Detection of contextual identity links in a knowledge base

Joe Raad; Nathalie Pernelle; Fatiha Saïs

doi:10.1145/3148011.3148032

Detection of contextual identity links in a knowledge base

Joe Raad^*, Nathalie Pernelle, Fatiha Saïs

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Most of the Linked Data applications currently rely on the use of owl : SameAs for linking ontology instances. However, several studies have noticed multiple misuses of this identity link. These misuses, which are mainly caused by the lack of other well-defined linking alternatives, can lead to erroneous statements or inconsistencies. We propose in this paper a new contextual identity link: IdentiConTo that could serve as a replacement for owl : SameAs in linking identical instances in a specified context. To detect these contextual links, we have defined an algorithm named DECIDE that has been tested on scientific knowledge bases describing transformation processes.

Original language	English
Title of host publication	Proceedings of the Knowledge Capture Conference, K-CAP 2017
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9781450355537
DOIs	https://doi.org/10.1145/3148011.3148032
Publication status	Published - 4 Dec 2017
Externally published	Yes
Event	9th International Conference on Knowledge Capture, K-CAP 2017 - Austin, United States Duration: 4 Dec 2017 → 6 Dec 2017

Publication series

Name	Proceedings of the Knowledge Capture Conference, K-CAP 2017

Conference

Conference	9th International Conference on Knowledge Capture, K-CAP 2017
Country/Territory	United States
City	Austin
Period	4/12/17 → 6/12/17

Keywords

Context
Identity link discovery
Scientific data

Access to Document

10.1145/3148011.3148032

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Cite this

@inproceedings{cb3d2378257d42f690b1a2bd8ed9922b,

title = "Detection of contextual identity links in a knowledge base",

abstract = "Most of the Linked Data applications currently rely on the use of owl : SameAs for linking ontology instances. However, several studies have noticed multiple misuses of this identity link. These misuses, which are mainly caused by the lack of other well-defined linking alternatives, can lead to erroneous statements or inconsistencies. We propose in this paper a new contextual identity link: IdentiConTo that could serve as a replacement for owl : SameAs in linking identical instances in a specified context. To detect these contextual links, we have defined an algorithm named DECIDE that has been tested on scientific knowledge bases describing transformation processes.",

keywords = "Context, Identity link discovery, Scientific data",

author = "Joe Raad and Nathalie Pernelle and Fatiha Sa{\"i}s",

year = "2017",

month = dec,

day = "4",

doi = "10.1145/3148011.3148032",

language = "English",

series = "Proceedings of the Knowledge Capture Conference, K-CAP 2017",

publisher = "Association for Computing Machinery, Inc",

booktitle = "Proceedings of the Knowledge Capture Conference, K-CAP 2017",

note = "9th International Conference on Knowledge Capture, K-CAP 2017 ; Conference date: 04-12-2017 Through 06-12-2017",

}

Raad, J, Pernelle, N & Saïs, F 2017, Detection of contextual identity links in a knowledge base. in Proceedings of the Knowledge Capture Conference, K-CAP 2017., 8, Proceedings of the Knowledge Capture Conference, K-CAP 2017, Association for Computing Machinery, Inc, 9th International Conference on Knowledge Capture, K-CAP 2017, Austin, United States, 4/12/17. https://doi.org/10.1145/3148011.3148032

Detection of contextual identity links in a knowledge base. / Raad, Joe; Pernelle, Nathalie; Saïs, Fatiha.

Proceedings of the Knowledge Capture Conference, K-CAP 2017. Association for Computing Machinery, Inc, 2017. 8 (Proceedings of the Knowledge Capture Conference, K-CAP 2017).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Detection of contextual identity links in a knowledge base

AU - Raad, Joe

AU - Pernelle, Nathalie

AU - Saïs, Fatiha

PY - 2017/12/4

Y1 - 2017/12/4

N2 - Most of the Linked Data applications currently rely on the use of owl : SameAs for linking ontology instances. However, several studies have noticed multiple misuses of this identity link. These misuses, which are mainly caused by the lack of other well-defined linking alternatives, can lead to erroneous statements or inconsistencies. We propose in this paper a new contextual identity link: IdentiConTo that could serve as a replacement for owl : SameAs in linking identical instances in a specified context. To detect these contextual links, we have defined an algorithm named DECIDE that has been tested on scientific knowledge bases describing transformation processes.

AB - Most of the Linked Data applications currently rely on the use of owl : SameAs for linking ontology instances. However, several studies have noticed multiple misuses of this identity link. These misuses, which are mainly caused by the lack of other well-defined linking alternatives, can lead to erroneous statements or inconsistencies. We propose in this paper a new contextual identity link: IdentiConTo that could serve as a replacement for owl : SameAs in linking identical instances in a specified context. To detect these contextual links, we have defined an algorithm named DECIDE that has been tested on scientific knowledge bases describing transformation processes.

KW - Context

KW - Identity link discovery

KW - Scientific data

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U2 - 10.1145/3148011.3148032

DO - 10.1145/3148011.3148032

M3 - Conference contribution

AN - SCOPUS:85040625419

T3 - Proceedings of the Knowledge Capture Conference, K-CAP 2017

BT - Proceedings of the Knowledge Capture Conference, K-CAP 2017

PB - Association for Computing Machinery, Inc

T2 - 9th International Conference on Knowledge Capture, K-CAP 2017

Y2 - 4 December 2017 through 6 December 2017

ER -

Detection of contextual identity links in a knowledge base

Abstract

Publication series

Conference

Keywords

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