Automated Knowledge Hierarchy Assessment
|Title:||Automated Knowledge Hierarchy Assessment||Authors:||Nayak, Guruprasad
|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 hierarchies; Directed 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Graphs (DAGs); Katz Graph Similarity||Other versions:||http://ceur-ws.org/Vol-2127/||Language:||en||Status of Item:||Not peer reviewed||Is part of:||Dietz, L., Koesten, L., Verbene, S. (eds.).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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