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Automated Knowledge Hierarchy Assessment
Date Issued
2018-07-12
Date Available
2019-04-10T11:11:42Z
Abstract
Automated construction of knowledge hierarchies is gaining increasing attention to tackle the infeasibility of manually extracting and semantically linking millions of concepts. With the evolution of knowledge hierarchies, there is a need for measures to assess its temporal evolution, quantifying the similarities between different versions and identifying the relative growth of different subgraphs in the knowledge hierarchy. This work proposes a principled and scalable similarity measure, based on Katz similarity between concept nodes, for comparing knowledge hierarchies, modeled as generic Directed Acyclic Graphs (DAGs).
Type of Material
Conference Publication
Publisher
CEUR Workshop Proceedings
Volume
2127
Start Page
59
End Page
60
Copyright (Published Version)
2018 the Authors
Web versions
Language
English
Status of Item
Not peer reviewed
Part of
Dietz, L., Koesten, L., Verbene, S. (eds.). Joint Proceedings of the First International Workshop on Professional Search (ProfS2018); the Second Workshop on Knowledge Graphs and Semantics for Text Retrieval, Analysis, and Understanding (KG4IR); and the International Workshop on Data Search (DATA:SEARCH’18) Co-located with (ACM SIGIR 2018)
Conference Details
The Second Workshop on Knowledge Graphs and Semantics for Text Retrieval, Analysis, and Understanding (KG4IR), Michigan, USA, 12 July 2018
ISSN
1613-0073
This item is made available under a Creative Commons License
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