Matches in ScholarlyData for { ?s ?p ?o. }
- 206 creator feng-shi.
- 206 creator guotong-xie.
- 206 creator hanyu-li.
- 206 creator jie-tang.
- 206 creator juanzi-li.
- 206 type InProceedings.
- 206 label "Actively Learning Ontology Matching via User Interaction".
- 206 sameAs 206.
- 206 abstract "Ontology matching plays a key role for semantic interoperability. Many methods have been proposed for automatically finding the alignment between heterogeneous ontologies. However, in many real-world applications, finding the alignment in a completely automatic way is highly infeasible. Ideally, an ontology matching system would have an interactive interface to allow users to provide feedbacks to guide the automatic algorithm. Fundamentally, we need answer the following questions: how can a system perform an efficiently interactive process with the user? How many interactions are sufficient for finding a more accurate matching? To address these questions, we propose an active learning framework for ontology matching, which tries to find the most informative candidate matches to query the user. The users feedbacks are used to: 1) correct the mistake matching and 2) propagate the supervise information to help the entire matching process. Three measures are proposed to estimate the confidence of each matching candidate. A correct propagation algorithm is further proposed to maximize the spread of the users guidance. Experimental results on several public data sets show that the proposed approach can significantly improve the matching accuracy (+8.0% better than the baseline methods).".
- 206 hasAuthorList authorList.
- 206 hasTopic Semantic_Web.
- 206 isPartOf proceedings.
- 206 title "Actively Learning Ontology Matching via User Interaction".
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- item-1 hasContent feng-shi.
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- item-2 hasContent juanzi-li.
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- item-3 hasContent jie-tang.
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- 217 creator christoph-boehm.
- 217 creator philip-groth.
- 217 creator ulf-leser.
- 217 type InProceedings.
- 217 label "Graph-Based Ontology Construction from Heterogenous Evidences".
- 217 sameAs 217.
- 217 abstract "Ontologies are tools for describing and structuring knowledge, with many applications in searching and analyzing complex knowledge bases. Since building them manually is a costly process, there are various approaches for bootstrapping ontologies automatically through the analysis of appropriate documents. Such an analysis needs to find the concepts and the relationships that should form the ontology. However, since relationship extraction methods are imprecise and cannot homogeneously cover all concepts, the initial set of relationships is usually inconsistent and rather imbalanced - a problem which, to the best of our knowledge, was mostly ignored so far. In this paper, we define the problem of extracting a consistent as well as properly structured ontology from a set of inconsistent and heterogeneous relationships. Moreover, we propose and compare three graph-based methods for solving the ontology extraction problem. We extract relationships from a large-scale data set of more than 325K documents and evaluate our methods against a gold standard ontology comprising more than 12K relationships. Our study shows that an algorithm based on a modified formulation of the dominating set problem outperforms greedy methods.".
- 217 hasAuthorList authorList.
- 217 hasTopic Semantic_Web.
- 217 isPartOf proceedings.
- 217 title "Graph-Based Ontology Construction from Heterogenous Evidences".
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- item-1 hasContent christoph-boehm.
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- item-3 hasContent ulf-leser.
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- 219 creator aman-goel.
- 219 creator craig-knoblock.
- 219 creator jose-luis-ambite.
- 219 creator kristina-lerman.
- 219 creator rahul-parundekar.
- 219 creator sirish-darbha.
- 219 creator thomas-russ.
- 219 type InProceedings.
- 219 label "Automatically Constructing Semantic Web Services from Online Sources".
- 219 sameAs 219.
- 219 abstract "The work on integrating sources and services in the Semantic Web assumes that the data is either already represented in RDF or OWL or is available through a Semantic Web Service. In practice, there is a tremendous amount of data on the Web that is not available through the Semantic Web. In this paper we present an approach to automatically discover and create new Semantic Web Services. The idea behind this approach is to start with a set of known sources and the corresponding semantic descriptions and then discover similar sources, extract the source data, build semantic descriptions of the sources, and then turn them into Semantic Web Services. We implemented an end-to-end solution to this problem in a system called Deimos and evaluated the system across five different domains. The results demonstrate that the system can automatically discover, learn semantic descriptions, and build Semantic Web Services with only example sources and their descriptions as input.".
- 219 hasAuthorList authorList.
- 219 hasTopic Semantic_Web.
- 219 isPartOf proceedings.
- 219 title "Automatically Constructing Semantic Web Services from Online Sources".
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- item-1 hasContent jose-luis-ambite.
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