A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering (2021)

Abstract: Relation detection in knowledge base question answering, aims to identify the path(s) of relations starting from the topic entity node that is linked to the answer node in knowledge graph. Such path might consist of multiple relations, which we call multi-hop. Moreover, for a single question, there may exist multiple relation paths to the correct answer, which we call multi-label. However, most of existing approaches only detect one single path to obtain the answer without considering other correct paths, which might affect the final performance. Therefore, in this paper, we propose a novel divide-and-conquer approach for multi-label multi-hop relation detection (DC-MLMH) by decomposing it into head relation detection and conditional relation path generation. In specific, a novel path sampling mechanism is proposed to generate diverse relation paths for the inference stage. A majority-vote policy is employed to detect final KB answer. Comprehensive experiments were conducted on the FreebaseQA benchmark dataset. Experimental results show that the proposed approach not only outperforms other competitive multi-label baselines, but also has superiority over some state-of-art KBQA methods.

## introduction knowledge graph (kg), an important form of structured human knowledge by organizing atomic facts in the triple format, i.e., @xcite , attracts more and more researchers. however, the ever-growing knowledge graphs make it extremely hard for users to access the information efficiently. to address this issue, question answering over knowledge base (kbqa) was proposed @xcite @xcite @xcite . a kbqa system often consists of two core components: (1) entity linking, which identifies the topic entities mentioned in the question and links them to the entity nodes in the kg; (2) relation detection, which extracts the relation paths starting from the topic entity to the answer node based on the question. in this paper, we mainly focus on the second task. based on the numbers (hops) of kg triples required to obtain the answer, relation detection methods can be divided into two categories @xcite , single-hop relation detection and multi-hop relation detection. for single-hop relation detection, only one triple is needed to answer the question. for instance, answering the question "where is new york?" relies on the kb @xmath0, located in, america>. for multi-hop relation detection, more than one triples are needed to answer the question correctly. for example, to answer the question "when is the birthday of the author of harry potter", two kb @xmath1, written by, j.k. @xmath2.k. rowling, birth date, 1965.7.31> are needed. most of relation detection approaches mentioned above aim to find one single relation path to answer the question. however, for some complex questions, there might exist multiple paths in the kg leading to the correct answer. as shown in figure 1 , given the question "who is the granddaughter of queen elizabeth and george vi?", there are exactly eight relation paths to the final answer "anne". two main reasons might contribute to the multiple relation paths: (1) multiple topic entities, both 'queen elizabeth' and 'george vi' are topic entities which leads to different relation paths. (2) diverse paths between nodes in the kg, due to the complex structure of the kg. obviously, performing multiple relation paths detection will make the answer prediction more robust. therefore, in this paper, relation detection in kbqa is cast into multi-label (paths) learning. however, it is not straightforward to detect multiple relation paths with multiple hops since (1) the number of paths (label number) and the length of each path (hop number) are unfixed. (2) the number of valid relation paths (label space) is extremely large due to the exponential growth of relation combination. to deal with the above challenges, a novel divideand-conquer approach for multi-label multi-hop relation detection (dc-mlmh) is proposed by decomposing the problem into two sub-problems: in summary, the main contributions of this paper are listed as follows: our work is related to two lines of research, relation detection in kbqa and multi-label learning. ## experiment in order to evaluate the proposed approach, we conduct experiments on the benchmark dataset free-baseqa @xcite . ## conclusion in this paper, a divide-and-conquer multi-label multi-hop relation detection approach is proposed.