Constraint-based Multi-hop Question Answering with Knowledge Graph (2022)

Abstract: The objective of a Question-Answering system over Knowledge Graph (KGQA) is to respond to natural language queries presented over the KG. A complex question answering system typically addresses one of the two categories of complexity: questions with constraints and questions involving multiple hops of relations. Most of the previous works have addressed these complexities separately. Multi-hop KGQA necessitates reasoning across numerous edges of the KG in order to arrive at the correct answer. Because KGs are frequently sparse, multi-hop KGQA presents extra complications. Recent works have developed KG embedding approaches to reduce KG sparsity by performing missing link prediction. In this paper, we tried to address multi-hop constrained-based queries using KG embeddings to generate more flexible query graphs. Empirical results indicate that the proposed methodology produces state-of-the-art outcomes on three KGQA datasets.

## introduction multi-relational graph, also known as knowledge graph (kg) comprises of a large number (often, millions) of entities and relations represented in the form of triplets (entity -> relation -> entity). some of the most widely used kgs include dbpedia @xcite , freebase foot_0 , yago @xcite , kensho 2 and nell @xcite . in the recent years, knowledge graph question answering (kgqa) has emerged as a significant research field @xcite @xcite . given a natural language question, a kgqa system derives the right answer by analyzing the question and mapping it to the underlying kg. early works of kgqa mainly focused on simple questions containing single relations @xcite @xcite . however, in the real world, questions are often complex and recent work focuses on addressing these complexities. the complexities in kgqa can broadly be divided into two types: (1) constraint based: single-relation questions with constraints. for example, in this query "when did the 7th harry potter book come out?" there is only one relation, "published in" between the answer entity and the entity, "harry potter book" but there is also a constraint "7th" which needs to be addressed. to handle these kind of questions, query graph generation methods have been proposed @xcite @xcite . these methods first identify the 1-hop paths and then apply constraints on them. (2) multi-hop based: questions with multihop answers. for example, consider this query "what language is spoken where the capital city is brussels?" the answer is associated with entity "brussels" through two hops of relations, namely, "capital of" and "language spoken". for addressing such multi-hop questions, it is important to consider longer paths. one of the main challenges is increasing search space. it is important to restrict the multi-hop relations to be considered, otherwise the search space can grow exponentially with the length of the relation paths. for example, chen et al. (2019) and @xcite proposed to consider only the best matching relations instead of all relations when extending a relation path. however, there exists little work to address both types of complexities together. in this paper, we address both types of complex question answering -with constraints as well as multi-hop relations -together. we propose an embedding based graph query generation method by allowing longer relation paths. instead of adding constraints after complete generation of all probable paths, we apply constraints on partial paths and explore the next path segments. this helps to lessen the query search space effectively. for the complexwebquestions dataset, which has more number of complex questions; our method outperforms sota in terms of prec@1 and f1. on other benchmark datasets as well our proposed approach achieves sota results. the overall representation of our proposed model is presented in figure 1 . we make the following contributions in this paper: 1. our proposed method combines embeddings with query graphs to address constraint based multi-hop complex questions. to the best of our knowledge, this is the first attempt of combining embeddings with query graph to address all types of complex questions. ## experiments in this section, we first describe the datasets used for evaluating our method and the sota models. finally we describe the results, ablation study and error analysis. ## conclusion in this paper, we propose an embedding based query graph generation method to address complex questions (constrained multiple hops queries). often kgs are incomplete or sparsely populated, and this poses additional challenges for complex kgqa methods. by using kg embedding, the proposed methodology effectively address this kg sparsity problem by predicting missing links with-out the use of secondary corpus. by strategically incorporating constraints into the query graphs, we are able to restrict the search space. experiments showed that our proposed method outperforms all other sota methods on all the datasets (cwq, wqsp and cq). in future, we would like include a module to handle abbreviation errors.