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Answering complex questions over knowledge bases (KB-QA) faces huge inputdata with billions of facts, involving millions of entities and thousands ofpredicates. For efficiency, QA systems first reduce the answer search space byidentifying a set of facts that is likely to contain all answers and relevantcues. The most common technique for doing this is to apply named entitydisambiguation (NED) systems to the question, and retrieve KB facts for thedisambiguated entities. This work presents CLOCQ, an efficient method thatprunes irrelevant parts of the search space using KB-aware signals. CLOCQ usesa top-k query processor over score-ordered lists of KB items that combinesignals about lexical matching, relevance to the question, coherence amongcandidate items, and connectivity in the KB graph. Experiments with two recentQA benchmarks for complex questions demonstrate the superiority of CLOCQ overstate-of-the-art baselines with respect to answer presence, size of the searchspace, and runtimes.

Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases