Edit Paper: LogMap: Logic-based and Scalable Ontology Matching

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LogMap: Logic-based and Scalable Ontology Matching
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Abstract[edit]

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Approach[edit]

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Implementations[edit]

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Bibliographical Metadata
Content Metadata
Approach
Implementations
Research Problem
Evaluation

Paper Title*:		Full title of the given paper
Subject:		Primary subject or topic of research of the paper.
Author(s):
Publication venue:		Publication venue of the paper.
Year:		The year in which the paper is published.
Keywords:
Abstract:	In this paper, we present LogMap, a highly scalable ontology matching system with built-in reasoning and diagnosis capabilities. To the best of our knowledge, LogMap is the only matching system that can deal with semantically rich ontologies containing tens (and even hun-dreds) of thousands of classes. In contrast to most existing tools, LogMap also implements algorithms for on the fly unsatisability detection and repair. Our experiments with the ontologies NCI, FMA and SNOMEDCT confirm that our system can efficiently match even the largest existing bio-medical ontologies. Furthermore, LogMap is able to produce a `clean' set of output mappings in many cases, in the sense that the ontology obtained by integrating LogMap's output mappings with the input ontologies is consistent and does not contain unsatisable classes.	Abstract of the paper.
Conclusion:	In this paper, we have presented LogMap, a highly scalable ontology matching tool with built-in reasoning and diagnosis capabilities. LogMap's features and scalability behaviour make it well-suited for matching large-scale ontologies. LogMap, however, is still an early-stage prototype and there is plenty of room for improvement.
Future work:	We are currently working on further optimizations, and in the near future, we are planning to integrate LogMap with a Protege-based front-end, such as the one implemented in our tool ContentMap.	Future ideas of the paper.

Problem:		The research problem.
Approach:		Approach used to achieve the goal.
Implementation:		The implementation/framework that is invoked when running.
Evaluation:		Proposed work Evaluation.

Positive Aspects:	No data available now.
Negative Aspects:	No data available now.
Limitations:	No data available now.
Challenges:	No data available now.
Proposes Algorithm:	No data available now.
Proposes Model:
Methodology:	No data available now.
Requirements:	No data available now.

Download-page:
Access API:
Information Representation:
Data Catalogue:
OS:
Vendor:		Organization: commercial, free or otherwise
Uses Framework:		Framework name
Documentation URL:		Link to Documentation webpage.
Programming Language:
Version:
Platform:
Toolbox:
GUI:		whether the implementation has a GUI?

Subproblem of:		A broader research problem.
Related Problem:		A another research problem.
Motivation:		The motivation to solve the problem.

Experiment setup:	No data available now.
Evaluation method :		A broader research problem.
Hypothesis:	No data available now.	A another research problem.
Description:	No data available now.	The motivation to solve the problem.
Dimensions:		Select evaluation dimension.
Benchmark:		Benchmark used for evaluation.
Results:	No data available now.

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