Path-Based Contextualization of Knowledge Graphs for Textual Entailment
This addresses the challenge of noise in knowledge graphs for NLP tasks like textual entailment, though it is incremental as it builds on existing path-based methods.
The paper tackles the problem of extracting relevant sub-graphs from knowledge graphs for textual entailment, showing that using entity and relationship information improves performance over text-based systems.
In this paper, we introduce the problem of knowledge graph contextualization -- that is, given a specific NLP task, the problem of extracting meaningful and relevant sub-graphs from a given knowledge graph. The task in the case of this paper is the textual entailment problem, and the context is a relevant sub-graph for an instance of the textual entailment problem -- where given two sentences p and h, the entailment relationship between them has to be predicted automatically. We base our methodology on finding paths in a cost-customized external knowledge graph, and building the most relevant sub-graph that connects p and h. We show that our path selection mechanism to generate sub-graphs not only reduces noise, but also retrieves meaningful information from large knowledge graphs. Our evaluation shows that using information on entities as well as the relationships between them improves on the performance of purely text-based systems.