Towards Semantically Enriched Embeddings for Knowledge Graph Completion
Not on the map. Why?
Abstract (arXiv)
Embedding based Knowledge Graph (KG) Completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the semantics underlying the schematic information. In a separate development, a vast amount of information has been captured within the Large Language Models (LLMs) which has revolutionized the field of Artificial Intelligence. KGs could benefit from these LLMs and vice versa. This vision paper discusses the existing algorithms for KG completion based on the variations for generating KG embeddings. It starts with discussing various KG completion algorithms such as transductive and inductive link prediction and entity type prediction algorithms. It then moves on to the algorithms utilizing type information within the KGs, LLMs, and finally to algorithms capturing the semantics represented in different description logic axioms. We conclude the paper with a critical reflection on the current state of work in the community and give recommendations for future directions.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 47 of its 104 references that are in the map. First posted on arXiv 31 Jul 2023.
Builds on
47 of 104 references.
References · 47
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2017Inductive Representation Learning on Large Graphscited by 6,320
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
- 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
- 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
- 2016Complex Embeddings for Simple Link Predictioncited by 902
- 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 898
- 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 894
- 2017Convolutional 2D Knowledge Graph Embeddingscited by 730
- 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 668
35 more
- 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 411
- 2019KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representationcited by 356
- 2019KG-BERT: BERT for Knowledge Graph Completioncited by 324
- 2019Inductive Relation Prediction by Subgraph Reasoningcited by 239
- 2019DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphscited by 175
- 2017A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Networkcited by 143
- 2017Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approachcited by 102
- 2018One-Shot Relational Learning for Knowledge Graphscited by 78
- 2020Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Frameworkcited by 70
- 2020OWL2Vec*: Embedding of OWL Ontologiescited by 63
- 2020Inductive Entity Representations from Text via Link Predictioncited by 62
- 2015Modeling Relation Paths for Representation Learning of Knowledge Basescited by 56
- 2018Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embeddingcited by 53
- 2019EL Embeddings: Geometric construction of models for the Description Logic EL ++cited by 48
- 2021OntoZSL: Ontology-enhanced Zero-shot Learningcited by 45
- 2022From Discrimination to Generation: Knowledge Graph Completion with Generative Transformercited by 45
- 2021Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Conceptscited by 37
- 2022Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completioncited by 32
- 2019A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?cited by 31
- 2020Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphscited by 30
- 2022Relational Message Passing for Fully Inductive Knowledge Graph Completioncited by 30
- 2022An Open Challenge for Inductive Link Prediction on Knowledge Graphscited by 26
- 2022Faithiful Embeddings for EL++ Knowledge Basescited by 25
- 2021Improving Inductive Link Prediction Using Hyper-Relational Factscited by 22
- 2023Dual Box Embeddings for the Description Logic EL++cited by 21
- 2016Knowledge Graph Representation with Jointly Structural and Textual Encodingcited by 17
- 2020Connecting Embeddings for Knowledge Graph Entity Typingcited by 17
- 2022Description Logic EL++ Embeddings with Intersectional Closurecited by 16
- 2020End-to-End Entity Classification on Multimodal Knowledge Graphscited by 12
- 2022RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphscited by 12
- 2023Sem@K: Is my knowledge graph embedding model semantic-aware?cited by 10
- 2017Corpus-level Fine-grained Entity Typingcited by 7
- 2020AutoETER: Automated Entity Type Representation for Knowledge Graph Embeddingcited by 6
- 2022Entity Type Prediction Leveraging Graph Walks and Entity Descriptionscited by 6
- 2017Multi-level Representations for Fine-Grained Typing of Knowledge Base Entitiescited by 4
Built on by
Citing papers per year: 2024: 3.
3 papers.