Matches in ScholarlyData for { ?s ?p ?o. }
- 1 hasTopic Query.
- 1 hasTopic Search_engine.
- 1 hasTopic Semantic_Web.
- 1 hasTopic Visualization.
- 1 isPartOf proceedings.
- 1 keyword "collaborative tagging".
- 1 keyword "folksonomy".
- 1 keyword "ontology".
- 1 title "An Integrated Approach to Extracting Ontological Structures from Folksonomies".
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- item-1 hasContent huairen-lin.
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- item-2 hasContent joseph-davis.
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- item-3 hasContent ying-zhou.
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- 101 creator christian-meilicke.
- 101 creator heiner-stuckenschmidt.
- 101 creator kai-eckert.
- 101 type InProceedings.
- 101 label "Improving Ontology Matching using Meta-level Learning".
- 101 sameAs 101.
- 101 abstract "Despite serious research efforts, automatic ontology matching still suffers from severe problems with respect to the quality of matching results. Existing matching systems trade-off precision and recall and have their specific strengths and weaknesses. This leads to problems when the right matcher for a given task has to be selected. In this paper, we present a method for improving matching results by not choosing a specific matcher but applying machine learning techniques on an ensemble of matchers. Hereby we learn rules for the correctness of a correspondence based on the output of different matchers and additional information about the nature of the elements to be matched, thus leveraging the weaknesses of an individual matcher. We show that our method always performs significantly better than the median of the matchers used and in most cases outperforms the best matcher with an optimal threshold for a given pair of ontologies. As a side product of our experiments, we discovered that the majority vote is a simple but powerful heuristic for combining matchers that almost reaches the quality of our learning results.".
- 101 hasAuthorList authorList.
- 101 hasTopic Ontology_%28computer_science%29.
- 101 hasTopic Ontology_alignment.
- 101 hasTopic Semantic_Web.
- 101 isPartOf proceedings.
- 101 keyword "Evaluation".
- 101 keyword "Machine Learning".
- 101 keyword "Ontology Matching".
- 101 title "Improving Ontology Matching using Meta-level Learning".
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- item-1 hasContent kai-eckert.
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- item-2 hasContent christian-meilicke.
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- item-3 hasContent heiner-stuckenschmidt.
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- 104 creator fausto-giunchiglia.
- 104 creator ilya-zaihrayeu.
- 104 creator uladzimir-kharkevich.
- 104 type InProceedings.
- 104 label "Concept Search".
- 104 sameAs 104.
- 104 abstract "In this paper we present a novel approach, called Concept Search, which extends syntactic search, i.e., search based on the computation of string similarity between words, with semantic search, i.e., search based on the computation of semantic relations among concepts. The key idea of Concept Search is to operate on complex concepts and to maximally exploit the semantic information available, reducing to syntactic search only when necessary, i.e., when no semantic information is available. The experimental results show that Concept Search performs at least as well as syntactic search, improving the quality of results as a function of the amount of available semantics.".
- 104 hasAuthorList authorList.
- 104 hasTopic Query.
- 104 hasTopic Search_engine.
- 104 hasTopic Semantic_Web.
- 104 hasTopic Visualization.
- 104 isPartOf proceedings.
- 104 keyword "Semantic Continuum".
- 104 keyword "Semantic Inverted Index".
- 104 keyword "Semantic Search".
- 104 title "Concept Search".
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- authorList hasFirstItem item-1.
- authorList hasItem item-1.
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- item-1 type ListItem.
- item-1 hasContent fausto-giunchiglia.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent uladzimir-kharkevich.
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- item-3 hasContent ilya-zaihrayeu.
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- 106 creator jose-iria.
- 106 type InProceedings.
- 106 label "A Core Ontology for Knowledge Acquisition".
- 106 sameAs 106.
- 106 abstract "Semantic descriptions of knowledge acquisition (KA) tools and resources enable machine reasoning about KA systems and can be used to automate the discovery and composition of KA services, thereby increasing interoperability among systems and reducing system design and maintenance costs. Whilst there are a few general-purpose ontologies available that could be combined for describing knowledge acquisition, albeit at an inadequate abstraction level, there is as yet no KA ontology based on Semantic Web technologies available. In this paper, we present OAK, a well-founded, modular, extensible and multimedia-aware ontology of knowledge acquisition which extends existing foundational and core Semantic Web ontologies. We start by using a KA tool development scenario to illustrate the complexity of the problem, and identify a number of requirements for OAK. After we present the ontology in detail, we evaluate it with respect to the identified requirements.".
- 106 hasAuthorList authorList.
- 106 hasTopic Annotation.
- 106 hasTopic Application.
- 106 hasTopic Data.
- 106 hasTopic Interoperability.