Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models
Knowledge Graph Embedding models, representing entities and edges in a low-dimensional space, have been extremely successful at solving tasks related to completing and exploring Knowledge Graphs (KGs). One of the key aspects of training most of these models is teaching to discriminate between true statements positives and false ones (negatives). However, the way in which negatives can be defined is not trivial, as facts missing from the KG are not necessarily false and a set of ground truth negatives is hardly ever given. This makes synthetic negative generation a necessity. Different generation strategies can heavily affect the quality of the embeddings, making it a primary aspect to consider. We revamp a strategy that generates corruptions during training respecting the domain and range of relations, we extend its capabilities and we show our methods bring substantial improvement (+10% MRR) for standard benchmark datasets and over +150% MRR for a larger ontology-backed dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Remining Hard Negatives for Generative Pseudo Labeled Domain Adaptation
Dense retrievers have demonstrated significant potential for neural information retrieval; however, they exhibit a lack of robustness to domain shifts, thereby limiting their efficacy in zero-shot settings across diverse…
Domain AdaptationInformation RetrievalKnowledge DistillationOn Synthetic Data Strategies for Domain-Specific Generative Retrieval
This paper investigates synthetic data generation strategies in developing generative retrieval models for domain-specific corpora, thereby addressing the scalability challenges inherent in manually annotating in-domain …
Document RankingRetrievalSynthetic Data GenerationSynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual Representations
Contrastive learning has become a dominant approach in self-supervised visual representation learning. Hard negatives - samples closely resembling the anchor - are key to enhancing learned representations' discriminative…
Contrastive LearningImage ClassificationImage SegmentationInstance Segmentation+8BiCA: Effective Biomedical Dense Retrieval with Citation-Aware Hard Negatives
Hard negatives are essential for training effective retrieval models. Hard-negative mining typically relies on ranking documents using cross-encoders or static embedding models based on similarity metrics such as cosine …
Domain AdaptationFALCON: False-Negative Aware Learning of Contrastive Negatives in Vision-Language Pretraining
False negatives pose a critical challenge in vision-language pretraining (VLP) due to the many-to-many correspondence between images and texts in large-scale datasets. These false negatives introduce conflicting supervis…
cross-modal alignment