TREAT: Automated Construction and Maintenance of Probabilistic Knowledge Bases from Logs (Extended Abstract)

Ricky Zhu; Xue Li; Sylvia Wang; Alan Bundy; Jeff Z Pan; Kwabena Nuamah; Stefano Mauceri; Lei Xu

doi:10.1007/978-3-031-25599-1_24

TREAT: Automated Construction and Maintenance of Probabilistic Knowledge Bases from Logs (Extended Abstract)

Ricky Zhu, Xue Li, Sylvia Wang, Alan Bundy, Jeff Z Pan, Kwabena Nuamah, Stefano Mauceri, Lei Xu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

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 Given that the world is changing over time, previously acquired knowledge can become outdated.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.

Original language	English
Title of host publication	Proceedings of the 8th Annual Conference on machine Learning, Optimization and Data science
Pages	325-329
Number of pages	5
Volume	13810
ISBN (Electronic)	9783031255991
DOIs	https://doi.org/10.1007/978-3-031-25599-1_24
Publication status	Published - 9 Mar 2023
Event	The 8th Annual Conference on Machine Learning, Optimization and Data Science - Siena, Italy Duration: 18 Sept 2022 → 22 Sept 2022 Conference number: 8 https://lod2022.icas.cc/

Publication series

Name	Lecture Notes in Computer Science (LNCS)
Publisher	Springer
Volume	13810
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	The 8th Annual Conference on Machine Learning, Optimization and Data Science
Abbreviated title	LOD 2022
Country/Territory	Italy
City	Siena
Period	18/09/22 → 22/09/22
Internet address	https://lod2022.icas.cc/

Access to Document

10.1007/978-3-031-25599-1_24Licence: All Rights Reserved

TREAT_ZHU_DOA02062022_AFVAccepted author manuscript, 183 KBLicence: Creative Commons: Attribution (CC-BY)

https://link.springer.com/chapter/10.1007/978-3-031-25599-1_24#chapter-infoLicence: All Rights Reserved

Cite this

Zhu, R., Li, X., Wang, S., Bundy, A., Pan, J. Z., Nuamah, K., Mauceri, S., & Xu, L. (2023). TREAT: Automated Construction and Maintenance of Probabilistic Knowledge Bases from Logs (Extended Abstract). In Proceedings of the 8th Annual Conference on machine Learning, Optimization and Data science (Vol. 13810, pp. 325-329). (Lecture Notes in Computer Science (LNCS); Vol. 13810). https://doi.org/10.1007/978-3-031-25599-1_24

@inproceedings{ffd72575cf31454b8ea4bc3007e4fbaa,

title = "TREAT: Automated Construction and Maintenance of Probabilistic Knowledge Bases from Logs (Extended Abstract)",

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.",

author = "Ricky Zhu and Xue Li and Sylvia Wang and Alan Bundy and Pan, {Jeff Z} and Kwabena Nuamah and Stefano Mauceri and Lei Xu",

note = "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. ; The 8th Annual Conference on Machine Learning, Optimization and Data Science, LOD 2022 ; Conference date: 18-09-2022 Through 22-09-2022",

year = "2023",

month = mar,

day = "9",

doi = "10.1007/978-3-031-25599-1_24",

language = "English",

isbn = "9783031255984",

volume = "13810",

series = "Lecture Notes in Computer Science (LNCS)",

publisher = "Springer",

pages = "325--329",

booktitle = "Proceedings of the 8th Annual Conference on machine Learning, Optimization and Data science",

url = "https://lod2022.icas.cc/",

}

Zhu, R, Li, X, Wang, S, Bundy, A , Pan, JZ , Nuamah, K, Mauceri, S & Xu, L 2023, TREAT: Automated Construction and Maintenance of Probabilistic Knowledge Bases from Logs (Extended Abstract). in Proceedings of the 8th Annual Conference on machine Learning, Optimization and Data science. vol. 13810, Lecture Notes in Computer Science (LNCS), vol. 13810, pp. 325-329, The 8th Annual Conference on Machine Learning, Optimization and Data Science, Siena, Italy, 18/09/22. https://doi.org/10.1007/978-3-031-25599-1_24

TREAT: Automated Construction and Maintenance of Probabilistic Knowledge Bases from Logs (Extended Abstract). / Zhu, Ricky; Li, Xue; Wang, Sylvia et al.
Proceedings of the 8th Annual Conference on machine Learning, Optimization and Data science. Vol. 13810 2023. p. 325-329 (Lecture Notes in Computer Science (LNCS); Vol. 13810).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - TREAT: Automated Construction and Maintenance of Probabilistic Knowledge Bases from Logs (Extended Abstract)

AU - Zhu, Ricky

AU - Li, Xue

AU - Wang, Sylvia

AU - Bundy, Alan

AU - Pan, Jeff Z

AU - Nuamah, Kwabena

AU - Mauceri, Stefano

AU - Xu, Lei

N1 - Conference code: 8

PY - 2023/3/9

Y1 - 2023/3/9

N2 - 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.

AB - 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.

UR - https://link.springer.com/book/10.1007/978-3-031-25599-1?sap-outbound-id=B99521CABD9F4558D9A8A3C8EC9C49F166C31683

U2 - 10.1007/978-3-031-25599-1_24

DO - 10.1007/978-3-031-25599-1_24

M3 - Conference contribution

SN - 9783031255984

VL - 13810

T3 - Lecture Notes in Computer Science (LNCS)

SP - 325

EP - 329

BT - Proceedings of the 8th Annual Conference on machine Learning, Optimization and Data science

T2 - The 8th Annual Conference on Machine Learning, Optimization and Data Science

Y2 - 18 September 2022 through 22 September 2022

ER -

TREAT: Automated Construction and Maintenance of Probabilistic Knowledge Bases from Logs (Extended Abstract)

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