14 citations · 46 across the 27 of their papers we have counts for
14 papers · 1 filter
Event Ontology Expansion via LLM-Based Conceptualization
Weicheng Ren, Zixuan Li, Long Bai +3
Event ontology expansion aims to discover emerging event types from data and extend them to appropriate positions in the existing event ontology.. Existing methods typically cluste…
G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models
Long Bai, Zixuan Li, Xiaolong Jin +3
Forecasting over Temporal Knowledge Graphs (TKGs) which predicts future facts based on historical ones has received much attention. Recent studies have introduced Large Language Mo…
Towards Robust Universal Information Extraction: Benchmark, Evaluation, and Solution
Jizhao Zhu, Akang Shi, Zixuan Li +4
In this paper, we aim to enhance the robustness of Universal Information Extraction (UIE) by introducing a new benchmark dataset, a comprehensive evaluation, and a feasible solutio…
Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction
Wenxuan Liu, Zixuan Li, Long Bai +5
Developing a general-purpose extraction system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the challenge comes from…
KnowCoder-X: Boosting Multilingual Information Extraction via Code
Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu +7
Empirical evidence indicates that LLMs exhibit spontaneous cross-lingual alignment. However, although LLMs show promising cross-lingual alignment in Information Extraction (IE), a…
Temporal Knowledge Graph Question Answering: A Survey
Miao Su, Zixuan Li, Zhuo Chen +3
Knowledge Base Question Answering (KBQA) has been a long-standing field to answer questions based on knowledge bases. Recently, the evolving dynamics of knowledge have attracted a…
Self-Improvement Programming for Temporal Knowledge Graph Question Answering
Zhuo Chen, Zhao Zhang, Zixuan Li +4
Temporal Knowledge Graph Question Answering (TKGQA) aims to answer questions with temporal intent over Temporal Knowledge Graphs (TKGs). The core challenge of this task lies in und…
Unlocking the Power of Large Language Models for Entity Alignment
Xuhui Jiang, Yinghan Shen, Zhichao Shi +6
Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on co…
An In-Context Schema Understanding Method for Knowledge Base Question Answering
Yantao Liu, Zixuan Li, Xiaolong Jin +5
The Knowledge Base Question Answering (KBQA) task aims to answer natural language questions based on a given knowledge base. Recently, Large Language Models (LLMs) have shown stron…
ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction
Zhilei Hu, Zixuan Li, Daozhu Xu +5
Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes,…
Nested Event Extraction upon Pivot Element Recogniton
Weicheng Ren, Zixuan Li, Xiaolong Jin +6
Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. Nested events involve a kind of Pivot Elem…
Semantic Structure Enhanced Event Causality Identification
Zhilei Hu, Zixuan Li, Xiaolong Jin +4
Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts. This is a very challenging task, because causal relations are usually e…
Rich Event Modeling for Script Event Prediction
Long Bai, Saiping Guan, Zixuan Li +3
Script is a kind of structured knowledge extracted from texts, which contains a sequence of events. Based on such knowledge, script event prediction aims to predict the subsequent…
Integrating Deep Event-Level and Script-Level Information for Script Event Prediction
Long Bai, Saiping Guan, Jiafeng Guo +3
Scripts are structured sequences of events together with the participants, which are extracted from the texts.Script event prediction aims to predict the subsequent event given the…