# Question Answering on Freebase via Relation Extraction and Textual Evidence Source: [Question Answering on Freebase via Relation Extraction and Textual Evidence]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:21:33.904Z Authors: Kun Xu, Siva Reddy, Yansong Feng, Songfang Huang, Dongyan Zhao Published: 2016-03-03T03:22:01.000Z ## Abstract Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a neural network based relation extractor to retrieve the candidate answers from Freebase, and then infer over Wikipedia to validate these answers. Experiments on the WebQuestions question answering dataset show that our method achieves an F\_1 of 53.3%, a substantial improvement over the state-of-the-art. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()