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- item-1 hasContent benedikt-kaempgen.
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- item-2 hasContent andreas-harth.
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- 205 creator amit-joshi.
- 205 creator guozhu-dong.
- 205 creator pascal-hitzler.
- 205 type InProceedings.
- 205 label "Logical Linked Data Compression".
- 205 sameAs 205.
- 205 abstract "Linked data has experienced accelerated growth in recent years. With the continuing proliferation of structured data, demand for RDF compression is becoming increasingly important. In this study, we introduce a novel lossless compression technique for RDF datasets, called Rule Based Compression (RB Compression) that compresses datasets by generating a set of new logical rules from the dataset and removing triples that can be inferred from these rules. Unlike other compression techniques, our approach not only takes advantage of syntactic verbosity and data redundancy but also utilizes semantic associations present in the RDF graph. Depending on the nature of the dataset, our system is able to prune more than 50% of the original triples without affecting data integrity.".
- 205 hasAuthorList authorList.
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- 205 keyword "compression".
- 205 keyword "rdf".
- 205 keyword "semantic web".
- 205 title "Logical Linked Data Compression".
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- item-1 hasContent amit-joshi.
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- item-3 hasContent guozhu-dong.
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- 206 creator anna-divoli.
- 206 creator anna-lan-huang.
- 206 creator ian-witten.
- 206 creator jeen-broekstra.
- 206 creator olena-medelyan.
- 206 creator steve-manion.
- 206 type InProceedings.
- 206 label "Constructing a Focused Taxonomy from a Document Collection".
- 206 sameAs 206.
- 206 abstract "We describe a new method for constructing custom taxonomies from document collections. It involves identifying relevant concepts and entities in text; linking them to knowledge sources like Wikipedia, DBpedia, Freebase, and any supplied taxonomies from related domains; disambiguating conflicting concept mappings; and selecting semantic relations that best group them hierarchically. An RDF model supports interoperability of these steps, and also provides a flexible way of including existing NLP tools and further knowledge sources. From 2000 news articles we construct a custom taxonomy with 10,000 concepts and 12,700 relations, similar in structure to manually created counterparts. Evaluation by 15 human judges shows the precision to be 89% and 90% for concepts and relations respectively; recall was 75% with respect to a manually generated taxonomy for the same domain.".
- 206 hasAuthorList authorList.
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- 206 keyword "DBpedia".
- 206 keyword "Freebase".
- 206 keyword "Linked Data".
- 206 keyword "concept disambiguation".
- 206 keyword "taxonomy induction".
- 206 title "Constructing a Focused Taxonomy from a Document Collection".
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- item-1 hasContent olena-medelyan.
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- item-2 hasContent steve-manion.
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- 207 creator lihua-zhao.
- 207 creator ryutaro-ichise.
- 207 type InProceedings.
- 207 label "Instance-based ontological knowledge acquisition".
- 207 sameAs 207.
- 207 abstract "The Linked Open Data (LOD) cloud contains tremendous amounts of interlinked instances, from where we can retrieve abundant knowledge. However, because of the heterogeneous and big ontologies, it is time consuming to learn all the ontologies manually and it is difficult to observe which properties are important for describing instances of a specific class. In order to construct an ontology that can help users easily access various data sets, we propose a semi-automatic ontology integration framework that can reduce the heterogeneity of ontologies and retrieve frequently used core properties for each class. The framework consists of three main components: graph-based ontology integration, machine learning based ontology schema extraction, and an ontology merger. By analyzing instances of the linked data sets, this framework acquires ontological knowledge and construct a high-quality integrated ontology, which is easily understandable and effective in knowledge acquisition from various data sets using simple SPARQL queries.".
- 207 hasAuthorList authorList.
- 207 isPartOf proceedings.
- 207 keyword "Semantic Web".
- 207 keyword "knowledge acquisition".
- 207 keyword "linked data".