Similarity-based Knowledge Graph Queries for Recommendation Retrieval

Tracking #: 2031-3244

This paper is currently under review
Lisa Wenige
Johannes Ruhland

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Guest Editors Knowledge Graphs 2018

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This paper investigates how similarity-based retrieval strategies can be combined with graph queries to enable users or system providers to explore repositories in the Linked Open Data (LOD) cloud more thoroughly. For this purpose, we developed a content-based recommender system (RS). It relies on concept annotations of Simple Knowledge Organization System (SKOS) vocabularies and a SPARQL-based query language that facilitates advanced and personalized requests for openly available and interlinked datasets. We have comprehensively evaluated the novel search strategies in several test cases and example application domains (i.e., travel search and multimedia retrieval). The results of the web-based online experiments showed that our approaches increase the recall and diversity of recommendations or at least provide a competitive alternative strategy of resource access when conventional methods do not provide helpful suggestions. The findings may be of use for Linked Data-enabled recommender systems (LDRS) as well as for semantic search engines that can consume LOD resources.
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