mlsanityData to 7 Oct 2026

CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding

arXiv 2112.10067 ↗First posted 19 Dec 2021cs.LG cs.AI cs.SIcited by 10

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Abstract (arXiv)

Entity type prediction is an important problem in knowledge graph (KG) research. A new KG entity type prediction method, named CORE (COmplex space Regression and Embedding), is proposed in this work. The proposed CORE method leverages the expressive power of two complex space embedding models; namely, RotatE and ComplEx models. It embeds entities and types in two different complex spaces using either RotatE or ComplEx. Then, we derive a complex regression model to link these two spaces. Finally, a mechanism to optimize embedding and regression parameters jointly is introduced. Experiments show that CORE outperforms benchmarking methods on representative KG entity type inference datasets. Strengths and weaknesses of various entity type prediction methods are analyzed.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 12 of its 32 references that are in the map. First posted on arXiv 19 Dec 2021.

Builds on

12 of 32 references.

References · 12

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  3. 2016Complex Embeddings for Simple Link Predictioncited by 897
  4. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  5. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  6. 2015Holographic Embeddings of Knowledge Graphscited by 181
  7. 2021Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Conceptscited by 37
  8. 2020Connecting Embeddings for Knowledge Graph Entity Typingcited by 17
  9. 2020Type-augmented Relation Prediction in Knowledge Graphscited by 12
  10. 2021KGBoost: A Classification-based Knowledge Base Completion Method with Negative Samplingcited by 10
  11. 2016Corpus-level Fine-grained Entity Typing Using Contextual Informationcited by 7
  12. 2020Knowledge graph based methods for record linkagecited by 1

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Cited by year

Citing papers per year: 2022: 3; 2023: 4; 2024: 2; 2025: 1.

10 papers.

  1. 2022Knowledge Graph Embedding: A Survey from the Perspective of Representation Spacescited by 41
  2. 2024Time Sensitive Knowledge Editing through Efficient Finetuningcited by 14
  3. 2022Transformer-based Entity Typing in Knowledge Graphscited by 13
  4. 2022CompoundE: Knowledge Graph Embedding with Translation, Rotation and Scaling Compound Operationscited by 10
  5. 2023Knowledge Graph Embedding: An Overviewcited by 10
  6. 2023Knowledge Graph Embedding with 3D Compound Geometric Transformationscited by 3
  7. 2023AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relationscited by 2
  8. 2023Multi-view Contrastive Learning for Entity Typing over Knowledge Graphscited by 2
  9. 2025Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methodscited by 1
  10. 2024COTET: Cross-view Optimal Transport for Knowledge Graph Entity Typingcited by 0