167 citations · 301 across the 32 of their papers we have counts for
34 papers
HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion
Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang +2
Hyper-relational knowledge graphs (HKGs) extend standard knowledge graphs by associating attribute-value qualifiers to triples, which effectively represent additional fine-grained…
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…
BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering
Jie He, Simon Chi Lok U, Víctor Gutiérrez-Basulto +1
Unsupervised commonsense reasoning (UCR) is becoming increasingly popular as the construction of commonsense reasoning datasets is expensive, and they are inevitably limited in the…
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…
Wiki-based Communities of Interest: Demographics and Outliers
Hiba Arnaout, Simon Razniewski, Jeff Z. Pan
In this paper, we release data about demographic information and outliers of communities of interest. Identified from Wiki-based sources, mainly Wikidata, the data covers 7.5k comm…
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
Mingyang Chen, Wen Zhang, Zhen Yao +4
We propose an entity-agnostic representation learning method for handling the problem of inefficient parameter storage costs brought by embedding knowledge graphs. Conventional kno…
Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs
Mingyang Chen, Wen Zhang, Yuxia Geng +3
Knowledge graphs (KGs) have become valuable knowledge resources in various applications, and knowledge graph embedding (KGE) methods have garnered increasing attention in recent ye…
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Zhuo Chen, Jiaoyan Chen, Wen Zhang +8
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images. However, current…
Transformer-based Entity Typing in Knowledge Graphs
Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang +2
We investigate the knowledge graph entity typing task which aims at inferring plausible entity types. In this paper, we propose a novel Transformer-based Entity Typing (TET) approa…
Relational Message Passing for Fully Inductive Knowledge Graph Completion
Yuxia Geng, Jiaoyan Chen, Jeff Z. Pan +4
In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical cha…
UnCommonSense: Informative Negative Knowledge about Everyday Concepts
Hiba Arnaout, Simon Razniewski, Gerhard Weikum +1
Commonsense knowledge about everyday concepts is an important asset for AI applications, such as question answering and chatbots. Recently, we have seen an increasing interest in t…
Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph
Shumin Deng, Chengming Wang, Zhoubo Li +11
Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. De…
Task-specific Pre-training and Prompt Decomposition for Knowledge Graph Population with Language Models
Tianyi Li, Wenyu Huang, Nikos Papasarantopoulos +2
We present a system for knowledge graph population with Language Models, evaluated on the Knowledge Base Construction from Pre-trained Language Models (LM-KBC) challenge at ISWC 20…
Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph
Yufeng Huang, Zhuo Chen, Jiaoyan Chen +3
Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image. However, previous methods fa…
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection
Zhuo Chen, Yufeng Huang, Jiaoyan Chen +5
Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge. Most existing methods exploit pre-tra…
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning
Zhuo Chen, Yufeng Huang, Jiaoyan Chen +5
Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never appeared during training. One of the most effective and widely used semantic information for zero-s…
Disentangled Ontology Embedding for Zero-shot Learning
Yuxia Geng, Jiaoyan Chen, Wen Zhang +6
Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge representation, and have shown to be quite effective in augmenting Zero-shot Learning (ZSL). Ho…
Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs
Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang +3
Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing in…
Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective
Wen Zhang, Jiaoyan Chen, Juan Li +3
Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and industry. Conventional KG reasoning based on symbolic logic is deterministic, with reasoning re…
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey
Jiaoyan Chen, Yuxia Geng, Zhuo Chen +5
Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision. As sufficient labeled train…
Geometric Models for (Temporally) Attributed Description Logics
Camille Bourgaux, Ana Ozaki, Jeff Z. Pan
In the search for knowledge graph embeddings that could capture ontological knowledge, geometric models of existential rules have been recently introduced. It has been shown that c…
Zero-shot Visual Question Answering using Knowledge Graph
Zhuo Chen, Jiaoyan Chen, Yuxia Geng +3
Incorporating external knowledge to Visual Question Answering (VQA) has become a vital practical need. Existing methods mostly adopt pipeline approaches with different components f…
Benchmarking Knowledge-driven Zero-shot Learning
Yuxia Geng, Jiaoyan Chen, Xiang Zhuang +5
External knowledge (a.k.a. side information) plays a critical role in zero-shot learning (ZSL) which aims to predict with unseen classes that have never appeared in training data.…