13 citations · 35 across the 15 of their papers we have counts for
15 papers
LLM-Based Knowledge Graph Completion Combining Discrete Structural Coding with Similar Entity Information
Jiaqi Wang, Dongying Lin, Yang Yang +3
Knowledge graph completion requires models to use both textual descriptions and relational structure. Existing LLM-based methods either encode KG structure as discrete tokens or re…
Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation
Zichun Jin, Zihan Zhou, Yinan Liu +2
Sequential recommendation predicts the next item from a user's interaction history, but not every interaction is equally informative. Real logs combine persistent preferences with…
Enhancing LLMs in Predictive Political QA with Semi-Structured Data
Yinan Liu, Zihan Zhou, Zichun Jin +3
Predictive political question answering (QA), such as predicting how a political actor will vote, goes beyond factual lookup. External political resources offer rich historical evi…
Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling
Yinan Liu, Wenjin Xu, Zhiyuan Zha +2
Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems. However, despite all this, KGs are often very…
Joint Knowledge Base Completion and Question Answering by Combining Large Language Models and Small Language Models
Yinan Liu, Dongying Lin, Sigang Luo +2
Knowledge Bases (KBs) play a key role in various applications. As two representative KB-related tasks, knowledge base completion (KBC) and knowledge base question answering (KBQA)…
OntoTKGE: Ontology-Enhanced Temporal Knowledge Graph Extrapolation
Dongying Lin, Yinan Liu, Shengwei tang +2
Temporal knowledge graph (TKG) extrapolation is an important task that aims to predict future facts through historical interaction information within KG snapshots. A key challenge…
DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphs
Zihan Zhou, Yinan Liu, Yuyang Xie +3
The global shortage and uneven distribution of medical expertise continue to hinder equitable access to accurate diagnostic care. While existing intelligent diagnostic system have…
Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking
Hang Dong, Jiaoyan Chen, Yuan He +2
Discovering entity mentions that are out of a Knowledge Base (KB) from texts plays a critical role in KB maintenance, but has not yet been fully explored. The current methods are m…
Joint Open Knowledge Base Canonicalization and Linking
Yinan Liu, Wei Shen, Yuanfei Wang +3
Open Information Extraction (OIE) methods extract a large number of OIE triples (noun phrase, relation phrase, noun phrase) from text, which compose large Open Knowledge Bases (OKB…
Low-resource Personal Attribute Prediction from Conversation
Yinan Liu, Hu Chen, Wei Shen +1
Personal knowledge bases (PKBs) are crucial for a broad range of applications such as personalized recommendation and Web-based chatbots. A critical challenge to build PKBs is extr…
Personal Attribute Prediction from Conversations
Yinan Liu, Hu Chen, Wei Shen
Personal knowledge bases (PKBs) are critical to many applications, such as Web-based chatbots and personalized recommendation. Conversations containing rich personal knowledge can…
Multi-View Clustering for Open Knowledge Base Canonicalization
Wei Shen, Yang Yang, Yinan Liu
Open information extraction (OIE) methods extract plenty of OIE triples <noun phrase, relation phrase, noun phrase> from unstructured text, which compose large open knowledge bases…
Entity Linking Meets Deep Learning: Techniques and Solutions
Wei Shen, Yuhan Li, Yinan Liu +3
Entity linking (EL) is the process of linking entity mentions appearing in web text with their corresponding entities in a knowledge base. EL plays an important role in the fields…
Subjective Bias in Abstractive Summarization
Lei Li, Wei Liu, Marina Litvak +4
Due to the subjectivity of the summarization, it is a good practice to have more than one gold summary for each training document. However, many modern large-scale abstractive summ…
On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity Search
Adam Hare, Yu Chen, Yinan Liu +2
Despite the recent successes of deep learning in natural language processing (NLP), there remains widespread usage of and demand for techniques that do not rely on machine learning…