Inducing Implicit Relations from Text using Distantly Supervised Deep Nets

Abstract

Knowledge Base Population (KBP) is an important problem in Semantic Web research and a key requirement for successful adoption of semantic technologies in many applications. In this paper we present Socrates, a deep learning based solution for Automated Knowledge Base Population from Text. Socrates does not require manual annotations which would make the solution hard to adapt to a new domain. Instead, it exploits a partially populated knowledge base and a large corpus of text documents to train a set of deep neural network models. As a result of the training process, the system learns how to identify implicit relations between entities across a highly heterogeneous set of documents from various sources, making it suitable for large-scale knowledge extraction from Web documents. Main contributions of this paper include (a) a novel approach based on composite contexts to acquire implicit relations from Title Oriented Documents, and (b) an architecture for unifying relation extraction using binary, unary, and composite contexts. We provide an extensive evaluation of the system across three different benchmarks with different characteristics, showing that our unified framework can consistently outperform state of the art solutions. Remarkably, Socrates ranked first in both the knowledge base population and attribute validation track at the Semantic Web Challenge at ISWC 2017.