Matches in ScholarlyData for { ?s ?p ?o. }
- 159 creator krzysztof-janowicz.
- 159 creator marc-wilkes.
- 159 type InProceedings.
- 159 label "SIM-DL_A: A Novel Semantic Similarity Measure for Description Logics Reducing Inter-Concept to Inter-Instance Similarity".
- 159 sameAs 159.
- 159 abstract "While semantic similarity plays a crucial role for human categorization and reasoning, computational similarity measures have also been applied to fields such as semantics-based information retrieval or ontology engineering. Several measures have been developed to compare concepts specified in various description logics. In most cases, these measures are either structural or require a populated ontology. Structural measures fail with an increasing expressivity of the used description logic, while several ontologies, e.g., geographic feature type ontologies, are not populated at all. In this paper, we present an approach to reduce inter-concept to inter-instance similarity and thereby avoid the canonization problem of structural measures. The novel approach, called SIM-DL_A, reuses existing similarity functions such as co-occurrence or network measures from our previous SIM-DL measure. The required instances for comparison are derived from the completion tree of a slightly modified DL-tableau algorithm as used for satisfiability checking. Instead of trying to find one (clash-free) model, the tableau algorithm generates a set of proxy individuals used for comparison. The paper presents the algorithm, alignment matrix, and similarity functions as well as a detailed example.".
- 159 hasAuthorList authorList.
- 159 hasTopic Data_mining.
- 159 hasTopic Ontology_%28computer_science%29.
- 159 hasTopic Ontology_alignment.
- 159 hasTopic Query.
- 159 hasTopic Search_engine.
- 159 hasTopic Semantic_Web.
- 159 hasTopic Visualization.
- 159 isPartOf proceedings.
- 159 keyword "description logics".
- 159 keyword "information retrieval".
- 159 keyword "semantic similarity".
- 159 title "SIM-DL_A: A Novel Semantic Similarity Measure for Description Logics Reducing Inter-Concept to Inter-Instance Similarity".
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- item-1 hasContent krzysztof-janowicz.
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- item-2 hasContent marc-wilkes.
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- 162 creator gianluca-demartini.
- 162 creator jakub-zakrzewski.
- 162 creator julien-gaugaz.
- 162 creator wolfgang-nejdl.
- 162 type InProceedings.
- 162 label "How to Trace and Revise Identities".
- 162 sameAs 162.
- 162 abstract "The Entity Name System is a service which aims at providing globally unique URIs for all kinds of real-world entities such as persons, locations and products, based on descriptions of these entities. Because entity descriptions available to the ENS for deciding on entity identity—Do two entity description refer to the same real-world entity?—are changing over time, the system sometimes has to revise its past decisions: One entity has been given two different URIs or two entities have been attributed the same URI. The question we have to investigate in this context is then: How do we propagate entity decision revisions to the clients, which make use of the URIs provided by the ENS? In this paper we propose a solution which relies on labelling the URIs with additional history information. These labels allows clients to locally detect deprecated URIs they are using and also merge IDs referring to the same real-world entity without needing to consult the ENS. Making update requests to the ENS for the detected URIs only considerably reduces the number of update requests, at the cost of a decrease in uniqueness quality. We investigate how much the number of update requests decreases using URI history labeling, as well as how this impacts the uniqueness of the URIs on the client. For the experiments we use both artificially generated entity revision histories as well as a real case study based on the revision history of the Dutch and Simple English Wikipedia.".
- 162 hasAuthorList authorList.
- 162 hasTopic Application.
- 162 hasTopic Interoperability.
- 162 hasTopic Knowledge_management.
- 162 hasTopic Query.
- 162 hasTopic Search_engine.
- 162 hasTopic Semantic_Web.
- 162 hasTopic Semantic_desktop.
- 162 hasTopic Visualization.
- 162 isPartOf proceedings.
- 162 keyword "Entity Name Service".
- 162 keyword "Identifiers Labelling".
- 162 keyword "Identifiers Lineage".
- 162 keyword "Uniqueness Quality".
- 162 title "How to Trace and Revise Identities".
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- item-1 hasContent julien-gaugaz.
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- item-2 hasContent jakub-zakrzewski.
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- item-3 hasContent gianluca-demartini.
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- item-4 hasContent wolfgang-nejdl.
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- 163 creator alasdair-j-g-gray.
- 163 creator iadh-ounis.
- 163 creator norman-gray.
- 163 type InProceedings.
- 163 label "Can RDB2RDF Tools Feasibly Expose Large Science Archives for Data Integration?".
- 163 sameAs 163.
- 163 abstract "Many science archive centres publish very large volumes of image, simulation, and experiment data. In order to integrate and analyse the available data, scientists need to be able to (i) identify and locate all the data relevant to their work; (ii) understand the multiple heterogeneous data models in which the data is published; and (iii) interpret and process the data they retrieve. RDF has been shown to be a generally successful framework within which to perform such data integration work. It can be equally successful in the context of scientific data, if it is demonstrably practical to expose that data as RDF. In this paper we investigate the capabilities of RDF to enable the integration of scientific data sources. Specifically, we discuss the suitability of SPARQL for expressing scientific queries, and the performance of several triple stores and RDB2RDF tools for executing queries over a moderately sized sample of a large astronomical data set. We found that more research and improvements are required into SPARQL and RDB2RDF tools to efficiently expose existing science archives for data integration.".
- 163 hasAuthorList authorList.
- 163 hasTopic Database_management_system.
- 163 hasTopic Performance_engineering.
- 163 hasTopic Scalability.
- 163 hasTopic Semantic_Web.
- 163 isPartOf proceedings.
- 163 keyword "RDB2RDF".
- 163 keyword "Semantic Integration".
- 163 title "Can RDB2RDF Tools Feasibly Expose Large Science Archives for Data Integration?".
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- item-1 hasContent alasdair-j-g-gray.
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