Paper Info
Title
Know Thy Neighbors, and More!: Studying the Role of Context in Entity Recommendation.
Abstract
Knowledge Graphs capture the semantic relations between real-world entities and can thus, allow end-users to explore different aspects of an entity of interest by traversing through the edges in the graph. Most of the state-of-the-art methods in entity recommendation are limited in the sense that they allow users to search only in the immediate neighborhood of the entity of interest. This is majorly due to efficiency reasons as the search space increases exponentially as we move further away from the entity of interest in the graph. Often, users perform the search task in the context of an information need and we investigate the role this context can play in overcoming the scalability issue and improving knowledge graph exploration. Intuitively, only a small subset of entities in the graph are relevant to a users' interest. We show how can we efficiently select this sub-set by utilizing contextual clues and using graph-theoretic measures to further re-rank this set to offer highly relevant graph exploration capabilities to end-users.
Year
DOI
Venue
2018
10.1145/3209542.3209548
HT
Keywords
Field
DocType
entity Search, entity recommendation, entity retrieval, contextual entity recommendation, contextual exploration, knowledge graph exploration, information discovery
Graph,Knowledge graph,World Wide Web,Information needs,Computer science,Scalability,Information discovery
Conference
ISBN
Citations 
PageRank 
978-1-4503-5427-1
1
0.35
References 
Authors
34
2
Name
Order
Citations
PageRank
Sumit Bhatia122916.93
Harit Vishwakarma211.70