Paper: Random Walk Inference and Learning in A Large Scale Knowledge Base

ACL ID D11-1049
Title Random Walk Inference and Learning in A Large Scale Knowledge Base
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

We consider the problem of performing learn- ing and inference in a large scale knowledge base containing imperfect knowledge with incomplete coverage. We show that a soft inference procedure based on a combination of constrained, weighted, random walks through the knowledge base graph can be used to reliably infer new beliefs for the knowledge base. More specifically, we show that the system can learn to infer different target relations by tuning the weights associated with random walks that follow different paths through the graph, using a version of the Path Ranking Algorithm (Lao and Cohen, 2010b). We apply this approach to a knowledge base of approximately 500,000 beliefs extracted imperfectly from the web by NELL, a never-ending language learner (Carlson et al., 2010). This new syste...