Similarity-based Knowledge Graph Queries for Recommendation Retrieval

Tracking #: 2146-3359

Lisa Wenige
Johannes Ruhland

Responsible editor: 
Guest Editors Knowledge Graphs 2018

Submission type: 
Full Paper
Current retrieval and recommendation approaches rely on hard-wired data models. This hinders personalized customizations to meet information needs of users in a more flexible manner. Therefore, the 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 novel content-based recommendation approaches. They rely 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 knowledge graphs. 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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