Complex Query Answering
19 papers with code • 6 benchmarks • 5 datasets
This task is concerned with answering complex queries over incomplete knowledge graphs. In the most simple case, the task is reduced to link prediction: a 1-hop query for predicting the existence of an edge between a pair of nodes. Complex queries are concerned with other structures between nodes, such as 2-hop and 3-paths, and intersecting paths with intermediate variables.
FB15k
FB15k-237
NELL
Most implemented papers
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
snap-stanford/KGReasoning
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NeurIPS 2020
Logical operations are performed in the embedding space by neural operators over the probabilistic embeddings.
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Our main insight is that queries can be embedded as boxes (i. e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query.
Embedding Logical Queries on Knowledge Graphs
williamleif/graphqembed
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NeurIPS 2018
Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities.
Logic Embeddings for Complex Query Answering
Answering logical queries over incomplete knowledge bases is challenging because: 1) it calls for implicit link prediction, and 2) brute force answering of existential first-order logic queries is exponential in the number of existential variables.
Complex Query Answering with Neural Link Predictors
uclnlp/cqd
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ICLR 2021
Finally, we demonstrate that it is possible to explain the outcome of our model in terms of the intermediate solutions identified for each of the complex query atoms.
Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs
hkust-knowcomp/efo-1-qa-benchmark
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Besides, our work, for the first time, provides 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.
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning.
QubitE: Qubit Embedding for Knowledge Graph Completion
linxueyuanstdio/qubite
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ACL ARR November 2021
Quantum-based KGEs utilise variational quantum circuits for link prediction and score triples via the probability distribution of measuring the qubit states.
Query2Particles: Knowledge Graph Reasoning with Particle Embeddings
hkust-knowcomp/query2particles
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Findings (NAACL) 2022
The query embedding method is proposed to answer these queries by jointly encoding queries and entities to the same embedding space.
Neural-Symbolic Models for Logical Queries on Knowledge Graphs
DeepGraphLearning/GNN-QE
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ICML 2022
Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning.
