Pay More Attention to Relation Exploration for Knowledge Base Question Answering (2023)

Abstract: Knowledge base question answering (KBQA) is a challenging task that aims to retrieve correct answers from large-scale knowledge bases. Existing attempts primarily focus on entity representation and final answer reasoning, which results in limited supervision for this task. Moreover, the relations, which empirically determine the reasoning path selection, are not fully considered in recent advancements. In this study, we propose a novel framework, RE-KBQA, that utilizes relations in the knowledge base to enhance entity representation and introduce additional supervision. We explore guidance from relations in three aspects, including (1) distinguishing similar entities by employing a variational graph auto-encoder to learn relation importance; (2) exploring extra supervision by predicting relation distributions as soft labels with a multi-task scheme; (3) designing a relation-guided re-ranking algorithm for post-processing. Experimental results on two benchmark datasets demonstrate the effectiveness and superiority of our framework, improving the F1 score by 5.8% from 40.5 to 46.3 on CWQ and 5.7% from 62.8 to 68.5 on WebQSP, better or on par with state-of-the-art methods.

## introduction given a question expressed in natural language, knowledge base question answering (kbqa) aims to find the correct answers from a large-scale knowledge base (kb), such as freebase @xcite , wikipedia @xcite , dbpeidia @xcite , etc. for example, the question "who is emma stone's father?" can be answered by the fact of "(jeff stone, person.parents, emma stone)". the deployment of kbqa can significantly enhance a system's knowledge, improving performance for applications such as dialogue systems and search engines. figure 1 : an example of kbqa process. the reasoning begins with the red node and passes through similar entities, which are defined as entities that have similar relations as shown in the upper right box. besides, key reasoning relations whose tokens (x i ) hold overlap between given questions (t i ) are important for reasoning. early attempts on kbqa @xcite @xcite mostly focus on transferring given questions into structured logic forms, which are strictly constrained by the consistent structure of parsed query and kb. to overcome the limitation of the incompleteness of kb, many approaches @xcite @xcite have been developed that aim to map questions and their related kb entities and relations into embeddings, and define the reasoning process as a similarity retrieval problem, which is called ir-based method. additionally, some studies @xcite @xcite have attempted to learn relation embeddings and then incorporate surrounding relations to represent entities, which successfully reduces the number of parameters needed for the model. however, most of these works @xcite ) primarily focus on final answer reasoning and the representation of entities, while few explore the full utilization of relations in kb. additionally, for answer reasoning, the supervision signal provided is also only from entities, while we believe that the relations also play an important role in determining the reasoning path and the answer choosing. we propose a new framework, called relation-enhanced kbqa (re-kbqa), to investigate the potential use of relations in kbqa by utilizing an embedding-fused framework. the proposed framework aims to study the role of relations in kbqa in the following three aspects: relations for entity representation. we find that similar entities with similar surrounding relations (e.g., the three green circles in the upper right of figure 1 ) play an important role in reasoning. to distinguish them, we introduce qa-vgae, a question-answering-oriented variational graph auto-encoder, which learns relation weights through global structure features and represents entities by integrating surrounding relations. relations for extra supervision. multi-hop reasoning is often hindered by weak supervision, as models can only receive feedback from final answers @xcite . to overcome this limitation, we propose a multi-task scheme by predicting the relation distribution of the final answers as additional guidance, using the same reasoning architecture and mostly shared parameters. as illustrated in figure 1 , the proposed scheme requires the prediction of both the answer "haitian creole" and its surrounding relation distribution. ## our approach as discussed in section 1, we consider three aspects to further boost the performance of kbqa, including (i) the enhancement of the representation capability, especially for similar entities; (ii) a strategy of mining more supervision signals to guide the training; and (iii) a reasoning path correction algorithm to adjust the ranking results. below, we shall elaborate on our network architecture (re-kbqa) with our solutions to the above issues. ## conclusion in this paper, we proposed a novel framework, namely re-kbqa, with three novel modules for knowledge base question answering, which are qa-vgae to explore the relation promotion for entity representation, multi-task learning to exploit relations for more supervisions, and serr to postprocess relations to re-rank candidates. extensive experiments validate the superior performance of our method compared with state-of-the-art irbased approaches.