Abstract
Knowledge bases (KBs) are ideal vehicles for tackling many challenges, such as Query Answering, Root Cause Analysis. Given that the world is changing over time, previously acquired knowledge can become outdated. Thus, we need methods to update the knowledge when new information comes and repair any identified faults in the constructed KBs. However, to the best of our knowledge, there are few research works in this area. In this paper, we propose a system called TREAT (Tacit Relation Extraction and Transformation) to automatically construct a probabilistic KB which is continuously self-updating such that the knowledge remains consistent and up to date.
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Acknowledgement
The authors would like to thank Huawei for supporting the research and providing data on which this paper was based under grant CIENG4721/LSC. The authors would also like to thank the anonymous reviewers for their helpful comments on improving the writing of this paper. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.
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Zhu, R. et al. (2023). TREAT: Automated Construction and Maintenance of Probabilistic Knowledge Bases from Logs (Extended Abstract). In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13810. Springer, Cham. https://doi.org/10.1007/978-3-031-25599-1_24
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