Link Topics from Q&A Platforms using Wikidata: A Tool for Cross-platform Hierarchical Classification

Alyssa Shuang Sha, Bernardo Pereira Nunes, Armin Haller

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    This paper proposes a novel rule-based topic classification tool for questions on Q&A platforms mediated by the Wikidata ontology-an open and accessible multilingual ontology curated by a large community of online users. Q&A platforms are important sources of information on the Web and often appear as part of Web search results. By adopting Wikidata taxonomic relations as references, our tool can categories the Web content from different platforms in a unified coarse-to-fine mode based on their domain coverage. To validate and demonstrate the potential applicability of our tool, a set of use cases and experiments are carried out on two popular Q&A platforms-Zhihu and Quora, where the impact of topic categories on question lifecycles is explored. Furthermore, we compare our results with the output generated by GPT-3 classifier. This tool sheds light on how structured knowledge bases can enable data interoperability and serve as a filtering functionality to mitigate classification bias of OpenAI.

    Original languageEnglish
    Title of host publicationWebSci 2023 - Proceedings of the 15th ACM Web Science Conference
    PublisherAssociation for Computing Machinery (ACM)
    Pages357-362
    Number of pages6
    ISBN (Electronic)9798400700897
    DOIs
    Publication statusPublished - 30 Apr 2023
    Event15th ACM Web Science Conference, WebSci 2023 - Austin, United States
    Duration: 30 Apr 20231 May 2023

    Publication series

    NameACM International Conference Proceeding Series

    Conference

    Conference15th ACM Web Science Conference, WebSci 2023
    Country/TerritoryUnited States
    CityAustin
    Period30/04/231/05/23

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