Relationship Queries on Extended Knowledge Graphs

Entity search over text corpora is not geared for relationship queries where answers are tuples of related entities and where a query often requires joining cues from multiple documents. With large knowledge graphs, structured querying on their relational facts is an alternative, but often suffers from poor recall because of mismatches between user queries and the knowledge graph or because of weakly populated relations. This paper presents the TriniT search engine for querying and ranking on extended knowledge graphs that combine relational facts with textual web contents. Our query language is designed on the paradigm of SPO triple patterns, but is more expressive, supporting textual phrases for each of the SPO arguments. We present a model for automatic query relaxation to compensate for mismatches between the data and a user’s query. Query answers – tuples of entities – are ranked by a statistical language model. We present experiments with different benchmarks, including ...

• Type: Journal
• Year: 2016
• Where: WSDM
• Authors: Mohamed Yahya, Denilson Barbosa, Klaus Berberich, Qiuyue Wang, Gerhard Weikum
• Keywords: Data Mining, WSDM 2016