Implicit Relation Linking for Question Answering over Knowledge Graph (2022)

Abstract: Relation linking (RL) is a vital module in knowledge-based question answering (KBQA) systems. It aims to link the relations expressed in natural language (NL) to the corresponding ones in knowledge graph (KG). Existing methods mainly rely on the textual similarities between NL and KG to build relation links. Due to the ambiguity of NL and the incompleteness of KG, many relations in NL are implicitly expressed, and may not link to a single relation in KG, which challenges the current methods. In this paper, we propose an implicit RL method called ImRL, which links relation phrases in NL to relation paths in KG. To find proper relation paths, we propose a novel path ranking model that aligns not only textual information in the word embedding space but also structural information in the KG embedding space between relation phrases in NL and relation paths in KG. Besides, we leverage a gated mechanism with attention to inject prior knowledge from external paraphrase dictionaries to address the relation phrases with vague meaning. Our experiments on two benchmark and a newly-created datasets show that ImRL significantly outperforms several state-of-the-art methods, especially for implicit RL.

## introduction the past few years have witnessed the rapid development of knowledge-based question answering (kbqa), which aims to answer natural language (nl) questions over knowledge graph (kg), e.g., dbpedia @xcite and freebase @xcite . in many kbqa systems @xcite , as a fundamental module, relation linking (rl) seeks to link a relation phrase expressed in nl to a corresponding relation in kg, which has a great impact on the accuracy of subsequent steps like query construction and even the entire kbqa systems. ## related work existing rl approaches mainly focus on two tasks: relation candidate generation and relation disambiguation. for the first task, rematch @xcite , sibkb @xcite and eerl @xcite generate candidates by running a textual similarity-based method over prebuilt dictionaries constructed by analyzing the nl patterns contained in massive text corpora through frequent item mining or crowdsourcing. several widely-used dictionaries include patty @xcite , ppdb @xcite , paraphrase @xcite , etc. as for the second task, earl @xcite leverages the connection density of kg for ranking. falcon @xcite uses several fundamental principles of english morphology to obtain auxiliary information, and exploits an alignment model for linking. with the development of deep learning, a few deep learning-based methods appear. sling @xcite leverages abstract meaning representation (amr) to capture the semantic relationships in a question, and ranks the candidates with a transformer-based model, which is trained in a distantly supervised manner. simrel @xcite also uses amr, and trains a bert-based model for relation disambiguation. in addition to the aforementioned studies using dbpedia, there are also some studies, e.g., kbpearl @xcite and falcon 2.0 @xcite , towards other kgs such as wikidata @xcite . despite some literature @xcite @xcite mentions the issue of implicit relations, there is still much left for improvement. for instance, eerl discovers the implicit relations by using the domains and ranges of properties in kg, but it cannot handle the issue of rl through relation paths. falcon and sling mainly focus on the implicit relations in phrases expressing the meaning about country, e.g., "spanish movie". however, they perform poorly for other kinds of implicit relations, and cannot handle rl through relation paths either. in recent years, kg embedding has become a popular area of research @xcite . its main idea is to encode entities and relations in kg into a continuous low-dimensional embedding space. these embeddings can be further applied to various tasks, such as kg completion and kbqa. many studies in the kg completion field @xcite @xcite predict the possibility of the establishment of a specific triple through the embedding of entities and relations. recently, researchers in the kbqa field have also begun to leverage kg embeddings. the studies @xcite try to learn a deep model that maps a nl question into a kg embedding space, and combine the kg embeddings of topic entities in the question with the question embedding to infer the answer. to the best of our knowledge, there is no prior work focusing on using kg embedding for implicit rl. ## implicit relation linking a knowledge graph (kg), denoted by g, can be defined as a triple @xmath0, r, t ), where e is the entity set, r is the relation set, and t ⊆ e × r × e denotes the relational triple set. each entity in e is denoted by e, and each relation in r is denoted by r. if there exists a set of m triples {(e 0 , r 1 , e 1 ), . . . , (e m-1 , r m , e m )} ⊆ t , we say that there is an m-hop relation path p between e 0 and e m , denoted by @xmath1, . . . , r m ). the goal of rl can be formulated as: given a nl question q and a kg g, linking a relation phrase s in q to its corresponding relation r or relation path p in g. figure 2 shows the architecture of our method. ## conclusion in this paper, we propose imrl, an implicit rl method that can better deal with the linking of implicit relations in nl and incomplete kg. we extend the rl task to implicit relation path linking, and propose a novel ranking model with knowledge injection. we evaluate our model on three datasets. the results show that our method achieves superior performance on lc-quad and qald-9, where the relations are mostly explicit, and pathsq, where the relations are all implicit, demonstrating that imrl can not only deal with relation phrases that are not explicitly expressed in nl, but also perform relation path reasoning in kg. in future work, we will explore how to combine entity linking and implicit rl for improvement.