86 citations · 256 across the 18 of their papers we have counts for
18 papers
Rethinking Memory as Continuously Evolving Connectivity
Jizhan Fang, Buqiang Xu, Zhixian Wang +12
Existing memory-augmented LLM agents often treat memory as a static repository with pre-defined representations and fixed retrieval pipelines, which is brittle in dynamic agentic e…
SkillNet: Create, Evaluate, and Connect AI Skills
Yuan Liang, Ruobin Zhong, Haoming Xu +47
Current AI agents can flexibly invoke tools and execute complex tasks, yet their long-term advancement is hindered by the lack of systematic accumulation and transfer of skills. Wi…
CodeKGC: Code Language Model for Generative Knowledge Graph Construction
Zhen Bi, Jing Chen, Yinuo Jiang +4
Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specificati…
Editing Language Model-based Knowledge Graph Embeddings
Siyuan Cheng, Ningyu Zhang, Bozhong Tian +3
Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deploy…
On Analyzing the Role of Image for Visual-enhanced Relation Extraction
Lei Li, Xiang Chen, Shuofei Qiao +3
Multimodal relation extraction is an essential task for knowledge graph construction. In this paper, we take an in-depth empirical analysis that indicates the inaccurate informatio…
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…
BCRLSP: An Offline Reinforcement Learning Framework for Sequential Targeted Promotion
Fanglin Chen, Xiao Liu, Bo Tang +3
We utilize an offline reinforcement learning (RL) model for sequential targeted promotion in the presence of budget constraints in a real-world business environment. In our applica…
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…
Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction
Tianshu Wang, Hongyu Lin, Cheng Fu +6
Entity matching (EM) is the most critical step for entity resolution (ER). While current deep learningbased methods achieve very impressive performance on standard EM benchmarks, t…
Relphormer: Relational Graph Transformer for Knowledge Graph Representations
Zhen Bi, Siyuan Cheng, Jing Chen +3
Transformers have achieved remarkable performance in widespread fields, including natural language processing, computer vision and graph mining. However, vanilla Transformer archit…
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
Wen Zhang, Xiangnan Chen, Zhen Yao +10
NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. It implements three different series of Knowledge Graph Embedding (KGE) met…
From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
Xin Xie, Ningyu Zhang, Zhoubo Li +5
Knowledge graph completion aims to address the problem of extending a KG with missing triples. In this paper, we provide an approach GenKGC, which converts knowledge graph completi…
Ontology-enhanced Prompt-tuning for Few-shot Learning
Hongbin Ye, Ningyu Zhang, Shumin Deng +5
Few-shot Learning (FSL) is aimed to make predictions based on a limited number of samples. Structured data such as knowledge graphs and ontology libraries has been leveraged to ben…
SQUIRE: A Sequence-to-sequence Framework for Multi-hop Knowledge Graph Reasoning
Yushi Bai, Xin Lv, Juanzi Li +4
Multi-hop knowledge graph (KG) reasoning has been widely studied in recent years to provide interpretable predictions on missing links with evidential paths. Most previous works us…
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
Ningyu Zhang, Xin Xu, Liankuan Tao +19
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base pop…
Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules
Wen Zhang, Shumin Deng, Mingyang Chen +5
Knowledge Graphs (KGs), representing facts as triples, have been widely adopted in many applications. Reasoning tasks such as link prediction and rule induction are important for t…
Interpretable performance analysis towards offline reinforcement learning: A dataset perspective
Chenyang Xi, Bo Tang, Jiajun Shen +3
Offline reinforcement learning (RL) has increasingly become the focus of the artificial intelligent research due to its wide real-world applications where the collection of data ma…
Studying Product Competition Using Representation Learning
Fanglin Chen, Xiao Liu, Davide Proserpio +2
Studying competition and market structure at the product level instead of brand level can provide firms with insights on cannibalization and product line optimization. However, it…