Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge GraphsDownload PDF

Aug 20, 2021 (edited Sep 24, 2021)NeurIPS 2021 Datasets and Benchmarks Track (Round 2)Readers: Everyone
  • Keywords: knowledge graph, complex query, logical query
  • Abstract: Complex Query Answering (CQA) is an important reasoning task on knowledge graphs. Current CQA learning models have been shown to be able to generalize from atomic operators to more complex formulas, which can be regarded as the combinatorial generalizability. In this paper, we present EFO-1-QA, a new dataset to benchmark the combinatorial generalizability of CQA models by including 301 different queries types, which is 20 times larger than existing datasets. Besides, our benchmark, for the first time, provide a benchmark to evaluate and analyze the impact of different operators and normal forms by using (a) 7 choices of the operator systems and (b) 9 forms of complex queries. Specifically, we provide the detailed study of the combinatorial generalizability of two commonly used operators, i.e., projection and intersection, and justify the impact of the forms of queries given the canonical choice of operators. Our code and data can provide an effective pipeline to benchmark CQA models.
  • Supplementary Material: zip
  • URL: https://github.com/HKUST-KnowComp/EFO-1-QA-benchmark
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