Dynamic matching-prototypical learning for noisy few-shot relation classification.

DRIVE: An adjustable parallel architecture based on evidence awareness for fake news detection.

Document-level Relation Extraction with Relation Correlations.

An effective knowledge graph entity alignment model based on multiple information.

A semi-supervised neighborhood matching model for global entity alignment.

Reinforcement learning with dynamic completion for answering multi-hop questions over incomplete knowledge graph.

Stepwise relation prediction with dynamic reasoning network for multi-hop knowledge graph question answering.

Cross-lingual knowledge graph entity alignment based on relation awareness and attribute involvement.

Path-based multi-hop reasoning over knowledge graph for answering questions via adversarial reinforcement learning.

Cross-lingual knowledge graph entity alignment by aggregating extensive structures and specific semantics.

Incorporating anticipation embedding into reinforcement learning framework for multi-hop knowledge graph question answering.

Is Information Extraction Solved by ChatGPT? An Analysis of Performance, Evaluation Criteria, Robustness and Errors.

NLIRE: A Natural Language Inference method for Relation Extraction.

Distantly Supervised Relation Extraction via Recursive Hierarchy-Interactive Attention and Entity-Order Perception.

Distantly Supervised Relation Extraction using Global Hierarchy Embeddings and Local Probability Constraints.

Meta-learning for few-shot time series forecasting.

Knowledge-aware hierarchical attention network for recommendation.

Document-level Relation Extraction with Relation Correlations.

Synchronously tracking entities and relations in a syntax-aware parallel architecture for aspect-opinion pair extraction.

Simple Question Answering over Knowledge Graph Enhanced by Question Pattern Classification.

Distantly Supervised Relation Extraction via Recursive Hierarchy-Interactive Attention and Entity-Order Perception.

Meta-path-based outlier detection in heterogeneous information network.

A reliability and link analysis based method for mining domain experts in dynamic social networks.

DEDSC: A Domain Expert Discovery Method Based on Structure and Content.

Structure2Content: An Incremental Method for Detecting Outlier Correlation in Heterogeneous Network.

Detecting outlier pairs in complex network based on link structure and semantic relationship.

Building text classifiers using positive, unlabeled and 'outdated' examples.

基于人工免疫算法的增量式用户兴趣挖掘 (Incremental User Interest Mining Based on Artificial Immune Algorithm).

Clustering-Based Topical Web Crawling for Topic-Specific Information Retrieval Guided by Incremental Classifier.

Topical web crawling for domain-specific resource discovery enhanced by selectively using link-context.

A novel incremental conceptual hierarchical text clustering method using CFu-tree.

Clustering-based Method for Positive and Unlabeled Text Categorization Enhanced by Improved TFIDF.

Clustering-based topical Web crawling using CFu-tree guided by link-context.

PU text classification enhanced by term frequency-inverse document frequency-improved weighting.

Focused crawling enhanced by CBP-SLC.