A comprehensive overview of knowledge graph completion
T Shen, F Zhang, J Cheng - Knowledge-Based Systems, 2022 - Elsevier
Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …
downstream knowledge-aware tasks (such as recommendation and intelligent question …
SimKGC: Simple contrastive knowledge graph completion with pre-trained language models
Knowledge graph completion (KGC) aims to reason over known facts and infer the missing
links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations …
links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations …
Fusing topology contexts and logical rules in language models for knowledge graph completion
Abstract Knowledge graph completion (KGC) aims to infer missing facts based on the
observed ones, which is significant for many downstream applications. Given the success of …
observed ones, which is significant for many downstream applications. Given the success of …
Geodesic graph neural network for efficient graph representation learning
Abstract Graph Neural Networks (GNNs) have recently been applied to graph learning tasks
and achieved state-of-the-art (SOTA) results. However, many competitive methods run …
and achieved state-of-the-art (SOTA) results. However, many competitive methods run …
Meta-knowledge transfer for inductive knowledge graph embedding
Knowledge graphs (KGs) consisting of a large number of triples have become widespread
recently, and many knowledge graph embedding (KGE) methods are proposed to embed …
recently, and many knowledge graph embedding (KGE) methods are proposed to embed …
Disentangled ontology embedding for zero-shot learning
Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge
representation, and have shown to be quite effective in augmenting Zero-shot Learning …
representation, and have shown to be quite effective in augmenting Zero-shot Learning …
Bertnet: Harvesting knowledge graphs from pretrained language models
Symbolic knowledge graphs (KGs) have been constructed either by expensive human
crowdsourcing or with domain-specific complex information extraction pipelines. The …
crowdsourcing or with domain-specific complex information extraction pipelines. The …
From discrimination to generation: Knowledge graph completion with generative transformer
Knowledge graph completion aims to address the problem of extending a KG with missing
triples. In this paper, we provide an approach GenKGC, which converts knowledge graph …
triples. In this paper, we provide an approach GenKGC, which converts knowledge graph …
Scientific language models for biomedical knowledge base completion: an empirical study
Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases,
drugs, and genes. Predicting missing links in these graphs can boost many important …
drugs, and genes. Predicting missing links in these graphs can boost many important …
Relational message passing for fully inductive knowledge graph completion
In knowledge graph completion (KGC), predicting triples involving emerging entities and/or
relations, which are unseen when the KG embeddings are learned, has become a critical …
relations, which are unseen when the KG embeddings are learned, has become a critical …