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Kun Xu: Knowledge Based Question Answering

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Published on Oct 24, 2016

Kun Xu: Knowledge Based Question Answering

Abstract:
As very large structured knowledge bases have become available, answering natural language
questions over structured knowledge facts has attracted increasing research efforts.
We tackle this task in a pipeline paradigm, that is, recognizing users’ query intention
and mapping the involved semantic items against a given knowledge base (KB). we propose an
efficient pipeline framework to model a user’s query intention as a phrase level
dependency DAG which is then instantiated regarding a specific KB to construct the final
structured query. Our model benefits from the efficiency of structured prediction models
and the separation of KB-independent and KB-related modelings. The most challenging
problem in the structure instantiation is to ground the relational phrases to KB
predicates which essentially can be treated as a relation classification (RE) task. To
learn a robust and generalized representation of the relation, we propose a multi-channel
convolutional neural network which works on the shortest dependency path. Furthermore, we
introduce a negative sampling strategy to learn the assignment of subjects and objects of
a relation.

Though knowledge based question answering systems can precisely answer some factoid
questions, due to the incompleteness and imperfection of the KB, they will still fail at
answering many questions. Fortunately, we find external textual sources such as Wikipedia
can offer additional evidence to improve both the question coverage and overall
performance of a KB-QA system. Specifically, we propose two methods to incorporate the
free text into the KB-QA system. The first one is in a pipeline fashion where we
additionally perform the text based inference after the traditional KB based inference.
The second one is to employ a joint inference model to simultaneously understand the query
intention both from the KB and text. Experiments show that these two methods achieve the
state-of-the-art performances on two benchmark data sets.

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