Knowledge Base Question Answering through Recursive Hypergraphs (2021)

Abstract: Knowledge Base Question Answering (KBQA) is the problem of predicting an answer for a factoid question over a given knowledge base (KB). Answering questions typically requires reasoning over multiple links in the given KB. Humans tend to answer questions by grouping different objects to perform reasoning over acquired knowledge. Hypergraphs provide a natural tool to model group relationships. In this work, inspired by typical human intelligence, we propose a new method for KBQA based on hypergraphs. Existing methods for KBQA, though effective, do not explicitly incorporate the recursive relational group structure in the given KB. Our method, which we name RecHyperNet (Recursive Hypergraph Network), exploits a new way of modelling KBs through recursive hypergraphs to organise such group relationships in KBs. Experiments on multiple KBQA benchmarks demonstrate the effectiveness of the proposed RecHyperNet. We have released the code.

## introduction knowledge base question answering (kbqa) @xcite ), a task that tests the ability of a machine to understand knowledge like a human, is a challenging, central, and popular task in natural language processing. kbqa is the problem of predicting an answer for a factoid question over a given knowledge base (kb) containing facts such as (inception, written by, christopher nolan). answering questions typically requires reasoning over multiple links in the given kb @xcite . when a typical human answers a question (e.g. what are the genres of movies written by christopher nolan?), they tend to group objects (e.g. movies written by christopher nolan such as inception, interstellar, etc. are grouped together) over their acquired knowledge. hypergraphs are mathematical tools that naturally encode group relationships. directed hypergraphs have been recently used to model kbs for kbqa @xcite but we argue that they fail to naturally model group relationships without loss of information. spcifically, two kb triples such as (inception, written by, christopher nolan) and (person of interest, written by, jonathan nolan) would be modelled by a directed hyperedge {inception, person of interest} -> {christopher nolan, jonathan nolan} clearly resulting in loss of information (not clear who directed which). though objects of similar types are grouped together and directions are encoded, directed hypergraphs fail to model kbs without loss of information. we argue that recursive hypergraphs @xcite provide a flexible way to model kbs without loss of information. specifically, all movies written by christopher nolan are grouped together and those written by jonathan nolan are separately grouped together. to summarise, we make the following contributions graph neural networks (gnns): while deep neural networks such as convolutional neural networks and recurrent neural networks are specially designed for grids and sequences respectively, gnns @xcite @xcite are specially designed to learn representations from graphstructured data. gnns have been recently extended to hypergraphs @xcite . the first published works of gnns for nlp investigated the tasks of semantic-role labelling @xcite , and neural machine translation @xcite . a recent tutorial touches upon the recent advances of gnns in nlp @xcite . gnns for question answering (qa): a popular task closely related to question answering is multi-hop reasoning across documents (context passages) where gnns have been extensively used @xcite @xcite . graph attention networks @xcite have been shown to be effective for modelling the multi-grained structure of documents for machine reading comprehension (mrc) @xcite . gnns have also recently been used on heterogeneous data sources for mrc @xcite , knowledge graphs for qa @xcite @xcite @xcite @xcite , answer sentence selection @xcite , and qa on tables @xcite and math @xcite . in all these publications except 2hr-dr @xcite , the input is a graph and limited to modelling pairwise relationships. we address this fundamental limitation by modelling a knowledge graph as a recursive hypergraph 2hr-dr models knowledge graphs through directed hypergraphs but we argue that they are more naturally represented by recursive hypergraphs. ## method we describe the kbqa problem, and how recursive hypergraphs @xcite can be used to model knowledge bases. we then describe our method of exploiting recursive hypergraphs for kbqa. ## experiments we evaluate our proposed method on metaqa @xcite and webquestionssp @xcite datasets. we closely follow the experimental setup of a prior work @xcite for the preprocessed versions of these datasets. metaqa consists of 400k questions (1 -hop, 2hop, and 3-hop), and a knowledge graph of 135k triples, 43k entities, and 9 relations. webquestion-ssp consists of 4700 questions and a knowledge