Matches in ScholarlyData for { ?s ?p ?o. }
- 213 keyword "GrAF, PAULA, ANNIS-QL".
- 213 keyword "corpus querying with SPARQL".
- 213 keyword "data modeling with OWL/DL".
- 213 keyword "interoperability of linguistic annotations".
- 213 title "POWLA: Modeling linguistic corpora in OWL/DL".
- authorList type List.
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- item-1 type ListItem.
- item-1 hasContent christian-chiarcos.
- 218 creator amal-zouaq.
- 218 creator dragan-gasevic.
- 218 creator marek-hatala.
- 218 type InProceedings.
- 218 label "Voting Theory for Concept Detection".
- 218 sameAs 218.
- 218 abstract "This paper explores the issue of detecting concepts for ontology learning from text. We investigate various metrics from graph theory and propose various voting schemes based on these metrics. The idea draws its root in social choice theory, and our objective is to mimic consensus in automatic learning methods and increase the confidence in concept extraction through the identification of the best performing metrics, the comparison of these metrics with standard information retrieval metrics (such as TF-IDF) and the evaluation of various voting schemes. Our results show that three graph-based metrics Degree, Reachability and HITS-hub were the most successful in identifying relevant concepts contained in two gold standard ontologies.".
- 218 hasAuthorList authorList.
- 218 isPartOf proceedings.
- 218 keyword "concepts".
- 218 keyword "machine learning".
- 218 keyword "ontology".
- 218 keyword "voting theory".
- 218 title "Voting Theory for Concept Detection".
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- item-1 hasContent amal-zouaq.
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- item-2 type ListItem.
- item-2 hasContent dragan-gasevic.
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- item-3 type ListItem.
- item-3 hasContent marek-hatala.
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- 231 creator blaz-fortuna.
- 231 creator dunja-mladenic.
- 231 creator lorand-dali.
- 231 creator thanh-tran.
- 231 type InProceedings.
- 231 label "Learning the Query-Independent Ranking of RDF Entity Search Results".
- 231 sameAs 231.
- 231 abstract "The amount of structured data is growing rapidly. Given a structured query that asks for some entities, the number of matching candidate results is often very high. The problem of ranking these results has gained attention. Because results in this setting equally and perfectly match the query, existing ranking approaches often use features that are independent of the query. A popular one is based on the notion of centrality that is derived via PageRank. In this paper, we adopt learning to rank approach to this structured query setting, provide a systematic categorization of query-independent features that can be used for that, and finally, discuss how to leverage information in access logs to automatically derive the training data needed for learning. In experiments using real-world datasets and human evaluation based on crowd sourcing, we show the superior performance of our approach over two relevant baselines.".
- 231 hasAuthorList authorList.
- 231 isPartOf proceedings.
- 231 keyword "information retrieval".
- 231 keyword "learning to rank".
- 231 keyword "semantic search".
- 231 title "Learning the Query-Independent Ranking of RDF Entity Search Results".
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- item-1 type ListItem.
- item-1 hasContent lorand-dali.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent blaz-fortuna.
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- item-3 hasContent thanh-tran.
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- item-4 hasContent dunja-mladenic.
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- 24 creator adil-el-ghali.
- 24 creator diego-berrueta.
- 24 creator emilio-rubiera.
- 24 creator luis-polo.
- 24 type InProceedings.
- 24 label "TELIX: An RDF-based Model for Linguistic Annotation".
- 24 sameAs 24.
- 24 abstract "This paper proposes to apply the RDF framework to the representation of linguistic annotations. We argue that RDF is a suitable data model to capture multiple annotations on the same text segment, and to integrate multiple layers of annotations. Besides the idea of using RDF for this purpose, the main contribution of the paper is an OWL ontology, called TELIX (Text Encoding and Linguistic Information eXchange), which models annotation content. This ontology builds on the SKOS~XL vocabulary, a W3C standard for lexical entities representation as RDF graphs. We extend SKOS in order to capture lexical relations between words (e.g., synonymy), as well as to support word sense disambiguation, morphological features and syntactic analysis, among others. Additionally, a formal mapping of feature structures to RDF graphs is defined, enabling complex composition of linguistic entities. Finally, the paper also suggests the use of RDFa as a convenient syntax that combines source texts and linguistic annotations in the same file.".
- 24 hasAuthorList authorList.
- 24 isPartOf proceedings.
- 24 keyword "Feature structures".
- 24 keyword "Linguistic annotations".
- 24 keyword "Linguistic knowledge".
- 24 keyword "OWL".
- 24 keyword "Ontology".
- 24 keyword "RDFa".
- 24 keyword "SKOS".
- 24 title "TELIX: An RDF-based Model for Linguistic Annotation".
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