Ontology approach

Xem 1-17 trên 17 kết quả Ontology approach
  • Automatic key phrase extraction is fundamental to the success of many recent digital library applications and semantic information retrieval techniques and a difficult and essential problem in Vietnamese natural language processing (NLP). In this work, we propose a novel method for key phrase extracting of Vietnamese text that exploits the Vietnamese Wikipedia as an ontology and exploits specific characteristics of the Vietnamese language for the key phrase selection stage.

    pdf4p hongphan_1 15-04-2013 20 5   Download

  • Extracting knowledge from unstructured text is a long-standing goal of NLP. Although learning approaches to many of its subtasks have been developed (e.g., parsing, taxonomy induction, information extraction), all end-to-end solutions to date require heavy supervision and/or manual engineering, limiting their scope and scalability. We present OntoUSP, a system that induces and populates a probabilistic ontology using only dependency-parsed text as input.

    pdf10p hongdo_1 12-04-2013 18 2   Download

  • 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 29 1   Download

  • Existing works on sentiment analysis on product reviews suffer from the following limitations: (1) The knowledge of hierarchical relationships of products attributes is not fully utilized. (2) Reviews or sentences mentioning several attributes associated with complicated sentiments are not dealt with very well. In this paper, we propose a novel HL-SOT approach to labeling a product’s attributes and their associated sentiments in product reviews by a Hierarchical Learning (HL) process with a defined Sentiment Ontology Tree (SOT). ...

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  • This paper describes a series of experiments to test the hypothesis that the parallel application of multiple NLP tools and the integration of their results improves the correctness and robustness of the resulting analysis. It is shown how annotations created by seven NLP tools are mapped onto toolindependent descriptions that are defined with reference to an ontology of linguistic annotations, and how a majority vote and ontological consistency constraints can be used to integrate multiple alternative analyses of the same token in a consistent way. ...

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  • Human categorization is neither a binary nor a context-free process. Rather, some concepts are better examples of a category than others, while the criteria for category membership may be satisfied to different degrees by different concepts in different contexts. In light of these empirical facts, WordNet’s static category structure appears both excessively rigid and unduly fragile for processing real texts. In this paper we describe a syntagmatic, corpus-based approach to redefining WordNet’s categories in a functional, gradable and context-sensitive fashion.

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  • This paper presents a novel algorithm for the acquisition of Information Extraction patterns. The approach makes the assumption that useful patterns will have similar meanings to those already identified as relevant. Patterns are compared using a variation of the standard vector space model in which information from an ontology is used to capture semantic similarity. Evaluation shows this algorithm performs well when compared with a previously reported document-centric approach.

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  • In this paper, we present a method for the semantic tagging of word chunks extracted from a written transcription of conversations. This work is part of an ongoing project for an information extraction system in the field of maritime Search And Rescue (SAR). Our purpose is to automatically annotate parts of texts with concepts from a SAR ontology. Our approach combines two knowledge sources a SAR ontology and the Wordsmyth dictionarythesaurus, and it uses a similarity measure for the classification.

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  • We present a weakly supervised approach to automatic Ontology Population from text and compare it with other two unsupervised approaches. In our experiments we populate a part of our ontology of Named Entities. We considered two high level categories - geographical locations and person names and ten sub-classes for each category. For each sub-class, from a list of training examples and a syntactically parsed corpus, we automatically learn a syntactic model - a set of weighted syntactic features, i.e.

    pdf8p bunthai_1 06-05-2013 18 1   Download

  • In the work of Baader and Distel, a method has been proposed to axiomatize all general concept inclusions (GCIs) expressible in the description logic ℰℒK and valid in a given interpretation ℐ. This provides us with an effective method to learn ℰℒK-ontologies from interpretations. In this work, we want to extend this approach in the direction of handling errors, which might be present in the data-set.

    pdf16p namdmcist 08-07-2015 10 2   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.

    pdf27p yasuyidol 02-04-2013 69 3   Download

  • A robust dictionary of semantic frames is an essential element of natural language understanding systems that use ontologies. However, creating lexical resources that accurately capture semantic representations en masse is a persistent problem. Where the sheer amount of content makes hand creation inefficient, computerized approaches often suffer from over generality and difficulty with sense disambiguation.

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  • Broad-coverage semantic annotations for training statistical learners are only available for a handful of languages. Previous approaches to cross-lingual transfer of semantic annotations have addressed this problem with encouraging results on a small scale. In this paper, we scale up previous efforts by using an automatic approach to semantic annotation that does not rely on a semantic ontology for the target language.

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  • Definition extraction is the task of automatically identifying definitional sentences within texts. The task has proven useful in many research areas including ontology learning, relation extraction and question answering. However, current approaches – mostly focused on lexicosyntactic patterns – suffer from both low recall and precision, as definitional sentences occur in highly variable syntactic structures.

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  • This paper presents a set of Bayesian methods for automatically extending the W ORD N ET ontology with new concepts and annotating existing concepts with generic property fields, or attributes. We base our approach on Latent Dirichlet Allocation and evaluate along two dimensions: (1) the precision of the ranked lists of attributes, and (2) the quality of the attribute assignments to W ORD N ET concepts. In all cases we find that the principled LDA-based approaches outperform previously proposed heuristic methods, greatly improving the specificity of attributes at each concept. ...

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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. ...

    pdf8p hongvang_1 16-04-2013 20 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. ...

    pdf4p bunthai_1 06-05-2013 17 1   Download


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