Huajun Chen

Knowledge-based Transfer Learning Explanation

2018 · arXiv (Cornell University)

Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at …

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

2019 · Zurich Open Repository and Archive (University of Zurich)

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and …

Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

2019

Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 …

Ontology-guided Semantic Composition for Zero-Shot Learning

2020 · arXiv (Cornell University)

Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information. In this study, …

AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba

2021

Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search. Prior conceptual graph construction approaches typically extract high-frequent, coarse-grained, and time-invariant concepts from formal texts such as Wikipedia. …

NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs

2022 · arXiv (Cornell University)

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) methods, including conventional KGEs, GNN-based KGEs, and Rule-based KGEs. With a unified …

Tele-Knowledge Pre-training for Fault Analysis

2022 · arXiv (Cornell University)

In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product …

Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting

2022 · arXiv (Cornell University)

We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this problem, a model trained on an existing KG …

Knowledge Rumination for Pre-trained Language Models

2023 · arXiv (Cornell University)

Previous studies have revealed that vanilla pre-trained language models (PLMs) lack the capacity to handle knowledge-intensive NLP tasks alone; thus, several works have attempted to integrate external knowledge into PLMs. However, despite the promising outcome, …

EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models

2024 · arXiv (Cornell University)

In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of Large Language Models (LLMs). To construct high-quality instruction datasets, many instruction processing approaches have been …

Knowledge Mechanisms in Large Language Models: A Survey and Perspective

2024

Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang. Findings of the Association for Computational Linguistics: EMNLP 2024. …

RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment

2024 · arXiv (Cornell University)

Large Language Models (LLMs) like GPT-4, MedPaLM-2, and Med-Gemini achieve performance competitively with human experts across various medical benchmarks. However, they still face challenges in making professional diagnoses akin to physicians, particularly in efficiently gathering …

Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking

2024 · arXiv (Cornell University)

Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud hanging over the LLM skyscraper. Structural knowledge prompting (SKP) …

SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement

2025

Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume …

Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding

2018

Distant supervision is an effective method to generate large scale labeled data for relation extraction, which assumes that if a pair of entities appears in some relation of a Knowledge Graph (KG), all sentences containing …

Document-level Relation Extraction as Semantic Segmentation

2021

Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the entities independently, regardless of global information among relational triples. This paper approaches the …

KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction

2022 · Proceedings of the ACM Web Conference 2022

Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into the input and transform a classification task into a masked language …