Matches in ScholarlyData for { ?s ?p ?o. }
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- 152 creator guenter-ladwig.
- 152 creator thanh-tran.
- 152 type InProceedings.
- 152 label "Linked Data Query Processing Strategies".
- 152 sameAs 152.
- 152 abstract "Recently, processing of queries on linked data has gained attention. We identify and systematically discuss three main strategies: a bottom-up strategy that discovers new sources during query processing by following links between sources, a top-down strategy that relies on complete knowledge about the sources to select and process relevant sources, and a mixed strategy that assumes some incomplete knowledge and discovers new sources at run-time. To exploit knowledge discovered at run-time, we propose an additional step, explicitly scheduled during query processing, called correct source ranking. Additionally, we propose the adoption of stream-based query processing to deal with the unpredictable nature of data access in the distributed Linked Data environment. In experiments, we show that our implementation of the mixed strategy leads to early reporting of results and thus, more responsive query processing, while not requiring complete knowledge.".
- 152 hasAuthorList authorList.
- 152 isPartOf proceedings.
- 152 keyword "semantic web".
- 152 title "Linked Data Query Processing Strategies".
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- 153 creator chen-wang.
- 153 creator lei-zhang.
- 153 creator yuan-ni.
- 153 creator zhaoming-qiu.
- 153 type InProceedings.
- 153 label "Enhancing the Open-Domain Classification of Named Entity using Linked Open Data".
- 153 sameAs 153.
- 153 abstract "Many applications make use of named entity classification. Machine learning is the preferred technique adopted for many named entity classification methods where the choice of features is critical to final performance. Existing approaches explore only the features derived from the characteristic of the named entity itself or its linguistic context. With the development of the SemanticWeb, a large number of data sources are published and connected across the Web as Linked Open Data (LOD). LOD provides rich a priori knowledge about entity type information, knowledge that can be a valuable asset when used in connection with named entity classification. In this paper, we explore the use of LOD to enhance named entity classification. Our method extracts information from LOD and builds a type knowledge base which is used to score a (named entity string, type) pair. This score is then injected as one or more features into the existing classifier in order to improve its performance. We conducted a thorough experimental study and report the results, which confirm the effectiveness of our proposed method.".
- 153 hasAuthorList authorList.
- 153 isPartOf proceedings.
- 153 keyword "classification".
- 153 keyword "linked open data".
- 153 keyword "named entity".
- 153 keyword "semantic web".
- 153 title "Enhancing the Open-Domain Classification of Named Entity using Linked Open Data".
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- item-1 hasContent yuan-ni.
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- item-2 hasContent lei-zhang.
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- 158 creator gerhard-friedrich.
- 158 creator kostyantyn-shchekotykhin.
- 158 type InProceedings.
- 158 label "Query strategy for sequential ontology debugging".
- 158 sameAs 158.
- 158 abstract "Debugging is an important prerequisite for the wide-spread application of ontologies, especially in areas that rely upon everyday users to create and maintain knowledge bases, such as the Semantic Web. Most recent approaches use diagnosis methods to identify sources of inconsistency. However, in most debugging cases these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. We exploit probabilities of typical user errors to formulate information theoretic concepts for query selection. Our evaluation showed that the suggested method reduces the number of required observations compared to myopic strategies.".
- 158 hasAuthorList authorList.
- 158 isPartOf proceedings.
- 158 keyword "semantic web".
- 158 title "Query strategy for sequential ontology debugging".
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- item-1 hasContent kostyantyn-shchekotykhin.
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- 167 creator hajo-rijgersberg.
- 167 creator jan-top.
- 167 creator mari-wigham.
- 167 creator mark-van-assem.
- 167 type InProceedings.
- 167 label "Converting and Annotating Quantitative Data Tables".
- 167 sameAs 167.
- 167 abstract "Companies, governmental agencies and scientists produce a large amount of quantitative (research) data, consisting of measurements ranging from e.g. the surface temperatures of an ocean to the viscosity of a sample of mayonnaise. Such measurements are stored in tables in e.g. spreadsheet files and research reports. To integrate and reuse such data, it is necessary to have a semantic description of the data. However, the notation used is often ambiguous, making automatic interpretation and conversion to rdf or other suitable format difficult. For example, the table header cell ``f (Hz)'' refers to frequency measured in Hertz, but the symbol ``f'' can also refer to the unit farad or the quantities force or luminous flux. Current annotation tools for this task either work on less ambiguous data or perform a more limited task. We introduce new disambiguation strategies based on an ont, which allows to improve performance on ``sloppy'' datasets not yet targeted by existing systems.".
- 167 hasAuthorList authorList.
- 167 isPartOf proceedings.
- 167 keyword "Linked Data".
- 167 keyword "RDF mapping".
- 167 keyword "data interaction".
- 167 keyword "data management".
- 167 keyword "semantic web".
- 167 title "Converting and Annotating Quantitative Data Tables".
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