Hierarchical topic tree: A hybrid model comprising network analysis and density peak search

Wu, M; Zhang, Y

Hierarchical topic tree: A hybrid model comprising network analysis and density peak search

Wu, M

Zhang, Y

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Publisher:: INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI
Publication Type:: Conference Proceeding
Citation:: 18th International Conference on Scientometrics and Informetrics, ISSI 2021, 2021, pp. 1241-1252
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Wu, M https://orcid.org/0000-0003-3956-7808
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.contributor.editor	Glanzel, W
dc.contributor.editor	Heeffer, S
dc.contributor.editor	Chi, PS
dc.contributor.editor	Rousseau, R
dc.date	2021-07-12
dc.date.accessioned	2022-06-29T08:29:35Z
dc.date.available	2022-06-29T08:29:35Z
dc.date.issued	2021-01-01
dc.identifier.citation	18th International Conference on Scientometrics and Informetrics, ISSI 2021, 2021, pp. 1241-1252
dc.identifier.isbn	9789080328228
dc.identifier.issn	2175-1935
dc.identifier.uri	http://hdl.handle.net/10453/158467
dc.description.abstract	Topic hierarchies can help researchers to develop a quick and concise understanding of the main themes and concepts in a field of interest. This is especially useful for newcomers to a field or those with a passing need for basic knowledge of a research landscape. Yet, despite a plethora of studies into hierarchical topic identification, there still lacks a model that is comprehensive enough or adaptive enough to extract the topics from a corpus, deal with the concepts shared by multiple topics, arrange the topics in a hierarchy, and give each topic an appropriate name. Hence, this paper presents a one-stop framework for generating fully-conceptualized hierarchical topic trees. First, we generate a co-occurrence network based on key terms extracted from a corpus of documents. Then a density peak search algorithm is developed and applied to identify the core topic terms, which are subsequently used as topic labels. An overlapping community allocation algorithm follows to detect topics and possible overlaps between them. Lastly, the density peak search and overlapping community allocation algorithms run recursively to structure the topics into a hierarchical tree. The feasibility, reliability, and extensibility of the proposed framework are demonstrated through a case study on the field of computer science.
dc.language	en
dc.publisher	INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI
dc.relation.ispartof	18th International Conference on Scientometrics and Informetrics, ISSI 2021
dc.relation.ispartof	18th International Conference on Scientometrics and Informetrics (ISSI)
dc.relation.ispartofseries	Proceedings of the International Conference on Scientometrics and Informetrics
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Hierarchical topic tree: A hybrid model comprising network analysis and density peak search
dc.type	Conference Proceeding
utslib.location.activity	KU Leuven, ELECTR NETWORK
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2022-06-29T08:29:32Z
pubs.finish-date	2021-07-15
pubs.publication-status	Published
pubs.start-date	2021-07-12

Abstract:

Topic hierarchies can help researchers to develop a quick and concise understanding of the main themes and concepts in a field of interest. This is especially useful for newcomers to a field or those with a passing need for basic knowledge of a research landscape. Yet, despite a plethora of studies into hierarchical topic identification, there still lacks a model that is comprehensive enough or adaptive enough to extract the topics from a corpus, deal with the concepts shared by multiple topics, arrange the topics in a hierarchy, and give each topic an appropriate name. Hence, this paper presents a one-stop framework for generating fully-conceptualized hierarchical topic trees. First, we generate a co-occurrence network based on key terms extracted from a corpus of documents. Then a density peak search algorithm is developed and applied to identify the core topic terms, which are subsequently used as topic labels. An overlapping community allocation algorithm follows to detect topics and possible overlaps between them. Lastly, the density peak search and overlapping community allocation algorithms run recursively to structure the topics into a hierarchical tree. The feasibility, reliability, and extensibility of the proposed framework are demonstrated through a case study on the field of computer science.

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http://hdl.handle.net/10453/158467