Zhishi.links Results for OAEI 2011

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Zhishi.links Results for OAEI 2011
Zhishi.links Results for OAEI 2011
Bibliographical Metadata
Subject: Ontology Alignment
Year: 2011
Authors: Xing Niu, Shu Rong, Yunlong Zhang, Haofen Wang
Venue OM
Content Metadata
Problem: Link Discovery
Approach: No data available now.
Implementation: Zhishi.links
Evaluation: Accuracy Evaluation

Abstract

This report presents the results of Zhishi.links, a distributed instance matching system, for this year’s Ontology Alignment Evaluation Initiative (OAEI) campaign. We participate in Data Interlinking track (DI) of IM@OAEI2011. In this report, we briefly describe the architecture and matching strategies of Zhishi.links, followed by an analysis of the results.

Conclusion

In this report, we have presented a brief description of Zhishi.links, an instance matching system. We have introduced the architecture of our system and specific techniques we used. Also, the results have been analyzed in detail and several guides for improvements have been proposed.

Future work

We look forwards to build an instance matching system with better performance and higher stability in the future.

Approach

Positive Aspects: No data available now.

Negative Aspects: No data available now.

Limitations: No data available now.

Challenges: – When it comes with the problem of homonyms, instance matching systems should exploit as much information as possible to enhance the discriminability of their matchers. Currently, subject to the fact that most descriptions given by New York Times are written in natural language, the performance of our semantic similarity calculator are constrained. We are considering more tests carrying out on datasets in different styles and designing a more robust system. – In DI track, only three types of resources are involved. The special words in names, which are extracted as values of characteristic properties, are chosen manually. Some smarter strategies should be applied to accomplish this mission.

Proposes Algorithm: No data available now.

Methodology: No data available now.

Requirements: No data available now.

Limitations: No data available now.

Implementations

Download-page: http://apex.sjtu.edu.cn/apex wiki/Zhishi.links

Access API: No data available now.

Information Representation: No data available now.

Data Catalogue: {{{Catalogue}}}

Runs on OS: No data available now.

Vendor: No data available now.

Uses Framework: No data available now.

Has Documentation URL: No data available now.

Programming Language: No data available now.

Version: No data available now.

Platform: No data available now.

Toolbox: No data available now.

GUI: No

Research Problem

Subproblem of: No data available now.

RelatedProblem: No data available now.

Motivation: No data available now.

Evaluation

Experiment Setup: Tests were carried out on a Hadoop computer cluster. Each node has a quad-core Intel Core 2 processor (4M Cache, 2.66 GHz), 8GB memory. The number of reduce tasks was set to 50.

Evaluation Method : Utilize distributed MapReduce framework to adopt index-based pre-matching

Hypothesis: No data available now.

Description: No data available now.

Dimensions: Accuracy

Benchmark used: DBpedia, Freebase, GeoNames

Results: No data available now.

Access APINo data available now. +
Event in seriesOM +
Has BenchmarkDBpedia +, Freebase + and GeoNames +
Has Challenges– When it comes with the problem of homony
– When it comes with the problem of homonyms, instance matching systems should exploit as much information as possible to enhance the discriminability of their matchers. Currently, subject to the fact that most descriptions given by New York Times are written in natural language, the performance of our semantic similarity calculator are constrained. We are considering more tests carrying out on datasets in different styles and designing a more robust system. – In DI track, only three types of resources are involved. The special words in names, which are extracted as values of characteristic properties, are chosen manually. Some smarter strategies should be applied to accomplish this mission.
uld be applied to accomplish this mission. +
Has DataCatalouge{{{Catalogue}}} +
Has DescriptionNo data available now. +
Has DimensionsAccuracy +
Has DocumentationURLhttp://No data available now. +
Has Downloadpagehttp://apex.sjtu.edu.cn/apex wiki/Zhishi.links +
Has EvaluationAccuracy Evaluation +
Has EvaluationMethodUtilize distributed MapReduce framework to adopt index-based pre-matching +
Has ExperimentSetupTests were carried out on a Hadoop computer cluster. Each node has a quad-core Intel Core 2 processor (4M Cache, 2.66 GHz), 8GB memory. The number of reduce tasks was set to 50. +
Has GUINo +
Has HypothesisNo data available now. +
Has ImplementationZhishi.links +
Has InfoRepresentationNo data available now. +
Has LimitationsNo data available now. +
Has NegativeAspectsNo data available now. +
Has PositiveAspectsNo data available now. +
Has RequirementsNo data available now. +
Has ResultsNo data available now. +
Has SubproblemNo data available now. +
Has VersionNo data available now. +
Has abstractThis report presents the results of Zhishi
This report presents the results of Zhishi.links, a distributed instance matching system, for this year’s Ontology Alignment Evaluation Initiative (OAEI) campaign. We participate in Data Interlinking track (DI) of IM@OAEI2011. In this report, we briefly describe the architecture and matching strategies of Zhishi.links, followed by an analysis of the results.
s, followed by an analysis of the results. +
Has approachNo data available now. +
Has authorsXing Niu +, Shu Rong +, Yunlong Zhang + and Haofen Wang +
Has conclusionIn this report, we have presented a brief
In this report, we have presented a brief description of Zhishi.links, an instance matching system. We have introduced the architecture of our system and specific techniques we used. Also, the results have been analyzed in detail and several guides for improvements have been proposed.
uides for improvements have been proposed. +
Has future workWe look forwards to build an instance matching system with better performance and higher stability in the future. +
Has motivationNo data available now. +
Has platformNo data available now. +
Has problemLink Discovery +
Has relatedProblemNo data available now. +
Has subjectOntology Alignment +
Has vendorNo data available now. +
Has year2011 +
ImplementedIn ProgLangNo data available now. +
Proposes AlgorithmNo data available now. +
RunsOn OSNo data available now. +
TitleZhishi.links Results for OAEI 2011 +
Uses FrameworkNo data available now. +
Uses MethodologyNo data available now. +
Uses ToolboxNo data available now. +