LEKG: A System for Constructing Knowledge Graphs from Log Extraction

Wang, Fangrong; Bundy, Alan; Li, Xue; Zhu, Ruiqi; Nuamah, Kwabena; Xu, Lei; Mauceri, Stefano; Pan, Jeff Z.

doi:10.1145/3502223.3502250

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…The input KG and the assertions of observed system failures are extracted from system logs and the user manual by [19] and Datalog rules representing domain knowledge are formalised by domain experts or the results of rule mining tools after being validated by experts.…”

Section: Abc In Root Cause Analysismentioning

confidence: 99%

“…Much work has focused on automatically mining logs and assisting experts in discovering the root cause of system failures, including log filtering that collects the most relevant logs [20], log extraction as knowledge graphs (KG) [19], clustering logs [15,11], mining and representing information from logs [6,8]; automating network management in software based on KG [21] and analysing causality patterns among components in a software system [12,14,3], where the last takes extra tests to learn and validate dependencies by experts manually. It can be seen that log-based RCA is popular because logs are arguably the most straightforward source of information about the system.…”

Section: Introductionmentioning

confidence: 99%

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ABC in Root Cause Analysis: Discovering Missing Information and Repairing System Failures

Li¹,

Bundy²,

Zhu³

et al. 2023

Lecture Notes in Computer Science

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Root-cause analysis (RCA) is a crucial task in software system maintenance, where system logs play an essential role in capturing system behaviours and describing failures. Automatic RCA approaches are desired, which face the challenge that the knowledge model (KM) extracted from system logs can be faulty when logs are not correctly representing some information. When unrepresented information is required for successful RCA, it is called missing information (MI). Although much work has focused on automatically finding root causes of system failures based on the given logs, automated RCA with MI remains under-explored. This paper proposes using the Abduction, Belief Revision and Conceptual Change (ABC) system to automate RCA after repairing the system's KM to contain MI. First, we show how ABC can be used to discover MI and repair the KM. Then we demonstrate how ABC automatically finds and repairs root causes. Based on automated reasoning, ABC considers the effect of changing a cause when repairing a system failure: the root cause is the one whose change leaves the fewest failures. Although ABC outputs multiple possible solutions for experts to choose from, it hugely reduces manual work in discovering MI and analysing root causes, especially in large-scale system management, where any reduction in manual work is very beneficial. This is the first application of an automatic theory repair system to RCA tasks: KM is not only used, it will be improved because our approach can guide engineers to produce KM/higher-quality logs that contain the spotted MI, thus improving the maintenance of complex software systems.

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Section: Abc In Root Cause Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ABC in Root Cause Analysis: Discovering Missing Information and Repairing System Failures

Li¹,

Bundy²,

Zhu³

et al. 2023

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…There have been automated knowledge acquisition techniques by datadriven methods, e.g. LeKG [11]. However, even state-of-the-art techniques for open knowledge acquisition are error-prone, due to noise in the data.…”

Section: Motivationmentioning

confidence: 99%

“…In the task of knowledge extraction, we developed a combination of text processing techniques called LeKG [11], which handles knowledge extraction from the documentation of natural language grammar, and logs generated from finite but unknown templates. The combined techniques involves template matching, Entity Recognition [9], Open Relation Extraction [2], and deductive reasoning with logical constraints.…”

Section: Knowledge Acquisitionmentioning

confidence: 99%

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

Zhu¹,

Li²,

Wang³

et al. 2023

Machine Learning, Optimization, and Data Science

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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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Automated Construction and Application of Operations and Maintenance Knowledge Graph

Zheng,

Zhao,

Luo

et al. 2023

2023 3rd International Conference on Computer Science and Blockchain (CCSB)

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LEKG: A System for Constructing Knowledge Graphs from Log Extraction

Cited by 6 publications

References 28 publications

ABC in Root Cause Analysis: Discovering Missing Information and Repairing System Failures

ABC in Root Cause Analysis: Discovering Missing Information and Repairing System Failures

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

Automated Construction and Application of Operations and Maintenance Knowledge Graph

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