Feature-Rich Networks for Knowledge Base Completion

Suresh Kumar Manandhar, Alexandros Komninos

Research output: Contribution to conferencePaperpeer-review


We propose jointly modelling Knowledge Bases and aligned text with Feature-Rich
Networks. Our models perform Knowledge Base Completion by learning to represent and compose diverse feature types from partially aligned and noisy resources. We perform experiments on Freebase utilizing additional entity type information and syntactic textual relations. Our evaluation suggests that the proposed models can better incorporate side information than previously proposed combinations of bilinear models with convolutional neural networks, showing large improvements when scoring the plausibility of unobserved facts with associated textual mentions.
Original languageEnglish
Number of pages6
Publication statusPublished - 30 Jul 2017
Event55th Annual Meeting of the Association for Computational Linguistics - Vancouver, Canada
Duration: 30 Jul 20174 Aug 2017


Conference55th Annual Meeting of the Association for Computational Linguistics

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