3 citations · 8 across the 11 of their papers we have counts for
13 papers
ChronoSynth: Constraint-Aware Question Synthesis over Temporal Knowledge Graphs
Feng Liang, Weixin Zeng, Runhao Zhao +3
Knowledge graph question generation (KGQG) generates natural-language questions from structured graph evidence and a target answer. When the input graph is temporal, a valid questi…
Identification and Estimation of Treatment Effects from Network Data with Confounded Social Links
Licheng Liu
In observational network data, the adoption of a treatment and the formation of social ties often depend on the same characteristics that affect outcomes. We propose a generalized…
Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference
Huang Peng, Jiuyang Tang, Weixin Zeng +2
Large language models (LLMs) have achieved strong performance across a wide range of language-based tasks by leveraging both extensive parametric knowledge and in-context learning…
Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge
Runhao Zhao, Weixin Zeng, Wentao Zhang +4
Domain-specific knowledge graphs (DKGs) are critical yet often suffer from limited coverage compared to General Knowledge Graphs (GKGs). Existing tasks to enrich DKGs rely primaril…
NeSTR: A Neuro-Symbolic Abductive Framework for Temporal Reasoning in Large Language Models
Feng Liang, Weixin Zeng, Runhao Zhao +1
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, temporal reasoning, particularly under comp…
Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection
Hao Guo, Zihan Ma, Zhi Zeng +4
Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake n…
Open Knowledge Base Canonicalization with Multi-task Learning
Bingchen Liu, Huang Peng, Weixin Zeng +3
The construction of large open knowledge bases (OKBs) is integral to many knowledge-driven applications on the world wide web such as web search. However, noun phrases and relation…
Open Knowledge Base Canonicalization with Multi-task Unlearning
Bingchen Liu, Shihao Hou, Weixin Zeng +3
The construction of large open knowledge bases (OKBs) is integral to many applications in the field of mobile computing. Noun phrases and relational phrases in OKBs often suffer fr…
Multi-modal Entity Alignment in Hyperbolic Space
Hao Guo, Jiuyang Tang, Weixin Zeng +2
Many AI-related tasks involve the interactions of data in multiple modalities. It has been a new trend to merge multi-modal information into knowledge graph(KG), resulting in multi…
Towards Entity Alignment in the Open World: An Unsupervised Approach
Weixin Zeng, Xiang Zhao, Jiuyang Tang +3
Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs). It is a pivotal step for integrating KGs to increase knowledge coverage and qual…
Reinforcement Learning based Collective Entity Alignment with Adaptive Features
Weixin Zeng, Xiang Zhao, Jiuyang Tang +2
Entity alignment (EA) is the task of identifying the entities that refer to the same real-world object but are located in different knowledge graphs (KGs). For entities to be align…
Degree-Aware Alignment for Entities in Tail
Weixin Zeng, Xiang Zhao, Wei Wang +2
Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge.…
Collective Entity Alignment via Adaptive Features
Weixin Zeng, Xiang Zhao, Jiuyang Tang +1
Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and in…