Authors:
Hrag Yoghourdjian
;
Shady Elbassuoni
;
Mohamad Jaber
and
Hiba Arnaout
Affiliation:
American University of Beirut, Lebanon
Keyword(s):
RDF, Knowledge Graphs, Top-k Search, Keyword Search, Ranking, Wikipedia.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Foundations of Knowledge Discovery in Databases
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Effective keyword search over RDF knowledge graphs is still an ongoing endeavor. Most existing techniques
have their own limitations in terms of the unit of retrieval, the type of queries supported or the basis on
which the results are ranked. In this paper, we develop a novel retrieval model for general keyword queries
over Wikipedia-based RDF knowledge graphs. Our model retrieves the top-k scored subgraphs for a given
keyword query. To do this, we develop a scoring function for RDF subgraphs and then we deploy a graph
searching algorithm that only retrieves the top-k scored subgraphs for the given query based on our scoring
function. We evaluate our retrieval model and compare it to state-of-the-art approaches using YAGO, a large
Wikipedia-based RDF knowledge graph.