Ontology-Based Semantic Similarity Approach for Biomedical Dataset Retrieval

Xu Wang; Zhisheng Huang; Frank van Harmelen

doi:10.1007/978-3-030-61951-0_5

Ontology-Based Semantic Similarity Approach for Biomedical Dataset Retrieval

Xu Wang^*, Zhisheng Huang, Frank van Harmelen

^*Corresponding author for this work

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

180 Downloads (Pure)

Abstract

Ontology-based semantic similarity approaches play an important role in text-similarity task, thanks to its ability of explanation. Ontology-based semantic similarity approaches can explain how two terms are similar with help of rich knowledge in ontology. Information retrieval aims to find relevant information for given user query. As a subareas of information retrieval, dataset retrieval is an activity to find dataset which are relevant to an information need, by using full-text indexing approach or content-based indexing approach. Ontology-based semantic similarity approaches can not only do some information retrieval tasks, such as full-text mapping, but also finding deeper similar information with the help of knowledge-richness in ontology. Because of the advantage of ontology-based similarity approaches, we are looking forwards to find the possibility to using ontology-based similarity for datasets retrieval. In this paper, we provide an ontology-based similarity approach for dataset retrieval. We run our novel approach on the bioCADDIE 2016 Dataset Retrieval Challenge. After ruining experiments, we evaluate our results with several information retrieval evaluation measures. The evaluation results show that our approach could perform well.

Original language	English
Title of host publication	Health Information Science
Subtitle of host publication	9th International Conference, HIS 2020, Amsterdam, The Netherlands, October 20–23, 2020, Proceedings
Editors	Zhisheng Huang, Siuly Siuly, Hua Wang, Yanchun Zhang, Rui Zhou
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	49-60
Number of pages	12
ISBN (Electronic)	9783030619510
ISBN (Print)	9783030619503
DOIs	https://doi.org/10.1007/978-3-030-61951-0_5
Publication status	Published - 2020
Event	9th International Conference on Health Information Science, HIS 2020 - Amsterdam, Netherlands Duration: 20 Oct 2020 → 23 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer
Volume	12435 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Health Information Science, HIS 2020
Country/Territory	Netherlands
City	Amsterdam
Period	20/10/20 → 23/10/20

Funding

Acknowledgments. This work has been funded by the Netherlands Science Foundation NWO grant nr. 652.001.002, it is co-funded by Elsevier B.V., with funding for the first author by the China Scholarship Council (CSC) grant number 201807730060.

Keywords

Biomedical dataset
Dataset retrieval
Semantic similarity

Access to Document

10.1007/978-3-030-61951-0_5

Wang2020_Chapter_Ontology-BasedSemanticSimilarityApproachForBiomediacalDatasetRetrievalFinal published version, 657 KBLicence: Article 25fa Dutch Copyright Act

Persistent URL (handle)

Persistent link

Cite this

Wang, X., Huang, Z., & van Harmelen, F. (2020). Ontology-Based Semantic Similarity Approach for Biomedical Dataset Retrieval. In Z. Huang, S. Siuly, H. Wang, Y. Zhang, & R. Zhou (Eds.), Health Information Science: 9th International Conference, HIS 2020, Amsterdam, The Netherlands, October 20–23, 2020, Proceedings (pp. 49-60). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12435 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-61951-0_5

Wang, Xu ; Huang, Zhisheng ; van Harmelen, Frank. / Ontology-Based Semantic Similarity Approach for Biomedical Dataset Retrieval. Health Information Science: 9th International Conference, HIS 2020, Amsterdam, The Netherlands, October 20–23, 2020, Proceedings. editor / Zhisheng Huang ; Siuly Siuly ; Hua Wang ; Yanchun Zhang ; Rui Zhou. Springer Science and Business Media Deutschland GmbH, 2020. pp. 49-60 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e6f7b14b2b2643a696b9658a315e42e9,

title = "Ontology-Based Semantic Similarity Approach for Biomedical Dataset Retrieval",

abstract = "Ontology-based semantic similarity approaches play an important role in text-similarity task, thanks to its ability of explanation. Ontology-based semantic similarity approaches can explain how two terms are similar with help of rich knowledge in ontology. Information retrieval aims to find relevant information for given user query. As a subareas of information retrieval, dataset retrieval is an activity to find dataset which are relevant to an information need, by using full-text indexing approach or content-based indexing approach. Ontology-based semantic similarity approaches can not only do some information retrieval tasks, such as full-text mapping, but also finding deeper similar information with the help of knowledge-richness in ontology. Because of the advantage of ontology-based similarity approaches, we are looking forwards to find the possibility to using ontology-based similarity for datasets retrieval. In this paper, we provide an ontology-based similarity approach for dataset retrieval. We run our novel approach on the bioCADDIE 2016 Dataset Retrieval Challenge. After ruining experiments, we evaluate our results with several information retrieval evaluation measures. The evaluation results show that our approach could perform well.",

keywords = "Biomedical dataset, Dataset retrieval, Semantic similarity",

author = "Xu Wang and Zhisheng Huang and \{van Harmelen\}, Frank",

year = "2020",

doi = "10.1007/978-3-030-61951-0\_5",

language = "English",

isbn = "9783030619503",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "49--60",

editor = "Zhisheng Huang and Siuly Siuly and Hua Wang and Yanchun Zhang and Rui Zhou",

booktitle = "Health Information Science",

address = "Germany",

note = "9th International Conference on Health Information Science, HIS 2020 ; Conference date: 20-10-2020 Through 23-10-2020",

}

Wang, X, Huang, Z & van Harmelen, F 2020, Ontology-Based Semantic Similarity Approach for Biomedical Dataset Retrieval. in Z Huang, S Siuly, H Wang, Y Zhang & R Zhou (eds), Health Information Science: 9th International Conference, HIS 2020, Amsterdam, The Netherlands, October 20–23, 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12435 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 49-60, 9th International Conference on Health Information Science, HIS 2020, Amsterdam, Netherlands, 20/10/20. https://doi.org/10.1007/978-3-030-61951-0_5

Ontology-Based Semantic Similarity Approach for Biomedical Dataset Retrieval. / Wang, Xu; Huang, Zhisheng; van Harmelen, Frank.
Health Information Science: 9th International Conference, HIS 2020, Amsterdam, The Netherlands, October 20–23, 2020, Proceedings. ed. / Zhisheng Huang; Siuly Siuly; Hua Wang; Yanchun Zhang; Rui Zhou. Springer Science and Business Media Deutschland GmbH, 2020. p. 49-60 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12435 LNCS).

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

TY - GEN

T1 - Ontology-Based Semantic Similarity Approach for Biomedical Dataset Retrieval

AU - Wang, Xu

AU - Huang, Zhisheng

AU - van Harmelen, Frank

PY - 2020

Y1 - 2020

N2 - Ontology-based semantic similarity approaches play an important role in text-similarity task, thanks to its ability of explanation. Ontology-based semantic similarity approaches can explain how two terms are similar with help of rich knowledge in ontology. Information retrieval aims to find relevant information for given user query. As a subareas of information retrieval, dataset retrieval is an activity to find dataset which are relevant to an information need, by using full-text indexing approach or content-based indexing approach. Ontology-based semantic similarity approaches can not only do some information retrieval tasks, such as full-text mapping, but also finding deeper similar information with the help of knowledge-richness in ontology. Because of the advantage of ontology-based similarity approaches, we are looking forwards to find the possibility to using ontology-based similarity for datasets retrieval. In this paper, we provide an ontology-based similarity approach for dataset retrieval. We run our novel approach on the bioCADDIE 2016 Dataset Retrieval Challenge. After ruining experiments, we evaluate our results with several information retrieval evaluation measures. The evaluation results show that our approach could perform well.

AB - Ontology-based semantic similarity approaches play an important role in text-similarity task, thanks to its ability of explanation. Ontology-based semantic similarity approaches can explain how two terms are similar with help of rich knowledge in ontology. Information retrieval aims to find relevant information for given user query. As a subareas of information retrieval, dataset retrieval is an activity to find dataset which are relevant to an information need, by using full-text indexing approach or content-based indexing approach. Ontology-based semantic similarity approaches can not only do some information retrieval tasks, such as full-text mapping, but also finding deeper similar information with the help of knowledge-richness in ontology. Because of the advantage of ontology-based similarity approaches, we are looking forwards to find the possibility to using ontology-based similarity for datasets retrieval. In this paper, we provide an ontology-based similarity approach for dataset retrieval. We run our novel approach on the bioCADDIE 2016 Dataset Retrieval Challenge. After ruining experiments, we evaluate our results with several information retrieval evaluation measures. The evaluation results show that our approach could perform well.

KW - Biomedical dataset

KW - Dataset retrieval

KW - Semantic similarity

UR - https://www.scopus.com/pages/publications/85094144162

UR - https://www.scopus.com/inward/citedby.url?scp=85094144162&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-61951-0_5

DO - 10.1007/978-3-030-61951-0_5

M3 - Conference contribution

SN - 9783030619503

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 49

EP - 60

BT - Health Information Science

A2 - Huang, Zhisheng

A2 - Siuly, Siuly

A2 - Wang, Hua

A2 - Zhang, Yanchun

A2 - Zhou, Rui

PB - Springer Science and Business Media Deutschland GmbH

T2 - 9th International Conference on Health Information Science, HIS 2020

Y2 - 20 October 2020 through 23 October 2020

ER -

Wang X, Huang Z, van Harmelen F. Ontology-Based Semantic Similarity Approach for Biomedical Dataset Retrieval. In Huang Z, Siuly S, Wang H, Zhang Y, Zhou R, editors, Health Information Science: 9th International Conference, HIS 2020, Amsterdam, The Netherlands, October 20–23, 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 49-60. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2020 Oct 17. doi: 10.1007/978-3-030-61951-0_5

Ontology-Based Semantic Similarity Approach for Biomedical Dataset Retrieval

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Persistent URL (handle)

Other files and links

Fingerprint

Cite this