Explainable multi-hop dense question answering using knowledge bases and text

Tracking #: 2902-4116

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Mohsen Kahani
Somayeh Asadifar
Saeedeh Shekarpour

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Guest Editors Ontologies in XAI

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Much research has been conducted extracting a response from either text sources or a knowledge base (KB). The challenge becomes more complicated when the goal is to answer a question with the help of both text and KB. In these hybrid systems, we address the following challenges: i) excessive growth of search space, ii) extraction of the answer from both KB and text, iii) extracting the path to reach to the answer, and vi) the scalability in terms of the volume of documents explored. A heterogeneous graph is utilized to tackle the first challenge guided by question decomposition. The second challenge is met with the usage of the idea behind an existing text-based method, and its customization for graph development. Based on this method for multi-hop questions, an approach is proposed for the extraction of answer explanation to address the third challenge. Since the basic method uses a dense vector for scalability, the final challenge is also addressed in the proposed hybrid method. Evaluation reveals that the proposed method has the ability to extract answers in an acceptable time and volume, while offering competitive accuracy and has created a trade-off between performance and accuracy in comparison with the base methods.
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