IIIT Hyderabad Publications |
|||||||||
|
Scalable Knowledge Graph Construction over Text using Deep Learning based Predicate MappingAuthors: Aman mehta,Aashay Singhal,Kamalakar Karlapalem Conference: The Web Conference 2019 (The Web Conference 2019 2019) Location San Fransisco, California, USA Date: 2019-05-13 Report no: IIIT/TR/2019/73 AbstractAutomatic extraction of information from text and its transforma- tion into a structured format is an important goal in both Semantic Web Research and computational linguistics. Knowledge Graphs (KG) serve as an intuitive way to provide structure to unstructured text. A fact in a KG is expressed in the form of a triple which captures entities and their interrelationships (predicates). Multiple triples extracted from text can be semantically identical but they may have a vocabulary gap which could lead to an explosion in the number of redundant triples. Hence, to get rid of this vocabulary gap, there is a need to map triples to a homogeneous namespace. In this work, we present an end-to-end KG construction system, which identifies and extracts entities and relationships from text and maps them to the homogenous DBpedia namespace. For Predi- cate Mapping, we propose a Deep Learning architecture to model semantic similarity. This mapping step is computation heavy, ow- ing to the large number of triples in DBpedia. We identify and prune unnecessary comparisons to make this step scalable. Our experiments show that the proposed approach is able to construct a richer KG at a significantly lower computation cost with respect to previous work. Full paper: pdf Centre for Data Engineering |
||||||||
Copyright © 2009 - IIIT Hyderabad. All Rights Reserved. |