Wiki2Prop: A multimodal approach for predicting wikidata properties from wikipedia

Michael Luggen, Julien Audiffren, Djellel DIfallah, Philippe CudrA©-Mauroux

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Wikidata is rapidly emerging as a key resource for a multitude of online tasks such as Speech Recognition, Entity Linking, Question Answering, or Semantic Search. The value of Wikidata is directly linked to the rich information associated with each entity - that is, the properties describing each entity as well as the relationships to other entities. Despite the tremendous manual and automatic efforts the community invested in the Wikidata project, the growing number of entities (now more than 100 million) presents multiple challenges in terms of knowledge gaps in the graph that are hard to track. To help guide the community in filling the gaps in Wikidata, we propose to identify and rank the properties that an entity might be missing. In this work, we focus on entities with a dedicated Wikipedia page in any language to make predictions directly based on textual content. We show that this problem can be formulated as a multi-label classification problem where every property defined in Wikidata is a potential label. Our main contribution, Wiki2Prop, solves this problem using a multimodal Deep Learning method to predict which properties should be attached to a given entity, using its Wikipedia page embeddings. Moreover, Wiki2Prop is able to incorporate additional features in the form of multilingual embeddings and multimodal data such as images whenever available. We empirically evaluate our approach against the state of the art and show how Wiki2Prop significantly outperforms its competitors for the task of property prediction in Wikidata, and how the use of multilingual and multimodal data improves the results further. Finally, we make Wiki2Prop available as a property recommender system that can be activated and used directly in the context of a Wikidata entity page.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021
PublisherAssociation for Computing Machinery, Inc
Number of pages10
ISBN (Electronic)9781450383127
StatePublished - Apr 19 2021
Event2021 World Wide Web Conference, WWW 2021 - Ljubljana, Slovenia
Duration: Apr 19 2021Apr 23 2021

Publication series

NameThe Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021


Conference2021 World Wide Web Conference, WWW 2021


  • Knowledge Graph
  • Multimodal Deep Learning
  • Property Prediction
  • Wikidata
  • Wikipedia

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software


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