Automated Knowledge Hierarchy Assessment

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Title: Automated Knowledge Hierarchy Assessment
Authors: Nayak, Guruprasad
Dutta, Sourav
Ajwani, Deepak
et al.
Permanent link: http://hdl.handle.net/10197/9889
Date: 12-Jul-2018
Online since: 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
Keywords: Knowledge hierarchiesDirected Acyclic Graphs (DAGs)Katz Graph Similarity
Other versions: http://ceur-ws.org/Vol-2127/
Language: en
Status of Item: Not peer reviewed
Is 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
Appears in Collections:Computer Science Research Collection

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