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
Inductive Logical Query Answering in Knowledge Graphs
tgebhart/sheaf_kg_transind
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Exploring the efficiency--effectiveness trade-off, we find the inductive relational structure representation method generally achieves higher performance, while the inductive node representation method is able to answer complex queries in the inference-only regime without any training on queries and scales to graphs of millions of nodes.
NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs
lhrlab/nqe
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AAAI 2023
Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs).
Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization
bys0318/qto
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QTO finds the optimal solution by a forward-backward propagation on the tree-like computation graph, i. e., query computation tree.
Logical Message Passing Networks with One-hop Inference on Atomic Formulas
On top of the query graph, we propose the Logical Message Passing Neural Network (LMPNN) that connects the local one-hop inferences on atomic formulas to the global logical reasoning for complex query answering.
Sequential Query Encoding For Complex Query Answering on Knowledge Graphs
Instead of parameterizing and executing the computational graph, SQE first uses a search-based algorithm to linearize the computational graph to a sequence of tokens and then uses a sequence encoder to compute its vector representation.
Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
hkust-knowcomp/ceqa
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NeurIPS 2023
Traditional neural complex query answering (CQA) approaches mostly work on entity-centric KGs.
Knowledge Graph Reasoning over Entities and Numerical Values
To address the difference between entities and numerical values, we also propose the framework of Number Reasoning Network (NRN) for alternatively encoding entities and numerical values into separate encoding structures.
$\text{EFO}_{k}$-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation
Learning-based methods are essential because they are capable of generalizing over unobserved knowledge.
Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs
yaooxu/q2t
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However, these methods train KG embeddings and neural set operators concurrently on both simple (one-hop) and complex (multi-hop and logical) queries, which causes performance degradation on simple queries and low training efficiency.
