Keyword search over weighted keyword-augmented RDF graphs -

Abstract

Large knowledge bases consisting of entities and relationships between them have become vital sources of information for many applications. Most of these knowledge bases adopt the Semantic-Web data model RDF as a representation model. Querying these knowledge bases is typically done using structured queries utilizing graph-pattern languages such as SPARQL. However, such structured queries require some expertise from users which limits the accessibility to such data sources. To overcome this, keyword search must be supported. In this thesis, we develop a retrieval model for keyword queries over RDF graphs. Our model retrieves the top-k most relevant sub-graphs to a given keyword query. To be able to do this, we augment the searched RDF graph with keywords which are extracted from entity labels and textual patterns for relations. Moreover, we associate each triple in the RDF graph with a weight reflecting the importance of the triple. Finally, we deploy a graph searching algorithm that searches this weighted keyword-augmented RDF graph and retrieves the top-k most relevant sub-graphs to the given keyword query, where relevance is measured based on the triple weights. We evaluate the effectiveness of our retrieval model using a real-world RDF dataset and compare it to various state-of-the-art approaches for keyword search over RDF graphs.