TRank: Ranking Entity Types Using the Web of Data

  • Alberto Tonon
  • Michele Catasta
  • Gianluca Demartini
  • Philippe Cudré-Mauroux
  • Karl Aberer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8218)


Much of Web search and browsing activity is today centered around entities. For this reason, Search Engine Result Pages (SERPs) increasingly contain information about the searched entities such as pictures, short summaries, related entities, and factual information. A key facet that is often displayed on the SERPs and that is instrumental for many applications is the entity type. However, an entity is usually not associated to a single generic type in the background knowledge bases but rather to a set of more specific types, which may be relevant or not given the document context. For example, one can find on the Linked Open Data cloud the fact that Tom Hanks is a person, an actor, and a person from Concord, California. All those types are correct but some may be too general to be interesting (e.g., person), while other may be interesting but already known to the user (e.g., actor), or may be irrelevant given the current browsing context (e.g., person from Concord, California). In this paper, we define the new task of ranking entity types given an entity and its context. We propose and evaluate new methods to find the most relevant entity type based on collection statistics and on the graph structure interconnecting entities and types. An extensive experimental evaluation over several document collections at different levels of granularity (e.g., sentences, paragraphs, etc.) and different type hierarchies (including DBPedia, Freebase, and shows that hierarchy-based approaches provide more accurate results when picking entity types to be displayed to the end-user while still being highly scalable.


Entity Type Mean Average Precision Inverted Index SPARQL Query Ranking Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alberto Tonon
    • 1
  • Michele Catasta
    • 2
  • Gianluca Demartini
    • 1
  • Philippe Cudré-Mauroux
    • 1
  • Karl Aberer
    • 2
  1. 1.eXascale InfolabUniversity of FribourgSwitzerland
  2. 2.EPFLLausanneSwitzerland

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