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- 222 creator ansgar-scherp.
- 222 creator johann-schaible.
- 222 creator pedro-szekely.
- 222 type InProceedings.
- 222 label "Comparing Vocabulary Term Recommendations using Association Rules and Learning To Rank: A User Study".
- 222 sameAs 222.
- 222 abstract "When modeling Linked Open Data (LOD), choosing appropriate vocabulary terms to represent data entities and relations between data entities is difficult, because there are many vocabularies to choose from. Inappropriate choices lead to LOD that is difficult both to understand for humans as well as to automatically exploit by machines. We present an evaluation of approaches that try to alleviate this situation by recommending vocabulary terms based on how other data providers have used RDF classes and properties in the LOD cloud. Our user study compares the machine learning technique Learning to Rank (L2R), the classical data mining approach Association Rule mining (AR), and a baseline that does not provide any recommendations. Results show that utilizing AR, participants needed less time and less effort to model the data, which in the end resulted to be of better quality.".
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- 222 title "Comparing Vocabulary Term Recommendations using Association Rules and Learning To Rank: A User Study".
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- 224 creator michelle-cheatham.
- 224 creator pawel-grzebala.
- 224 type InProceedings.
- 224 label "A Comparison of Techniques for Name-based Private Record Linkage".
- 224 sameAs 224.
- 224 abstract "The rise of Big Data Analytics has shown the utility of analyzing all aspects of a problem by bringing together disparate data sets. Efficient and accurate private record linkage algorithms are necessary to achieve this. However, records are often linked based on personally identifiable information, and protecting the privacy of individuals is critical. This paper contributes to this field by studying an important component of the private record linkage problem: linking based on names while keeping those names encrypted, both on disk and in memory. We explore the applicability, accuracy and speed of three different primary approaches to this problem (along with several variations) and compare the results to common name-matching metrics on unprotected data. While these approaches are not new, this paper provides a thorough analysis on a range of datasets containing systematically introduced flaws common to name-based data entry, such as typographical errors, optical character recognition errors, and phonetic errors.".
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- 224 title "A Comparison of Techniques for Name-based Private Record Linkage".
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- 227 creator agnieszka-lawrynowicz.
- 227 creator c-maria-keet.
- 227 type InProceedings.
- 227 label "Test-Driven Development of Ontologies".
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- 227 abstract "Emerging ontology authoring methods to add knowledge to an ontology focus on ameliorating the validation bottleneck. The verification of the newly added axiom is still one of trying and seeing what the reasoner says, because a systematic testbed for ontology authoring is missing. We sought to address this by introducing the approach of test-driven development for ontology authoring. We specify 36 generic tests, as TBox queries and TBox axioms tested through individuals, and structure their inner workings in an `open box'-way, which cover the OWL 2 DL language features. This is implemented as a Protege plugin so that one can perform a TDD test as a black box test. We evaluated the two test approaches on their performance. ".
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- 227 title "Test-Driven Development of Ontologies".
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- 230 creator amparo-e-cano.
- 230 creator enrico-motta.
- 230 creator harith-alani.
- 230 creator hassan-saif.
- 230 type InProceedings.
- 230 label "Semantic Topic Compass – Classification based on Unsupervised Feature Ambiguity Gradation".
- 230 sameAs 230.
- 230 abstract "In this paper we introduce the Semantic Topic Compass (STC) Framework, which makes use of topic priors derived from structured knowledge sources to facilitate the semantic feature grading of a topic. The proposed STC characterises ambiguity in a topic's feature space. In this paper we investigate the impact of the use of ambiguous lexicons in a topic classification task. Our findings show promising results in understanding the impact of ambiguity in feature relevance assessment for topic classification. ".
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- 230 title "Semantic Topic Compass – Classification based on Unsupervised Feature Ambiguity Gradation".
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- item-1 hasContent amparo-e-cano.
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