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Natural Language Query for Technical Knowledge Graph Navigation

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Data Mining (AusDM 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1741))

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Abstract

Technical knowledge graphs are difficult to navigate. To support users with no coding experience, one can use traditional structured HTML form controls, such as drop-down lists and check-boxes, to construct queries. However, this requires multiple clicks and selections. Natural language queries, on the other hand, are more convenient and less restrictive for knowledge graphs navigation. In this paper, we propose a system that enables natural language queries against technical knowledge graphs. Given an input utterance (i.e., a query in human language), we first perform Named Entity Recognition (NER) to identify domain specific entity mentions as node names, entity types as node labels, and question words (e.g., what, how many and list) as keywords of a structured query language before the rule-based formal query constructions. Three rules are exploited to generate a valid structured formal query. The web-based interactive application is developed to help maintainers access industrial maintenance knowledge graph which is constructed from text data.

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Notes

  1. 1.

    GraphQL: https://graphql.org/learn/.

  2. 2.

    Neo4j Cypher: https://neo4j.com/docs/cypher-manual/current/).

  3. 3.

    Freebase: https://developers.google.com/freebase.

  4. 4.

    Wikidata: https://www.wikidata.org.

  5. 5.

    \(\lambda \)-calculus: https://plato.stanford.edu/entries/lambda-calculus/.

  6. 6.

    YAGO: https://yago-knowledge.org.

  7. 7.

    DBpedia: https://www.dbpedia.org/resources/knowledge-graphs/.

  8. 8.

    MITIE. https://github.com/mit-nlp/MITIE.

  9. 9.

    https://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html.

  10. 10.

    Echidna: the visualisation of the maintenance KG with form controls to support navigation, is available at https://nlp-tlp.org/maintenance_kg/.

  11. 11.

    ISO 15926-4: https://www.iso.org/standard/73830.html.

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Acknowledgment

This research is supported by the Australian Research Council through the Centre for Transforming Maintenance through Data Science (grant number IC180100030), funded by the Australian Government. We thank the reviewers for their insightful comments, and Tyler Bikaun for his proofreading and suggestions.

Author information

Authors and Affiliations

  1. UWA NLP-TLP Group, The University of Western Australia, Perth, Australia

    Ziyu Zhao, Michael Stewart, Wei Liu, Tim French & Melinda Hodkiewicz

  2. School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Australia

    Ziyu Zhao, Michael Stewart, Wei Liu & Tim French

  3. School of Engineering, The University of Western Australia, Perth, Australia

    Melinda Hodkiewicz

Authors
  1. Ziyu Zhao
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  3. Wei Liu
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Corresponding author

Correspondence to Ziyu Zhao .

Editor information

Editors and Affiliations

  1. Western Sydney University, Sydney, NSW, Australia

    Laurence A. F. Park

  2. Victoria University of Wellington, Wellington, New Zealand

    Heitor Murilo Gomes

  3. Auckland University of Technology, Auckland, New Zealand

    Maryam Doborjeh

  4. RMIT University, Melbourne, VIC, Australia

    Yee Ling Boo

  5. University of Auckland, Auckland, New Zealand

    Yun Sing Koh

  6. CSIRO Scientific Computing, Canberra, ACT, Australia

    Yanchang Zhao

  7. Australian National University, Canberra, ACT, Australia

    Graham Williams

  8. Western Sydney University, Sydney, NSW, Australia

    Simeon Simoff

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Zhao, Z., Stewart, M., Liu, W., French, T., Hodkiewicz, M. (2022). Natural Language Query for Technical Knowledge Graph Navigation. In: Park, L.A.F., et al. Data Mining. AusDM 2022. Communications in Computer and Information Science, vol 1741. Springer, Singapore. https://doi.org/10.1007/978-981-19-8746-5_13

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  • DOI: https://doi.org/10.1007/978-981-19-8746-5_13

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