Matches in ScholarlyData for { ?s ?p ?o. }
- 48 creator milan-stankovic.
- 48 creator philippe-laublet.
- 48 type InProceedings.
- 48 label "Linked Data-based Concept Recommendation: Comparison of Different Methods in Open Innovation Scenario".
- 48 sameAs 48.
- 48 abstract "Concept recommendation is a widely used technique aimed to assist users to chose the right tags, improve their Web search experience and a multitude of other tasks. In finding potential problem solvers in Open Innovation (OI) scenarios, the concept recommendation is of a crucial importance as it can help to discover the right topics, directly or laterally related to an innovation problem. Such topics then could be used to identify relevant experts. In this paper, we propose two Linked Data-based concept recommendation methods for topic discovery. The first one – called hyProximity - exploits only the particularities of Linked Data structures, while the other one applies a well-known Information Retrieval method – called Random Indexing - to the linked data. We compare the performance of the two methods against the baseline in the gold standard-based and user study-based evaluations, using the real problems and solutions from an OI company.".
- 48 hasAuthorList authorList.
- 48 isPartOf proceedings.
- 48 keyword "Concept Discovery".
- 48 keyword "Linked Data".
- 48 keyword "Open Innovation".
- 48 keyword "Recommender Systems".
- 48 title "Linked Data-based Concept Recommendation: Comparison of Different Methods in Open Innovation Scenario".
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- item-1 hasContent danica-damljanovic.
- item-1 next item-2.
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- item-2 hasContent milan-stankovic.
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- item-3 hasContent philippe-laublet.
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- 49 creator matthew-rowe.
- 49 creator milan-stankovic.
- 49 creator philippe-laublet.
- 49 type InProceedings.
- 49 label "Finding Co-solvers on Twitter, with a Little Help from Linked Data".
- 49 sameAs 49.
- 49 abstract "Finding collaborators that can complement one’s competence in collaborative research and innovation projects has become important with the advent of multidisciplinary challenges and collaborative R&D in general. In this paper we propose a method for suggesting potential candidates for collaborative solving of innovation challenges online, based on their competence, similarity of interest and social proximity with the user receiving the recommendations. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com.".
- 49 hasAuthorList authorList.
- 49 isPartOf proceedings.
- 49 keyword "Co-solver recommendation".
- 49 keyword "Linked Data".
- 49 keyword "Recommender Systems".
- 49 title "Finding Co-solvers on Twitter, with a Little Help from Linked Data".
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- item-1 hasContent milan-stankovic.
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- item-2 hasContent matthew-rowe.
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- item-3 hasContent philippe-laublet.
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- 6 creator jose-borbinha.
- 6 creator nuno-freire.
- 6 creator pavel-calado.
- 6 type InProceedings.
- 6 label "An approach for Named Entity Recognition in Poorly Structured Data".
- 6 sameAs 6.
- 6 abstract "This paper describes an approach for the task of named entity recognition in structured data containing free text as the values of its elements. We studied the recognition of the entity types of person, location and organization in bibliographic data sets from a concrete wide digital library initiative. Our ap-proach is based on conditional random fields models, using features designed to perform named entity recognition in the absence of strong lexical evidence, and exploiting the semantic context given by the data structure. The evaluation results support that, with the specialized features, named entity recognition can be done in free text in structured data with an acceptable accuracy. Our approach was able to achieve a maximum precision of 0.91 at 0.55 recall and a maximum recall of 0.82 at 0.77 precision. The achieved results were always higher than those obtained with Stanford Named Entity Recognizer, which was developed for well-structured text. We believe this level of quality in named entity recognition allows the use of this approach to support a wide range of information extraction applications in structured data.".
- 6 hasAuthorList authorList.
- 6 isPartOf proceedings.
- 6 keyword "conditional random fields".
- 6 keyword "metadata".
- 6 keyword "named entity recognition".
- 6 keyword "structured data".
- 6 title "An approach for Named Entity Recognition in Poorly Structured Data".
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- item-1 type ListItem.
- item-1 hasContent nuno-freire.
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- item-2 hasContent jose-borbinha.
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- item-3 hasContent pavel-calado.
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- 60 creator david-carral-martinez.
- 60 creator pascal-hitzler.
- 60 type InProceedings.
- 60 label "Extending Description Logic Rules".
- 60 sameAs 60.
- 60 abstract "Description Logics -- the logic underpinning the Web Ontology Language OWL -- and rules are currently the most prominent paradigms used for modeling knowledge for the Semantic Web. While both of these approaches are based on classical logic, the paradigms also differ significantly, so that naive combinations result in undesirable properties such as undecidability. Recent work has shown that many rules can in fact be expressed in OWL. In this paper we extend this work to include some types of rules previously excluded. We formally define a set of first order logic rules, C-Rules, which can be expressed within OWL extended with role conjunction. We also show that the use of nominal schemas results in even broader coverage.".
- 60 hasAuthorList authorList.
- 60 isPartOf proceedings.
- 60 keyword "Description Logics".
- 60 keyword "Nominal Schemas".
- 60 keyword "OWL".
- 60 keyword "Role Conjunction".