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- 143 sameAs 143.
- 143 abstract "Research in ontology learning from text has been mainly focused on entity recognition, taxonomy induction and relation extraction. In this work we approach a more challenging research issue, consisting in detecting semantic frames from texts, and using them to encode web ontologies. We exploit a new generation frame detection system, which is able to identify FrameNet frames and their argument boundaries from text parsing. In addition, we enrich frames with the results provided by a super-sense tagger, which extracts and classifies concepts and named entities from texts according to WordNet super-senses. The enrichment results include argument restrictions for the elements of a frame, and domain specializations, based on domain lexical unit detection for the target of a frame. The results are encoded according to the Lexical MetaModel, which allows a complete translation of lexical resources, keeps trace of the learning metadata, and enables custom transformations of enriched frames into modular ontology components, known as content ontology design patterns.".
- 143 hasAuthorList authorList.
- 143 hasTopic Data.
- 143 hasTopic Evaluation.
- 143 hasTopic Interoperability.
- 143 hasTopic Language_technology.
- 143 hasTopic Machine_learning.
- 143 hasTopic Natural_language_processing.
- 143 hasTopic Ontology_%28computer_science%29.
- 143 hasTopic Ontology_engineering.
- 143 hasTopic Semantic_Web.
- 143 isPartOf proceedings.
- 143 keyword "Frame Detection".
- 143 keyword "Lexical Ontologies".
- 143 keyword "NLP".
- 143 keyword "Ontology Design".
- 143 keyword "Ontology Learning".
- 143 title "Frame Detection over the Semantic Web".
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- item-1 hasContent bonaventura-coppola.
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- item-2 hasContent aldo-gangemi.
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- item-3 hasContent alfio-massimiliano-gliozzo.
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- item-4 hasContent davide-picca.
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- 147 creator andrew-branson.
- 147 creator jetendr-shamdasani.
- 147 creator mohammed-odeh.
- 147 creator peter-bloodsworth.
- 147 creator richard-mcclatchey.
- 147 creator tamas-hauer.
- 147 type InProceedings.
- 147 label "Semantic Matching using the UMLS".
- 147 sameAs 147.
- 147 abstract "Traditional ontology alignment techniques enable equivalence relationships to be established between concepts in two ontologies with some confidence value. With semantic matching, however, it is possible to identify not only equivalence relationships between concepts, but less general and more general relationships. This is beneficial since more expressive relationships can be discovered between ontologies thus helping us to resolve heterogeneity between differing semantic representations at a finer level of granularity. This work concerns the application of semantic matching to the medical domain. We have extended the SMatch algorithm to function in the medical domain with the use of the UMLS metathesaurus as the background resource, hence removing its previous reliance on WordNet, which does not cover the medical domain in a satisfactory manner. We describe the steps required to extend the SMatch algorithm to the medical domain for use with UMLS. We test the accuracy of our approach on subsets of the FMA and MeSH ontologies, with both precision and recall showing the accuracy and coverage of different versions of our algorithm on each dataset.".
- 147 hasAuthorList authorList.
- 147 hasTopic Ontology_%28computer_science%29.
- 147 hasTopic Ontology_alignment.
- 147 hasTopic Semantic_Web.
- 147 isPartOf proceedings.
- 147 keyword "UMLS".
- 147 keyword "ontology matching".
- 147 keyword "semantic matching".
- 147 title "Semantic Matching using the UMLS".
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- item-1 hasContent jetendr-shamdasani.
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- 15 creator guohui-xiao.
- 15 creator xiaowang-zhang.
- 15 creator zuoquan-lin.
- 15 type InProceedings.
- 15 label "A Tableau Algorithm for Handling Inconsistency in OWL".