LODeDeC: a framework for integration of entity relations from knowledge graphs

Sini Govindapillai; Lay-Ki Soon; Su-Cheng Haw

doi:10.1007/978-3-030-42835-8_17

LODeDeC: a framework for integration of entity relations from knowledge graphs

Sini Govindapillai, Lay-Ki Soon, Su-Cheng Haw

Malaysia Sch of Information Technology

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

Abstract

Large knowledge graphs (KGs), which are part of Linked Open Data (LOD) serve as the primary source for retrieving structured data in many Semantic Web applications. In order for machines to efficiently process the data for different data mining, entity linking and information retrieval tasks, it is always beneficial to have as many reliable facts from KGs as possible. But none of the KGs is complete on its own with respect to the number of relations describing an entity. Moreover, large KGs like DBpedia, YAGO and Wikidata appear similar in nature, but do not fully merge in terms relations of the entities from different domains. The complementary nature of different KGs can be utilized to expand the coverage of relations of the an entity. In order to achieve this, a framework for integration of entity information from different KGs using LOD, semantic similarity approaches and RDF reification is proposed in this paper.

Original language	English
Title of host publication	15th Asia Information Retrieval Societies Conference, AIRS 2019 Hong Kong, China, November 7–9, 2019 Proceedings
Editors	Fu Lee Wang, Haoran Xie, Wai Lam, Aixin Sun, Lun-Wei Ku, Tianyong Hao, Wei Chen, Tak-Lam Wong, Xiaohui Tao
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	195-201
Number of pages	7
ISBN (Electronic)	9783030428358
ISBN (Print)	9783030428341
DOIs	https://doi.org/10.1007/978-3-030-42835-8_17
Publication status	Published - 2020
Event	Asia Information Retrieval Symposium 2019 - Kowloon, Hong Kong Duration: 7 Nov 2019 → 9 Nov 2019 Conference number: 15th https://link.springer.com/book/10.1007/978-3-030-42835-8 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	12004
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Asia Information Retrieval Symposium 2019
Abbreviated title	AIRS 2019
Country/Territory	Hong Kong
City	Kowloon
Period	7/11/19 → 9/11/19
Internet address	https://link.springer.com/book/10.1007/978-3-030-42835-8 (Proceedings)

Keywords

Entity information integration
Knowledge Graphs
Linked Open Data

Access to Document

10.1007/978-3-030-42835-8_17

Cite this

Govindapillai, S., Soon, L.-K., & Haw, S.-C. (2020). LODeDeC: a framework for integration of entity relations from knowledge graphs. In F. L. Wang, H. Xie, W. Lam, A. Sun, L.-W. Ku, T. Hao, W. Chen, T.-L. Wong, & X. Tao (Eds.), 15th Asia Information Retrieval Societies Conference, AIRS 2019 Hong Kong, China, November 7–9, 2019 Proceedings (pp. 195-201). (Lecture Notes in Computer Science; Vol. 12004). Springer. https://doi.org/10.1007/978-3-030-42835-8_17

Govindapillai, Sini ; Soon, Lay-Ki ; Haw, Su-Cheng. / LODeDeC : a framework for integration of entity relations from knowledge graphs. 15th Asia Information Retrieval Societies Conference, AIRS 2019 Hong Kong, China, November 7–9, 2019 Proceedings. editor / Fu Lee Wang ; Haoran Xie ; Wai Lam ; Aixin Sun ; Lun-Wei Ku ; Tianyong Hao ; Wei Chen ; Tak-Lam Wong ; Xiaohui Tao. Cham Switzerland : Springer, 2020. pp. 195-201 (Lecture Notes in Computer Science).

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title = "LODeDeC: a framework for integration of entity relations from knowledge graphs",

abstract = "Large knowledge graphs (KGs), which are part of Linked Open Data (LOD) serve as the primary source for retrieving structured data in many Semantic Web applications. In order for machines to efficiently process the data for different data mining, entity linking and information retrieval tasks, it is always beneficial to have as many reliable facts from KGs as possible. But none of the KGs is complete on its own with respect to the number of relations describing an entity. Moreover, large KGs like DBpedia, YAGO and Wikidata appear similar in nature, but do not fully merge in terms relations of the entities from different domains. The complementary nature of different KGs can be utilized to expand the coverage of relations of the an entity. In order to achieve this, a framework for integration of entity information from different KGs using LOD, semantic similarity approaches and RDF reification is proposed in this paper.",

keywords = "Entity information integration, Knowledge Graphs, Linked Open Data",

author = "Sini Govindapillai and Lay-Ki Soon and Su-Cheng Haw",

note = "Funding Information: This work is partially funded by Fundamental Research Grant Scheme (FRGS) by Malaysia Ministry of Higher Education (Ref: FRGS/2/2013/ ICT07/MMU/02/2). Funding Information: Acknowledgements. This work is partially funded by Fundamental Research Grant Scheme (FRGS) by Malaysia Ministry of Higher Education (Ref: FRGS/2/2013/ ICT07/MMU/02/2). Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; Asia Information Retrieval Symposium 2019, AIRS 2019 ; Conference date: 07-11-2019 Through 09-11-2019",

year = "2020",

doi = "10.1007/978-3-030-42835-8_17",

language = "English",

isbn = "9783030428341",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "195--201",

editor = "Wang, {Fu Lee} and Haoran Xie and Wai Lam and Aixin Sun and Lun-Wei Ku and Tianyong Hao and Wei Chen and Tak-Lam Wong and Xiaohui Tao",

booktitle = "15th Asia Information Retrieval Societies Conference, AIRS 2019 Hong Kong, China, November 7–9, 2019 Proceedings",

url = "https://link.springer.com/book/10.1007/978-3-030-42835-8",

}

Govindapillai, S, Soon, L-K & Haw, S-C 2020, LODeDeC: a framework for integration of entity relations from knowledge graphs. in FL Wang, H Xie, W Lam, A Sun, L-W Ku, T Hao, W Chen, T-L Wong & X Tao (eds), 15th Asia Information Retrieval Societies Conference, AIRS 2019 Hong Kong, China, November 7–9, 2019 Proceedings. Lecture Notes in Computer Science, vol. 12004, Springer, Cham Switzerland, pp. 195-201, Asia Information Retrieval Symposium 2019, Kowloon, Hong Kong, 7/11/19. https://doi.org/10.1007/978-3-030-42835-8_17

LODeDeC: a framework for integration of entity relations from knowledge graphs. / Govindapillai, Sini; Soon, Lay-Ki; Haw, Su-Cheng.
15th Asia Information Retrieval Societies Conference, AIRS 2019 Hong Kong, China, November 7–9, 2019 Proceedings. ed. / Fu Lee Wang; Haoran Xie; Wai Lam; Aixin Sun; Lun-Wei Ku; Tianyong Hao; Wei Chen; Tak-Lam Wong; Xiaohui Tao. Cham Switzerland: Springer, 2020. p. 195-201 (Lecture Notes in Computer Science; Vol. 12004).

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

TY - GEN

T1 - LODeDeC

T2 - Asia Information Retrieval Symposium 2019

AU - Govindapillai, Sini

AU - Soon, Lay-Ki

AU - Haw, Su-Cheng

N1 - Conference code: 15th

PY - 2020

Y1 - 2020

N2 - Large knowledge graphs (KGs), which are part of Linked Open Data (LOD) serve as the primary source for retrieving structured data in many Semantic Web applications. In order for machines to efficiently process the data for different data mining, entity linking and information retrieval tasks, it is always beneficial to have as many reliable facts from KGs as possible. But none of the KGs is complete on its own with respect to the number of relations describing an entity. Moreover, large KGs like DBpedia, YAGO and Wikidata appear similar in nature, but do not fully merge in terms relations of the entities from different domains. The complementary nature of different KGs can be utilized to expand the coverage of relations of the an entity. In order to achieve this, a framework for integration of entity information from different KGs using LOD, semantic similarity approaches and RDF reification is proposed in this paper.

AB - Large knowledge graphs (KGs), which are part of Linked Open Data (LOD) serve as the primary source for retrieving structured data in many Semantic Web applications. In order for machines to efficiently process the data for different data mining, entity linking and information retrieval tasks, it is always beneficial to have as many reliable facts from KGs as possible. But none of the KGs is complete on its own with respect to the number of relations describing an entity. Moreover, large KGs like DBpedia, YAGO and Wikidata appear similar in nature, but do not fully merge in terms relations of the entities from different domains. The complementary nature of different KGs can be utilized to expand the coverage of relations of the an entity. In order to achieve this, a framework for integration of entity information from different KGs using LOD, semantic similarity approaches and RDF reification is proposed in this paper.

KW - Entity information integration

KW - Knowledge Graphs

KW - Linked Open Data

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U2 - 10.1007/978-3-030-42835-8_17

DO - 10.1007/978-3-030-42835-8_17

M3 - Conference Paper

AN - SCOPUS:85082390667

SN - 9783030428341

T3 - Lecture Notes in Computer Science

SP - 195

EP - 201

BT - 15th Asia Information Retrieval Societies Conference, AIRS 2019 Hong Kong, China, November 7–9, 2019 Proceedings

A2 - Wang, Fu Lee

A2 - Xie, Haoran

A2 - Lam, Wai

A2 - Sun, Aixin

A2 - Ku, Lun-Wei

A2 - Hao, Tianyong

A2 - Chen, Wei

A2 - Wong, Tak-Lam

A2 - Tao, Xiaohui

PB - Springer

CY - Cham Switzerland

Y2 - 7 November 2019 through 9 November 2019

ER -

Govindapillai S, Soon LK, Haw SC. LODeDeC: a framework for integration of entity relations from knowledge graphs. In Wang FL, Xie H, Lam W, Sun A, Ku LW, Hao T, Chen W, Wong TL, Tao X, editors, 15th Asia Information Retrieval Societies Conference, AIRS 2019 Hong Kong, China, November 7–9, 2019 Proceedings. Cham Switzerland: Springer. 2020. p. 195-201. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-42835-8_17

LODeDeC: a framework for integration of entity relations from knowledge graphs

Abstract

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Keywords

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