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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.
Original language | English |
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Title of host publication | Data Mining - 20th Australasian Conference, AusDM 2022, Proceedings |
Editors | Laurence A.F. Park, Simeon Simoff, Heitor Murilo Gomes, Maryam Doborjeh, Yee Ling Boo, Yun Sing Koh, Yanchang Zhao, Graham Williams |
Place of Publication | Singapore |
Publisher | Springer |
Pages | 176-191 |
Number of pages | 16 |
ISBN (Electronic) | 9789811987465 |
ISBN (Print) | 9789811987458 |
DOIs | |
Publication status | Published - 2022 |
Event | 20th Australasian Data Mining Conference, AusDM 2022 - Western Sydney, Australia Duration: 12 Dec 2022 → 15 Dec 2022 |
Publication series
Name | Communications in Computer and Information Science |
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Volume | 1741 CCIS |
ISSN (Print) | 1865-0929 |
ISSN (Electronic) | 1865-0937 |
Conference
Conference | 20th Australasian Data Mining Conference, AusDM 2022 |
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Country/Territory | Australia |
City | Western Sydney |
Period | 12/12/22 → 15/12/22 |
Fingerprint
Dive into the research topics of 'Natural Language Query for Technical Knowledge Graph Navigation'. Together they form a unique fingerprint.Projects
- 1 Active
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ARC Training Centre for Transforming Maintenance through Data Science
Rohl, A. (Investigator 01), Small, M. (Investigator 02), Hodkiewicz, M. (Investigator 03), Loxton, R. (Investigator 04), O'Halloran, K. (Investigator 05), Tan, T. (Investigator 06), Calo, V. (Investigator 07), Reynolds, M. (Investigator 08), Liu, W. (Investigator 09), While, R. (Investigator 10), French, T. (Investigator 11), Cripps, E. (Investigator 12), Cardell-Oliver, R. (Investigator 13) & Correa, D. (Investigator 14)
ARC Australian Research Council
1/01/19 → 24/02/25
Project: Research
Research output
- 2 Citations
- 1 Doctoral Thesis
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Semantic parsing for natural language queries over industrial knowledge graphs
Zhao, Z., 2023, (Unpublished)Research output: Thesis › Doctoral Thesis
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