Exploring the Suitability of Semantic Spaces as Word Association Models for the Extraction of Semantic Relationships
This work addresses the challenge of continuously updating knowledge graphs for intelligent applications, but it is incremental as it builds on existing relation extraction methods.
The paper tackles the problem of extracting semantic relationships for building knowledge graphs by exploring the use of classical semantic spaces like word embeddings to reinforce current relation extraction approaches, finding that this method can enhance extraction for certain relationship types.
Given the recent advances and progress in Natural Language Processing (NLP), extraction of semantic relationships has been at the top of the research agenda in the last few years. This work has been mainly motivated by the fact that building knowledge graphs (KG) and bases (KB), as a key ingredient of intelligent applications, is a never-ending challenge, since new knowledge needs to be harvested while old knowledge needs to be revised. Currently, approaches towards relation extraction from text are dominated by neural models practicing some sort of distant (weak) supervision in machine learning from large corpora, with or without consulting external knowledge sources. In this paper, we empirically study and explore the potential of a novel idea of using classical semantic spaces and models, e.g., Word Embedding, generated for extracting word association, in conjunction with relation extraction approaches. The goal is to use these word association models to reinforce current relation extraction approaches. We believe that this is a first attempt of this kind and the results of the study should shed some light on the extent to which these word association models can be used as well as the most promising types of relationships to be considered for extraction.