Paper Accepted at WISE 2020

We are very pleased to announce that we got a paper accepted for presentation at WISE 2020 (International Conference on Web Information Systems Engineering). WISE has established itself as a community aiming at high quality research and offering the ground for advancing efforts in topics related to Web information systems. WISE 2020 will be an international forum for researchers, professionals, and industrial practitioners to share their knowledge and insights in the rapidly growing areas of Web technologies for Big Data and Artificial Intelligence (AI), two highly important areas for the world economy.

  • Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking
    By Isaiah Onando Mulang, Kuldeep Singh, Akhilesh Vyas, Saeedeh Shekarpour, Akhilesh Vyas, Maria Esther Vidal, Jens Lehmann, and Sören Auer.
    Abstract The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information. Since the crowd is not bound to a standard protocol for assigning entity titles, the knowledge graph is populated by non-standard, noisy, long or even sometimes awkward titles. The issue of long, implicit, and nonstandard entity representations is a challenge in Entity Linking (EL) approaches for gaining high precision and recall. Underlying KG in general is the source of target entities for EL approaches, however, it often contains other relevant information, such as aliases of entities (e.g., Obama and Barack Hussein Obama are aliases for the entity Barack Obama). EL models usually ignore such readily available entity attributes. In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata. Our approach contributes by exploiting the sufficient context from a KG as a source of background knowledge, which is then fed into the neural network. This approach demonstrates merit to address challenges associated with entity titles (multi-word, long, implicit, case-sensitive). Our experimental study shows approx 8% improvements over the baseline approach, and significantly outperforms an end to end approach for Wikidata entity linking.