Domain ontology

Xem 1-18 trên 18 kết quả Domain ontology
  • Often, there is a need to use the knowledge from multiple ontologies. This is particularly the case within the context of medical imaging, where a single ontology is not enough to provide the complementary knowledge about anatomy, radiology and diseases that is required by the related applications. Consequently, semantic integration of these different but related types of medical knowledge that is present in disparate domain ontologies becomes necessary. Medical ontology alignment addresses this need by identifying the semantically equivalent concepts across multiple medical ontologies. ...

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  • In this paper, a domain specific ontology called Information Technology Ontology (ITO) is proposed. This ontology is built basing on three distinct sources of Wikipedia, WordNet and ACM Digital Library. An information extraction system focusing on computing domain based on this ontology in the future will be built.

    pdf10p dieutringuyen 07-06-2017 15 2   Download

  • We present a Korean question answering framework for restricted domains, called K-QARD. K-QARD is developed to achieve domain portability and robustness, and the framework is successfully applied to build question answering systems for several domains. Domain-dependent External Resources NL Question Training examples Domain ontology TE/TR rules Semantic frames Question Analysis.

    pdf4p hongvang_1 16-04-2013 27 1   Download

  • (bq) part 1 book “telecommunications networks – current status and future trends” has contents: access control solutions for next generation networks, power considerations for sensor networks, telecommunications service domain ontology - semantic interoperation foundation of intelligent integrated services,… and other contents.

    pdf222p tieu_vu15 07-09-2018 18 1   Download

  • An open-domain spoken dialog system has to deal with the challenge of lacking lexical as well as conceptual knowledge. As the real world is constantly changing, it is not possible to store all necessary knowledge beforehand. Therefore, this knowledge has to be acquired during the run time of the system, with the help of the out-of-vocabulary information of a speech recognizer. As every word can have various meanings depending on the context in which it is uttered, additional context information is taken into account, when searching for the meaning of such a word.

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  • In this paper, participants in the Translational Medicine task force of the World Wide Web Consortium’s Health Care and Life Sciences Interest Group (W3C HCLSIG) present the Translational Medicine Ontology (TMO) and the Translational Medicine Knowledge Base (TMKB). The TMKB consists of the TMO, mappings to other terminologies and ontologies, and data in RDF format spanning discovery research and drug development, which are of therapeutic relevance to clinical research and clinical practice.

    pdf21p thangbienthai 17-11-2012 47 1   Download

  • Thesauri and ontologies provide important value in facilitating access to digital archives by representing underlying principles of organization. Translation of such resources into multiple languages is an important component for providing multilingual access. However, the specificity of vocabulary terms in most ontologies precludes fully-automated machine translation using general-domain lexical resources. In this paper, we present an efficient process for leveraging human translations when constructing domain-specific lexical resources.

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  • The comprehensive diagnosis ontology database, including the knowledge domains of fault, collection and structure, is established. And the relationships among the ontology are described.

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  • The ARIES methodology is based on explicit conceptualizations (ontologies: Villa, Athanasiadis et al. 2009) that lay out first of all a novel vision of ES, based on the breakdown into individual benefits, each of which is modeled independently, then linked to the others. Domain ontologies in ARIES result from a large-scale expert consensus. Artificial intelligence techniques (machine reasoning, pattern recognition) examine source data and extract from the ontologies models that best represent the situation at hand.

    pdf8p connhobinh 07-12-2012 33 3   Download

  • This paper presents a novel information system integrating advanced information extraction technology and automatic hyper-linking. Extracted entities are mapped into a domain ontology that relates concepts to a selection of hyperlinks. For information extraction, we use SProUT, a generic platform for the development and use of multilingual text processing components. By combining finite-state and unification-based formalisms, the grammar formalism used in SProUT offers both processing efficiency and a high degree of decalrativeness. ...

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  • ontological mode, wherever there are what might be called `modified objects' to which the modified meanings refer. And indeed, according to Meinong and Witasek, such is the case in the domain of `presentations' and other psychic phenomena. That which I experience when I `see' the sheriff on the screen is not strictly speaking, a presentation at all, for when I present to myself the sheriff in the throes of death, there is no (existing) object which is presented to me (and here it is irrelevant whether a certain person - an actor - was involved at an early stage...

    pdf146p dangsuynghi 27-03-2013 45 3   Download

  • Both a classified content and unstructured data view are valid and necessary. Organizational principles are the domain of librarians creating classifications and ontologies including the semantic web versus machine learning approaches to self-organize content. However, with large quantities of information, users are not just unwilling to classify, but are in fact unable to do it. Special skills are required to construct useful classifications. The first impulse we and others have felt is to just wish away the usefulness of metadata and hierarchies.

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  • Resolving polysemy and synonymy is required for high-quality information extraction. We present ConceptResolver, a component for the Never-Ending Language Learner (NELL) (Carlson et al., 2010) that handles both phenomena by identifying the latent concepts that noun phrases refer to. ConceptResolver performs both word sense induction and synonym resolution on relations extracted from text using an ontology and a small amount of labeled data. Domain knowledge (the ontology) guides concept creation by defining a set of possible semantic types for concepts.

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  • This paper presents a hybrid approach to question answering in the clinical domain that combines techniques from summarization and information retrieval. We tackle a frequently-occurring class of questions that takes the form “What is the best drug treatment for X?” Starting from an initial set of MEDLINE citations, our system first identifies the drugs under study. Abstracts are then clustered using semantic classes from the UMLS ontology. Finally, a short extractive summary is generated for each abstract to populate the clusters. ...

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  • The main aim of the MIMA (Mining Information for Management and Acquisition) Search System is to achieve ‘structuring knowledge’ to accelerate knowledge exploitation in the domains of science and technology. This system integrates natural language processing including ontology development, information retrieval, visualization, and database technology. The ‘structuring knowledge’ that we define indicates 1) knowledge storage, 2) (hierarchical) classification of knowledge, 3) analysis of knowledge, 4) visualization of knowledge.

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  • A semantics of linguistic categories like tense, aspect, and certain temporal adverbials, and a theory of their use in defining the temporal relations of events, both require a more complex structure on the domain underlying the meaning representations than is commonly assumed. The paper proposes an ontology based on such notions as causation and consequence, rather than on purely temporal primitives.

    pdf7p bungio_1 03-05-2013 41 1   Download

  • GOD (General Ontology Discovery) is an unsupervised system to extract semantic relations among domain specific entities and concepts from texts. Operationally, it acts as a search engine returning a set of true predicates regarding the query instead of the usual ranked list of relevant documents. Our approach relies on two basic assumptions: (i) paradigmatic relations can be established only among terms in the same Semantic Domain an (ii) they can be inferred from texts by analyzing the Subject-Verb-Object patterns where two domain specific terms co-occur. ...

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  • A regular user of a semantic search system frequently posses no knowledge about the SPARQL language nor about the ontology of a given knowledge base, especially when it provides domain-unspecific data obtained from heterogeneous sources. Nevertheless, he/she should be provided with tools enabling both intuitive and effective exploration of RDF-compliant knowledge bases.

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