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https://ausdm22.ausdm.org/index.html%3Fp=4414.html
Australasian Conference on Data Mining
Zhao, Z., Stewart, M., Liu, W., French, T., Hodkiewicz, M. (2022). Natural Language Query for Technical Knowledge Graph Navigation. In: , 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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| This paper is directly related to Ziyu's PhD in Research Theme 1. Ziyu's The web-based interactive application is developed to help maintainers access industrial maintenance knowledge graph which is constructed from text data. 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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