LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals
On the map: LLM reasoning → Knowledge- and tool-augmented reasoning
Abstract (arXiv)
Most real-world knowledge graphs, including Wikidata, DBpedia, and Yago are incomplete. Answering queries on such incomplete graphs is an important, but challenging problem. Recently, a number of approaches, including complex query decomposition (CQD), have been proposed to answer complex, multi-hop queries with conjunctions and disjunctions on such graphs. However, all state-of-the-art approaches only consider graphs consisting of entities and relations, neglecting literal values. In this paper, we propose LitCQD -- an approach to answer complex, multi-hop queries where both the query and the knowledge graph can contain numeric literal values: LitCQD can answer queries having numerical answers or having entity answers satisfying numerical constraints. For example, it allows to query (1)~persons living in New York having a certain age, and (2)~the average age of persons living in New York. We evaluate LitCQD on query types with and without literal values. To evaluate LitCQD, we generate complex, multi-hop queries and their expected answers on a version of the FB15k-237 dataset that was extended by literal values.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 22 of its 36 references that are in the map. First posted on arXiv 28 Apr 2023.
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Other references · 21
- 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
- 2016Complex Embeddings for Simple Link Predictioncited by 897
- 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
- 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
- 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
- 2015A Review of Relational Machine Learning for Knowledge Graphscited by 264
- 2019Quaternion Knowledge Graph Embeddingscited by 175
- 2020Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddingscited by 169
- 2018Canonical Tensor Decomposition for Knowledge Base Completioncited by 150
- 2020Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphscited by 128
- 2018Embedding Logical Queries on Knowledge Graphscited by 120
- 2020Complex Query Answering with Neural Link Predictorscited by 98
9 more
- 2018Incorporating Literals into Knowledge Graph Embeddingscited by 29
- 2017KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Featurescited by 25
- 2020Convolutional Complex Knowledge Graph Embeddingscited by 21
- 2021SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphscited by 17
- 2021EvoLearner: Learning Description Logics with Evolutionary Algorithmscited by 17
- 2021Convolutional Hypercomplex Embeddings for Link Predictioncited by 15
- 2017Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphscited by 14
- 2021Learning Concept Lengths Accelerates Concept Learning in ALCcited by 10
- 2021Neural Class Expression Synthesiscited by 6
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Citing papers per year: 2024: 2; 2025: 2; 2026: 2.
6 papers.
- 2024A Foundation Model for Zero-shot Logical Query Reasoningcited by 13
- 2025Top Ten Challenges Towards Agentic Neural Graph Databasescited by 13
- 2024Adaptive Stochastic Weight Averagingcited by 4
- 2025CQD-SHAP: Explainable Complex Query Answering via Shapley Valuescited by 0
- 2026NGDBench: Towards Neural Graph Data Managementcited by 0
- 2026Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphscited by 0
- 2026Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs27 Aug 2026
- 2026NGDBench: Towards Neural Graph Data Management27 Feb 2026
- 2025CQD-SHAP: Explainable Complex Query Answering via Shapley Values17 Oct 2025
- 2025Top Ten Challenges Towards Agentic Neural Graph Databases24 Jan 2025
- 2024Adaptive Stochastic Weight Averaging27 Jun 2024
- 2024A Foundation Model for Zero-shot Logical Query Reasoning10 Apr 2024
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- 2023Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases
- 2022NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs
- 2025The benefits of query-based KGQA systems for complex and temporal questions in LLM era
- 2026Toward Multi-Database Query Reasoning for Text2Cypher
- 2023Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database
- 2024Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning