Knowledge Graph Embedding Based Question Answering

By Xiao Huang, Jingyuan Zhang, Dingcheng Li, Ping Li. Web Search and Data Mining, 2019

637 citations recorded by the source corpus.

Question answering over knowledge graph (QA-KG) aims to use facts in the knowledge graph (KG) to answer natural language questions. It helps end users more efficiently and more easily access the substantial and valuable knowledge in the KG, without knowing its data structures. QA-KG is a nontrivial problem since capturing the semantic meaning of natural language is difficult for a machine. Meanwhile, many knowledge graph embedding methods have been proposed. The key idea is to represent each predicate/entity as a low-dimensional vector, such that the relation information in the KG could be preserved. The learned vectors could benefit various applications such as KG completion and recommender systems. In this paper, we explore to use them to handle the QA-KG problem. However, this remains a challenging task since a predicate could be expressed in different ways in natural language questions. Also, the ambiguity of entity names and partial names makes the number of possible answers large. To bridge the gap, we propose an effective Knowledge Embedding based Question Answering (KEQA) framework. We focus on answering the most common types of questions, i.e., simple questions, in which each question could be answered by the machine straightforwardly if its single head entity and single predicate are correctly identified. To answer a simple question, instead of inferring its head entity and predicate directly, KEQA targets at jointly recovering the question's head entity, predicate, and tail entity representations in the KG embedding spaces. Based on a carefully-designed joint distance metric, the three learned vectors' closest fact in the KG is returned as the answer. Experiments on a widely-adopted benchmark demonstrate that the proposed KEQA outperforms the state-of-the-art QA-KG methods.

View Paper: https://www.semanticscholar.org/paper/7572aefcd241ec76341addcb2e2e417587cb2e4c

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Citations in the corpus

Relational Memory-Augmented Language Models (2022, background): “, 2019), question answering (Zhang et al., 2018; Huang et al., 2019; Hixon et al., 2015), dialogue systems (Zhou et al.”
Knowledge Graphs (2020, unknown): “In the context of knowledge graphs, machine learning can either be used for for directly refining a knowledge graph [399] (discussed further in Section 8); or for downstream tasks using the knowledge graph, such as recommendation [574], information extraction [532], question answering [254], query r”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “In their question answering system, Huang et al. [104] considered simple natural language questions that can be answered correctly by predicting the head entity and the relation mentioned in the question.”
Unifying Large Language Models and Knowledge Graphs: A Roadmap (2023, background): “These embeddings contain both semantic and structural information of KGs, which can be utilized for various tasks such as question answering [180], reasoning [38], and recommendation [181].”
Knowledge Graphs: Opportunities and Challenges (2023, background): “Focusing on simple questions, Huang et al. (2019) proposed a knowledge graph embedding-based question-answering system (KEQA).”
Neural, symbolic and neural-symbolic reasoning on knowledge graphs (2021, methodology): “Finally, for each candidate triplet, KEQA minimizes the Euclidean distance of the predicted embedding between the candidate and the ground truth, in head entity, predicate, and answer.”
A Survey on Knowledge Graph-Based Recommender Systems (2020, methodology): “To date, KGs have been created and applied in multiple scenarios, including search engines, recommender systems, Question Answering system [22], relation detection [23], etc.”
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction (2019, background): “2019), question answering (Huang et al. 2019), and recommendation systems (Wang et al.”