Managing polysemy and synonymy in science mapping using the mixtures of factor analyzers model.
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| Title: | Managing polysemy and synonymy in science mapping using the mixtures of factor analyzers model. |
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| Authors: | Kwakkel, Jan H.1 j.h.kwakkel@tudelft.nl, Cunningham, Scott W.1 s.cunningham@tudelft.nl |
| Source: | Journal of the American Society for Information Science & Technology. Oct2009, Vol. 60 Issue 10, p2064-2078. 15p. 2 Diagrams, 6 Charts, 3 Graphs. |
| Subjects: | Polysemy, Comparative semantics, Differentiable mappings, Cluster analysis (Statistics), Concept mapping, Factor analysis, Taxonomy, Numerical analysis, Linguistics |
| Abstract: | A new method for mapping the semantic structure of science is described. We assume that different researchers, working on the same set of research problems, will use the same words for concepts central to their research problems. Therefore, different research fields and disciplines should be identifiable by different words and the pattern of co-occurring words. In natural language, however, there is quite some diversity because many words have multiple meaning. In addition, the same meaning can be expressed by using different words. We argue that traditional factor analytic and cluster analytic techniques are inadequate for mapping the semantic structure if such polysemous and synonymous words are present. Instead, an alternative model, the mixtures of factor analyzers (MFA) model, is utilized. This model extends the traditional factor analytic model by allowing multiple centroids of the dataset. We argue that this model is structurally better suited to map the semantic structure of science. The model is illustrated by a case study of the uncertainty literature sampled from data from the ISI Web of Science. The MFA model is applied with the goal of discovering multiple, potentially incommensurate, conceptualizations of uncertainty in the literature. In this way, the MFA model can help in creating understanding of the use of language in science, which can benefit multidisciplinary research and interdisciplinary understanding, and assist in the development of multidisciplinary taxonomies of science. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 44295784 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Managing polysemy and synonymy in science mapping using the mixtures of factor analyzers model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kwakkel%2C+Jan+H%2E%22">Kwakkel, Jan H.</searchLink><relatesTo>1</relatesTo><i> j.h.kwakkel@tudelft.nl</i><br /><searchLink fieldCode="AR" term="%22Cunningham%2C+Scott+W%2E%22">Cunningham, Scott W.</searchLink><relatesTo>1</relatesTo><i> s.cunningham@tudelft.nl</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Society+for+Information+Science+%26+Technology%22">Journal of the American Society for Information Science & Technology</searchLink>. Oct2009, Vol. 60 Issue 10, p2064-2078. 15p. 2 Diagrams, 6 Charts, 3 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Polysemy%22">Polysemy</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+semantics%22">Comparative semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Differentiable+mappings%22">Differentiable mappings</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Concept+mapping%22">Concept mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Factor+analysis%22">Factor analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Taxonomy%22">Taxonomy</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistics%22">Linguistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A new method for mapping the semantic structure of science is described. We assume that different researchers, working on the same set of research problems, will use the same words for concepts central to their research problems. Therefore, different research fields and disciplines should be identifiable by different words and the pattern of co-occurring words. In natural language, however, there is quite some diversity because many words have multiple meaning. In addition, the same meaning can be expressed by using different words. We argue that traditional factor analytic and cluster analytic techniques are inadequate for mapping the semantic structure if such polysemous and synonymous words are present. Instead, an alternative model, the mixtures of factor analyzers (MFA) model, is utilized. This model extends the traditional factor analytic model by allowing multiple centroids of the dataset. We argue that this model is structurally better suited to map the semantic structure of science. The model is illustrated by a case study of the uncertainty literature sampled from data from the ISI Web of Science. The MFA model is applied with the goal of discovering multiple, potentially incommensurate, conceptualizations of uncertainty in the literature. In this way, the MFA model can help in creating understanding of the use of language in science, which can benefit multidisciplinary research and interdisciplinary understanding, and assist in the development of multidisciplinary taxonomies of science. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/asi.21114 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 2064 Subjects: – SubjectFull: Polysemy Type: general – SubjectFull: Comparative semantics Type: general – SubjectFull: Differentiable mappings Type: general – SubjectFull: Cluster analysis (Statistics) Type: general – SubjectFull: Concept mapping Type: general – SubjectFull: Factor analysis Type: general – SubjectFull: Taxonomy Type: general – SubjectFull: Numerical analysis Type: general – SubjectFull: Linguistics Type: general Titles: – TitleFull: Managing polysemy and synonymy in science mapping using the mixtures of factor analyzers model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kwakkel, Jan H. – PersonEntity: Name: NameFull: Cunningham, Scott W. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2009 Type: published Y: 2009 Identifiers: – Type: issn-print Value: 15322882 Numbering: – Type: volume Value: 60 – Type: issue Value: 10 Titles: – TitleFull: Journal of the American Society for Information Science & Technology Type: main |
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