Representing Gender in Library Catalogue: Developing Multilingual Homosaurus for Automated Subject Indexing

The sexist and homophobic attitude of global, generic knowledge organization systems like LCSH and DDC (as reported by many researchers and critiques since the 1970s) made the availability of LGBTQ+ resources extremely limited in India. This research uses the Homosaurus, a domain-specific comprehensive vocabulary tool (but in the English language only and non-interactive mode), and focuses on developing a multilingual and collaborative software framework to host the Homosaurus (in Hindi and Bengali to start with), as an interactive, participative, and collaborative global vocabulary tool. The main deliverable of this study is a multilingual Homosaurus in RDF serialization formats, which will be used as the vocabulary backend for a machine learning framework to support semi-automated indexing of LGBTQ+ documentary resources. The research aims to counter the challenges posed by limited indexed resources within the LGBTQ+ knowledge domain in Indian libraries by formulating and implementing a semi-automated subject indexing system. The prototype developed deploys the following open source tools and open access data sources: (i) Annif as the AI/ML framework; (ii) Machine learning backends like FastText, Omikuji and Neural network; (iii) VocBench to host multilingual Homosaurus; (iv) Skosify to curate RDF serializing formats of multilingual Homosaurus; (v) Skosmos to develop user interface for the multilingual Homosaurus; and (vi) open access databases (CrossRef, CoRE, Lens, OpenAlex and Semantic Scholar) to collect, gather and process the required training datasets. The research is multifaceted, encompassing the development of a semi-automated indexing framework, the evaluation of its operational efficiencies, and exploring the feasibility of a REST/ API call-based approach for expeditious indexing of a substantial volume of records pertinent to the LGBTQ+ domain. The proposed semi-automated subject indexing system aims to enhance access to LGBTQ+ knowledge and challenge the prevailing biases inherent in existing knowledge organization paradigms.

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Publication Details

Journal
Journal of Information and Knowledge
Published
2024-10-21
DOI
https://doi.org/10.17821/srels/2024/v61i5/171585
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
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article

Representing Gender in Library Catalogue: Developing Multilingual Homosaurus for Automated Subject Indexing

Parthasarathi Mukhopadhyay, Roshni Mitra
Journal of Information and Knowledge
Natural Language Processing Techniques
article

Representing Gender in Library Catalogue: Developing Multilingual Homosaurus for Automated Subject Indexing

Parthasarathi Mukhopadhyay, Roshni Mitra
article en

Abstract

The sexist and homophobic attitude of global, generic knowledge organization systems like LCSH and DDC (as reported by many researchers and critiques since the 1970s) made the availability of LGBTQ+ resources extremely limited in India. This research uses the Homosaurus, a domain-specific comprehensive vocabulary tool (but in the English language only and non-interactive mode), and focuses on developing a multilingual and collaborative software framework to host the Homosaurus (in Hindi and Bengali to start with), as an interactive, participative, and collaborative global vocabulary tool. The main deliverable of this study is a multilingual Homosaurus in RDF serialization formats, which will be used as the vocabulary backend for a machine learning framework to support semi-automated indexing of LGBTQ+ documentary resources. The research aims to counter the challenges posed by limited indexed resources within the LGBTQ+ knowledge domain in Indian libraries by formulating and implementing a semi-automated subject indexing system. The prototype developed deploys the following open source tools and open access data sources: (i) Annif as the AI/ML framework; (ii) Machine learning backends like FastText, Omikuji and Neural network; (iii) VocBench to host multilingual Homosaurus; (iv) Skosify to curate RDF serializing formats of multilingual Homosaurus; (v) Skosmos to develop user interface for the multilingual Homosaurus; and (vi) open access databases (CrossRef, CoRE, Lens, OpenAlex and Semantic Scholar) to collect, gather and process the required training datasets. The research is multifaceted, encompassing the development of a semi-automated indexing framework, the evaluation of its operational efficiencies, and exploring the feasibility of a REST/ API call-based approach for expeditious indexing of a substantial volume of records pertinent to the LGBTQ+ domain. The proposed semi-automated subject indexing system aims to enhance access to LGBTQ+ knowledge and challenge the prevailing biases inherent in existing knowledge organization paradigms.

Journal of Information and Knowledge
University of Kalyani (IN)
Quality Education
Openalex Percentile: Top 83%
Natural Language Processing Techniques
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