Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph (2022)

Abstract: Knowledge graph question answering (KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph. Most existing methods first roughly retrieve the knowledge subgraphs (KSG) that may contain candidate answer, and then search for the exact answer in the KSG. However, the KSG may contain thousands of candidate nodes since the knowledge graph involved in querying is often of large scale, thus decreasing the performance of answer selection. To tackle this problem, we first propose to partition the retrieved KSG to several smaller sub-KSGs via a new subgraph partition algorithm and then present a graph-augmented learning to rank model to select the top-ranked sub-KSGs from them. Our proposed model combines a novel subgraph matching networks to capture global interactions in both question and subgraphs and an Enhanced Bilateral Multi-Perspective Matching model to capture local interactions. Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method. The experimental results on multiple benchmark datasets have demonstrated the effectiveness of our approach.

## introduction with the rise of large-scale knowledge graphs (kg) such as dbpedia @xcite and freebase @xcite , question answering over knowledge graph has attracted massive attention recently, which aims to leverage the factual information in a kg to answer natural language question. depending on the complexity of question, kgqa can be divided into two forms: simple and complex. simple kgqa often requires only one hop of factual knowledge, while complex kgqa requires reasoning over a multi-hop knowledge subgraph (ksg) and selecting the correct answer among several candidate answers. in this paper, we focus on the latter, i.e., complex kgqa, which is more challenging. currently, most kgqa approaches resort to semantic parsing @xcite @xcite or retrieve-then-extract methods @xcite . semantic parsing methods usually translate a natural language question to a kg query and then use it to query the kg directly. however, semantic parsing methods often rely on complex and specialised hand-crafted rules or schemes. in contrast, retrieve-then-extract methods are easier to understand and more interpretable. they first retrieve the kg coarsely to obtain a ksg containing answer candidates. then, the target answer is extracted from the retrieved ksg. this paper follows the research idea of the retrieve-then-extract methods. most previous works retrieve a knowledge subgraph from the original kg by choosing topic entities (e.g., kg entities mentioned in the given question) and their few-hop neighbors. however, since the kg is often of large volume and the initial retrieval process on it is coarse-grained and heuristic, the ksg retrieved by this method may still contain thousands of nodes and most of them are irrelevant to the given question, especially when the number of topic entities or hops significantly increases. the larger the ksg is, the more difficult it is to find the correct answer in it. to reduce the size of the ksg, the similarity between the question and the relations around the topic entities is computed @xcite and then the personalized pagerank algorithm is used to select the most relevant relations. this method only considers the semantic similarity between the question and the relations while ignoring the structural information around each entity node. knowledge embeddings on the whole retrieved ksg are directly computed @xcite question: where did dr martin luther king (m.051cc) get his doctorate m.051cc ## knowledge subgraph partition for better use of the ranking model, we need to partition the knowledge subgraph into several sub-ksgs. as shown in figure 1 , m.051cc is the topic entity of the given question and nodes on the same path from topic entity node m.051cc should be partitioned in the same sub-ksg. in particular, entity nodes in this example graph are denoted by freebase ids. the first sub-ksg (the red dashed line area) is about the education information of m.051cc, which contains the true answer entity node m.0gl5_. the second sub-ksg (the green dashed line area) is about the namesake entity m.076hxb3. it is also a confusing subgraph because it contains tokens like education, which are consistent with the context of the question. therefore, the learning to rank model is expected to distinguish not only irrelevant sub-ksgs, but also confusing ones. algorithm 1: ksg partition to partition related nodes in the same sub-ksg, we propose a knowledge subgraph partition algorithm detailed in algorithm 1. given a question q and its answer entities e q a , we first use the retrieval method proposed by @xcite to obtain a question-specific ksg s, which may contain thousands of answer candidate entities and relationships. e q a is a set containing the ground truth answer entities for question q. then, our proposed algorithm partitions the retrieved ksg into several sub-ksgs serving as inputs to the graph-augmented learning to rank model to select the most relevant sub-ksgs. our algorithm follows the intuition that the answer to the given question is usually found on a multihop path from the topic entity node. in order to keep the size of the sub-ksg moderate, we partition it from the node whose child nodes are all leaf nodes, which is shown in the left of figure 2 . the reason for partitioning from such nodes is two-fold. firstly, if partitioned from a leaf node (see the right of figure 2 ), the sub-ksg will degrade to a sequence and the number of sub-ksgs will be too large. second, if partitioned from a parent node near the root node, the sub-ksg may still contain too much redundant information for a given question. ## graph-augmented learning to rank given a question q and a set of sub-ksgs s @xmath0,1 , ..., s q,n }, we compute the ranking score y representing the relevance of q and s q,i for subgraph ranking. the overall model architecture is shown in figure 3 , which consists of a graph construction module for the input question and the input triples, a biggnn encoder and an enhanced bimpm encoder. ## conclusions in the information retrieval based knowledge graph question answering (kgqa), this paper focuses on a subgraph ranking task with the aim of reducing the size of the coarsely retrieved knowledge subgraph and capturing both local and global interactions between question and sub-ksgs. we propose a knowledge subgraphs (ksg) partition algorithm and a graph-augmented learning to rank model to match-then-rank them. we further validate that reducing the size of knowledge subgraph is beneficial to the subsequent answer selection in an information retrieval based kgqa process. in the future, we will further explore a more effective answer selection model over the small-scale knowledge subgraph selected by our learning to rank model.