Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion (2020)

Abstract: The incompleteness of knowledge base (KB) is a vital factor limiting the performance of question answering (QA). This paper proposes a novel QA method by leveraging text information to enhance the incomplete KB. The model enriches the entity representation through semantic information contained in the text, and employs graph convolutional networks to update the entity status. Furthermore, to exploit the latent structural information of text, we treat the text as hyperedges connecting entities among it to complement the deficient relations in KB, and hypergraph convolutional networks are further applied to reason on the hypergraph-formed text. Extensive experiments on the WebQuestionsSP benchmark with different KB settings prove the effectiveness of our model.

## introduction open domain question answering (qa) is a challenging task that requires answering the factual questions in natural language. according to the structure of supporting information, qa system can be divided into knowledge-based qa (kbqa) @xcite ) and textbased qa (tbqa) @xcite . kbqa obtains the answers by a structured knowledge base, which is easy to query and reason with but limited by the incompleteness of welldesigned triples. tbqa's supporting information is plain text containing rich semantic and latent structural information, however, it's difficult for a machine to understand. the complementary properties inspire us to fuse these two kinds of data to enhance the incomplete kb and further improve the qa system's performance. some work has already been proposed. @xcite represent kb and text using universal schema and apply memory networks, but lack the in this paper, we propose a novel qa model based on text enhanced knowledge graph, which enriches entity representation by text semantic information and complements the relations in kb through structural information of the text. specifically, the model firstly encodes entities in kb combining text information and applies graph convolutional networks (gcn) @xcite to reason across kb. note that a document usually mentions multiple entities, we convert the unstructured text into a structured hypergraph by regarding text as hyperedge connecting entities among text, and then employ hypergraph convolutional networks (hgcn) @xcite to further update the entity states. finally, the model predicts the final answers. our highlights are summarized as follows: 1) we novelly treat documents as high-order relations (hyperedges) connecting entities mentioned in them. ## related work the combination of knowledge base and text in qa is a challenging task, which has attracted many researchers' attention. the work of @xcite extends universal schema to question answering and employs key-value memory networks to process to text and kb. @xcite regard documents as heterogeneous nodes and combine them with entities in kb to form a uniform graph. the model proposed by @xcite contains a graph-attention based kb reader and a knowledge-aware text reader. some other work focuses on retrieving a small graph that contains just the question-related information @xcite and the interpretability of qa on kb and text @xcite . these methods lack considering the high-order relationship among the entities contained in the text. this paper regards the text as hyperedge and further employs hypergraph convolutional networks. hypergraph convolutional networks @xcite utilize hypergraph structure rather than a general graph to represent the high-order correlation among data entirely, and hypergraph attention @xcite further enhances the ability of representation learning by using an attention module. ## model 3.1 task definition to maintain consistency and fairness, we adopt the same setting as @xcite that builds a subgraph for each question. specifically, given a question @xmath0, w 2 , ..., w |q| ), the related sub knowledge graph @xmath1, e, t ) is extracted by the personalized pagerank @xcite , where v is the entity set, e is the relation set, and t contains a set of triples (v h , r, v t ) indicated there is a relation @xmath2. also a relevant text corpus @xmath3, d 2 , ..., d |d| is retrieved from wikipedia by an off-the-shelf document retriever @xcite , which d @xmath4, w 2 , ..., w |d i | ) represents a document and the entities mentioned in documents have been linked. the task requires to extract answers from all kb and document entities. the overview of our model is shown in figure 2 . ## conclusion we propose a qa method that aims to enhance the incomplete kb by text information, which fully explored the semantic and latent structural information in the text. in particular, the text is treated as hyperedges to complement the lack of relations in kb. the model first applies gcn to encode the entity-enriched kb, then employs hgcn to further reason over hypergraph-formed text, and predicts the final answers. experimental results on the we-bquestionssp benchmark prove the effectiveness of our model and each component.