mlsanityData to 7 Oct 2026

LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals

arXiv 2304.14742 ↗First posted 28 Apr 2023cs.AIcited by 6

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.

Builds on

22 of 36 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Neural-Symbolic Models for Logical Queries on Knowledge Graphs16 May

Other references · 21

  1. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  2. 2016Complex Embeddings for Simple Link Predictioncited by 897
  3. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  4. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  5. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
  6. 2015A Review of Relational Machine Learning for Knowledge Graphscited by 264
  7. 2019Quaternion Knowledge Graph Embeddingscited by 175
  8. 2020Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddingscited by 169
  9. 2018Canonical Tensor Decomposition for Knowledge Base Completioncited by 150
  10. 2020Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphscited by 128
  11. 2018Embedding Logical Queries on Knowledge Graphscited by 120
  12. 2020Complex Query Answering with Neural Link Predictorscited by 98
9 more
  1. 2018Incorporating Literals into Knowledge Graph Embeddingscited by 29
  2. 2017KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Featurescited by 25
  3. 2020Convolutional Complex Knowledge Graph Embeddingscited by 21
  4. 2021SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphscited by 17
  5. 2021EvoLearner: Learning Description Logics with Evolutionary Algorithmscited by 17
  6. 2021Convolutional Hypercomplex Embeddings for Link Predictioncited by 15
  7. 2017Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphscited by 14
  8. 2021Learning Concept Lengths Accelerates Concept Learning in ALCcited by 10
  9. 2021Neural Class Expression Synthesiscited by 6

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Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

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Close in wording, no citation either way

  1. 2023Complex Logical Reasoning over Knowledge Graphs using Large Language Models
  2. 2023EFOk-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation
  3. 2023Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases
  4. 2022NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs
  5. 2025The benefits of query-based KGQA systems for complex and temporal questions in LLM era
  6. 2026Toward Multi-Database Query Reasoning for Text2Cypher
  7. 2023Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database
  8. 2024Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning