Integrating Multi-level Tag Recommendation with External Knowledge Bases for Automatic Question Answering

摘要

We focus on using natural language unstructured textual Knowledge Bases (KBs) to answer questions from community-based Question-and-Answer (Q&A) websites. We propose a novel framework that integrates multi-level tag recommendation with external KBs to retrieve the most relevant KB articles to answer user posted questions. Different from many existing efforts that primarily rely on the Q&A sites' own historical data (e.g., user answers), retrieving answers from authoritative external KBs (e.g., online programming documentation repositories) has the potential to provide rich information to help users better understand the problem, acquire the knowledge, and hence avoid asking similar questions in future. The proposed multilevel tag recommendation best leverages the rich tag information by first categorizing them into different semantic levels based on their usage frequencies. A post-tag co-clustering model, augmented by a two-step tag recommender, is used to predict tags at different levels for a given user posted question. A KB article retrieval component leverages the recommended multi-level tags to select the appropriate KBs and search/rank the matching articles thereof. We conduct extensive experiments using real-world data from a Q&A site and multiple external KBs to demonstrate the effectiveness of the proposed question-answering framework.