29 citations · 286 across the 54 of their papers we have counts for
11 papers · 1 filter
Large Language Models and Knowledge Graphs: Opportunities and Challenges
Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo +13
Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm. This inflection point marks a shift from explicit knowledge representation to a renew…
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
Zhuo Chen, Lingbing Guo, Yin Fang +6
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting assoc…
DeepOnto: A Python Package for Ontology Engineering with Deep Learning
Yuan He, Jiaoyan Chen, Hang Dong +4
Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need…
Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement
Hang Dong, Jiaoyan Chen, Yuan He +1
Mentions of new concepts appear regularly in texts and require automated approaches to harvest and place them into Knowledge Bases (KB), e.g., ontologies and taxonomies. Existing d…
Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
Lingbing Guo, Zhuo Chen, Jiaoyan Chen +3
Recent embedding-based methods have achieved great successes in exploiting entity alignment from knowledge graph (KG) embeddings of multiple modalities. In this paper, we study emb…
An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering
Nan Hu, Yike Wu, Guilin Qi +4
Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP). It is now the consensu…
Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment
Yangning Li, Jiaoyan Chen, Yinghui Li +3
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering. Existing EA methods mostly focus on utilizing the graph structures and entity attri…
Knowledge-augmented Few-shot Visual Relation Detection
Tianyu Yu, Yangning Li, Jiaoyan Chen +8
Visual Relation Detection (VRD) aims to detect relationships between objects for image understanding. Most existing VRD methods rely on thousands of training samples of each relati…
Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking
Hang Dong, Jiaoyan Chen, Yuan He +2
Discovering entity mentions that are out of a Knowledge Base (KB) from texts plays a critical role in KB maintenance, but has not yet been fully explored. The current methods are m…
Language Model Analysis for Ontology Subsumption Inference
Yuan He, Jiaoyan Chen, Ernesto Jiménez-Ruiz +2
Investigating whether pre-trained language models (LMs) can function as knowledge bases (KBs) has raised wide research interests recently. However, existing works focus on simple,…
Dual Box Embeddings for the Description Logic EL++
Mathias Jackermeier, Jiaoyan Chen, Ian Horrocks
OWL ontologies, whose formal semantics are rooted in Description Logic (DL), have been widely used for knowledge representation. Similar to Knowledge Graphs (KGs), ontologies are o…