the innovation, we propose an automatic system to feed an innovation knowledge base (IKB) starting from texts located on the Web. To facilitate the extraction of concepts from texts we distinguished in our work two knowledge types: primitive knowledge and definite knowledge. Each one is separately represented. Primitive knowledge is directly extracted from natural language texts and temporally organized in a specific base called TKB (Temporary Knowledge Base). The entry of the base IKB is the knowledge filtered from the TKB by some specified rules. After each filtering step, the TKB ...
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      Mentions: Heidelberg
    11. Construction of trainable semantic vectors and clustering, classification, and searching using trainable semantic vectors

      An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types ...
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      Mentions: Eprom
    12. Two Types of Hierarchies in Geospatial Ontologies

      Geospatial ontologies contain hierarchical structures, which are either based on the taxonomy of entity classes or functions and roles these entities can take. While the taxonomic hierarchies can be extracted from noun phrases contained in the formal texts that describe the geospatial domain, the hierarchies of action concepts can be traced from the verb phrases. This paper reports a simple case study of extracting the two types of such hierarchies from formal texts of traffic code. Problems of concurrent use of both hierarchies for ontology reasoning are dis-cussed, particularly, in context of the different views on geospatial ontologies. An approach ...
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    13. Word Sense Disambiguation

      Word Sense Disambiguation Content Type BookPublisher Springer NetherlandsDOI 10.1007/978-1-4020-4809-8Copyright 2006ISBN 978-1-4020-4808-1 (Print) 978-1-4020-4809-8 (Online)Editors Eneko Agirre, University of the Basque Country Department of Computer Science Manuel de Lardizabal 1 E-20018 Donostia Basque Country SpainPhilip Edmonds, Oxford Science Park Sharp Laboratories of Europe Limited OX4 4GB Oxford UK Book Series Text, Speech and Language TechnologyPrint ISSN 1386-291X Book Series Volume Volume 33
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      Mentions: Eneko Agirre
    14. Evaluation of WSD Systems

      Evaluation of WSD Systems Content Type Book ChapterDOI 10.1007/978-1-4020-4809-8_4Authors Martha Palmer, University of Colorado Departments of Linguistics and Computer Science Hellems 295 80309 Boulder CO USAHwee Ng, National University of Singapore Department of Computer Science 3 Science Drive 2 117543 SingaporeHoa Dang, National Institute of Standards and Technology 100 Bureau Drive 8940 20899-8940 Gaithersburg MD USA Book Series Text, Speech and Language TechnologyPrint ISSN 1386-291X Book Series Volume Volume 33 Book Word Sense DisambiguationDOI 10.1007/978-1-4020-4809-8Online ISBN 978-1-4020-4809-8Print ISBN 978-1-4020-4808-1
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    15. The Same Antique Web

      It’s in the air. We’re flooded by catchy phrases announcing it. It’s all about semantics, AI and Web 3.0: “Web3 is closer than you think!” “You ain’t seen nothing yet!” “Web as artificial intelligence supplanting human race!” Some years ago, “you” were the superstar of Web 2.0 and its social networks. In the late ’90s, the dot-com boom had everything going [...]
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      Mentions: Google
    16. System for normalizing a discourse representation structure and normalized data structure

      The present invention is a system and method for normalizing a discourse representation structure (DRS). The elements of the structure are rewritten and sorted in a way such that structures which may appear different but are nonetheless equivalent can be associated with the same, normalized representation. The present invention can also include a data structure for a DRS. The DRS is represented by an array of boxes, each having a set of elements which in turn has a predefined structure suitable for representing a wide variety of linguistic information.
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    17. Media agent to suggest contextually related media content

      The described arrangements and procedures provide an intelligent media agent to autonomously collect semantic multimedia data text descriptions on behalf of a user whenever and wherever the user accesses media content. The media agent analyzes these semantic multimedia data text descriptions in view of user behavior patterns and actions to assist the user in identifying multimedia content and related information that is appropriate to the context within which the user is operating or working. For instance, the media agent detects insertion of text and analyzes the inserted text. Based on the analysis, the agent predicts whether a user intends to ...
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    18. The Need for a Prescriptive Ontology

      A great deal of effort is invested in universities, research labs and companies to create prescriptive ontologies. Just think about large-scale project such as Cyc/OpenCyc or smaller projects build around OWL. I use the term “prescriptive” to emphasize the fact that ontologies are usually defined in a hard-coded and formal manner. Let’s use the “Hotel” type, [...]
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    19. Topic modeling: syntactic versus semantic

      Topic modeling has turned into a bit of a cottage industry in the NLP/machine learning world. Most seems to stem from latent Dirichlet allocation, though this of course built on previous techniques; the most well-known of which is latent semantic analysis. At the end of the day, such "topic models" really look more like dimensionality reduction techniques (eg., the similarity to multinomial PCA); however, in practice, they're often used as (perhaps soft) clustering methods. Words are mapped to t
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      Mentions: UMass
    20. Natural Language Processing and Information Systems

      Natural Language Processing and Information Systems Content Type BookPublisher Springer Berlin / HeidelbergDOI 10.1007/978-3-540-73351-5Copyright 2007ISBN 978-3-540-73350-8Editors Zoubida KedadNadira LammariElisabeth MétaisFarid MezianeYacine Rezgui Book Series Lecture Notes in Computer ScienceOnline ISSN 1611-3349Print ISSN 0302-9743 Book Series Volume Volume 4592/2007
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