Link analysis of life science linked data: The Semantic Web - ISWC 2015

W. Hu; H. Qiu; M. Dumontier

doi:10.1007/978-3-319-25010-6_29

Link analysis of life science linked data: The Semantic Web - ISWC 2015

W. Hu^*, H. Qiu, M. Dumontier

^*Corresponding author for this work

Research output: Contribution to journal › Conference article in journal › Academic › peer-review

Abstract

Semantic web technologies offer a promising mechanism for the representation and integration of thousands of biomedical databases. Many of these databases provide cross-references to other data sources, but they are generally incomplete and error-prone. In this paper, we conduct an empirical link analysis of the life science linked data, obtained from the bio2rdf project. Three different link graphs for datasets, entities and terms are characterized using degree distribution, connectivity, and clustering metrics, and their correlation is measured as well. Furthermore, we analyze the symmetry and transitivity of entity links to build a benchmark and preliminarily evaluate several entity matching methods. Our findings indicate that the life science data network can help identify hidden links, can be used to validate links, and may offer the mechanism to integrate a wider set of resources for biomedical knowledge discovery.

Original language	English
Pages (from-to)	446-462
Number of pages	17
Journal	Lecture Notes in Computer Science
Volume	9367
DOIs	https://doi.org/10.1007/978-3-319-25010-6_29
Publication status	Published - 2015
Externally published	Yes

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10.1007/978-3-319-25010-6_29

Cite this

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title = "Link analysis of life science linked data: The Semantic Web - ISWC 2015",

abstract = "Semantic web technologies offer a promising mechanism for the representation and integration of thousands of biomedical databases. Many of these databases provide cross-references to other data sources, but they are generally incomplete and error-prone. In this paper, we conduct an empirical link analysis of the life science linked data, obtained from the bio2rdf project. Three different link graphs for datasets, entities and terms are characterized using degree distribution, connectivity, and clustering metrics, and their correlation is measured as well. Furthermore, we analyze the symmetry and transitivity of entity links to build a benchmark and preliminarily evaluate several entity matching methods. Our findings indicate that the life science data network can help identify hidden links, can be used to validate links, and may offer the mechanism to integrate a wider set of resources for biomedical knowledge discovery.",

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T1 - Link analysis of life science linked data

T2 - The Semantic Web - ISWC 2015

AU - Hu, W.

AU - Qiu, H.

AU - Dumontier, M.

PY - 2015

Y1 - 2015

N2 - Semantic web technologies offer a promising mechanism for the representation and integration of thousands of biomedical databases. Many of these databases provide cross-references to other data sources, but they are generally incomplete and error-prone. In this paper, we conduct an empirical link analysis of the life science linked data, obtained from the bio2rdf project. Three different link graphs for datasets, entities and terms are characterized using degree distribution, connectivity, and clustering metrics, and their correlation is measured as well. Furthermore, we analyze the symmetry and transitivity of entity links to build a benchmark and preliminarily evaluate several entity matching methods. Our findings indicate that the life science data network can help identify hidden links, can be used to validate links, and may offer the mechanism to integrate a wider set of resources for biomedical knowledge discovery.

AB - Semantic web technologies offer a promising mechanism for the representation and integration of thousands of biomedical databases. Many of these databases provide cross-references to other data sources, but they are generally incomplete and error-prone. In this paper, we conduct an empirical link analysis of the life science linked data, obtained from the bio2rdf project. Three different link graphs for datasets, entities and terms are characterized using degree distribution, connectivity, and clustering metrics, and their correlation is measured as well. Furthermore, we analyze the symmetry and transitivity of entity links to build a benchmark and preliminarily evaluate several entity matching methods. Our findings indicate that the life science data network can help identify hidden links, can be used to validate links, and may offer the mechanism to integrate a wider set of resources for biomedical knowledge discovery.

U2 - 10.1007/978-3-319-25010-6_29

DO - 10.1007/978-3-319-25010-6_29

M3 - Conference article in journal

SN - 0302-9743

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SP - 446

EP - 462

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

ER -