SParseQA: Sequential word reordering and parsing for answering complex natural language questions over knowledge graphs

• Published in: Knowledge-based systems 2022-01, Vol.235, p.107626, Article 107626
• Main Authors: Bakhshi, Mahdi, Nematbakhsh, Mohammadali, Mohsenzadeh, Mehran, Rahmani, Amir Masoud
• Format: Article
• Language: English
• Subjects: Graphs; Knowledge representation; Natural language; Natural language processing; Queries; Questions; RDF complex question answering; Relation pattern-based similarity; Sequential word reordering and parsing; Similarity; Taxonomy; Uncertain question graph construction
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2021.107626

One of the effective approaches for answering natural language questions (NLQs) over knowledge graphs consists of two main stages. It first creates a query graph based on the NLQ and then matches this graph over the knowledge graph to construct a structured query. An obstacle in the first stage is the need to build question interpretations with candidate resources, even if some implicit phrases exist in the sentence. In the second stage, a serious problem is to map diverse NLQ relations to their corresponding predicates. To overcome these problems, in this paper, we propose a novel sequential word parsing-based method to construct and refine an uncertain question graph that is disambiguated directly over the knowledge graph. Instead of relying on the syntactic dependency relations and some predefined rules that recognize the relations and their arguments, we consider the identified entities and variables in the NLQ as well as their corresponding place in the structure of a query graph pattern to build question triples. First, by leveraging the ordered dependency tree of an NLQ, sentence words are reordered. Then the question graph structure is constructed by parsing the new sequence backward, starting from the identified items. Subsequently, the question graph is refined by eliminating the useless elements. Additionally, to improve the relation similarity measure in the graph similarity process, we exploit the knowledge hidden in a relation pattern taxonomy. Experimental studies over several benchmarks demonstrate that our proposed approach is effective as it achieves promising results in answering the complex NLQs. •A sequential word reordering & parsing method builds the interpretations of NLQs.•The question graph is constructed and refined considering the query graph pattern.•The ordered dependency tree is leveraged to provide a more stable word ordering.•A graph similarity process is proposed that exploits a relation pattern taxonomy.•Extensive experiments reveal significant progress in answering the complex NLQs.