Harnessing Relationships for Domain-specific Subgraph Extraction: A Recommendation Use Case

TitleHarnessing Relationships for Domain-specific Subgraph Extraction: A Recommendation Use Case
Publication TypeConference Paper
Year of Publication2016
AuthorsSarasi Lalithsena, Pavan Kapanipathi, Amit Sheth
Conference NameIEEE International Conference on Big Data
Conference LocationWashington D.C.
KeywordsDomain-specific knowledge graph, Recommendation, Relationship Ranking
Abstract

Applications on the Web such as search engines and recommendation systems are increasingly adapting semantic approaches by leveraging knowledge graphs. While some applications require processing of the whole knowledge graph, most are domain-specific and require only a relevant subset of it. For example, a movie or a book recommendation system would require a subgraph that comprises knowledge relevant to the specific domain. In such scenarios, processing the whole knowledge graph, particularly the commonly used, large, and openly available knowledge graphs on the Web, is computationally intensive and the irrelevant portion may negatively impact the performance of the application. This necessitates the identification and extraction of relevant subgraphs that adequately captures entities and their relationships for a given application domain and/or task. In this work, we present an approach to identify a minimal domain-specific subgraph by utilizing statistic and semantic-based metrics. Our approach highlights the importance of relationships as first-class elements to capture domain specificity of a subgraph. We demonstrate the applicability of this approach for a recommendation use case on two domains, i.e. movie and book. Our evaluation demonstrates a reduction of 80% to 90% of the knowledge graph with orders of magnitude decrease in time for computation without compromising accuracy.

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Citation:
S. Lalithsena, P Kapanipathi, A. Sheth. Harnessing Relationships for Domain-specific Subgraph Extraction: A Recommendation Use Case, IEEE International Conference on Big Data, Washington, D.C., 2016.

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