The Effect of Semantic Knowledge Graph Richness on Embedding ...

In this paper, our central focus is to investigate the impact of the semantic richness of knowledge graphs on the effectiveness of such.

Knowledge graph embedding based on semantic hierarchy

Experiments show that compared with other models, the proposed model improves the knowledge graph link prediction index Hits@10% by about 10% and the accuracy ...

What is a semantic knowledge graph? - metaphacts Blog

While a knowledge graph can exist without an ontology, an ontology is often represented in a knowledge graph because of the natural human desire ...

What is a Knowledge Graph? Exploring Its Role in AI by Kumo.ai

The strength of knowledge graph embeddings lies in their ability to capture the semantic relationships between entities, thus enabling more ...

Do Embeddings Actually Capture Knowledge Graph Semantics?

... A key finding is that in most datasets, the semantic representation of some entities is easier to grasp than for other ones. For instance, the task of ...

A novel model for relation prediction in knowledge graphs exploiting ...

Previous studies indicate that both structural features and semantic information are meaningful for predicting missing relations in knowledge ...

Embedding Knowledge Graphs with Semantic-Guided Walk

ATTWALK orchestrates a two-step workflow by first evaluating neighbors' semantic weights using graph attention networks for each entity, then exploring the ...

Multimodal Reasoning with Multimodal Knowledge Graph - arXiv

The primary benefit of MMKG lies in their integration of additional modalities into traditional KGs. By associating entities with related images ...

Comprehensive Analysis of Knowledge Graph Embedding ... - MDPI

KGs provide a concise and intuitive abstraction for a variety of domains, where edges capture semantic relations between the entities inherent in social data, ...

Learning structured embeddings of knowledge graphs with ...

One of the promising methods is to embed the entities and the relations between them stored in a knowledge graph into a continuous low- ...

Elucidating the semantics-topology trade-off for knowledge ...

Another work by Bonner et al reinforces this finding by showing that knowledge graph embedding methods for biomedical link prediction also favor ...

Combining Large Language Models and Knowledge Graphs

These graphs leverage rich data connections to empower advanced reasoning, semantic search, and knowledge-based applications, paving the way for ...

Knowledge Graph Embedding: A Survey from the Perspective of ...

Knowledge graph embedding (KGE) is an increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional ...

Multi-domain knowledge graph embeddings for gene-disease ...

The experiments showed that considering richer knowledge graphs significantly improves gene-disease prediction and that different knowledge ...

A Knowledge Graph Embedding Framework With Triple Semantics

TKRL integrates the rich semantic information of entity types [22], while PtransE integrates the path information of the head entity to the tail.

Knowledge Graphs: Opportunities and Challenges

Knowledge graph embedding is one of the central research issues. This task aims to map entities and relations of a knowledge graph to a low- ...

Knowledge Graph Embeddings Tutorial: From Theory to Practice

Knowledge Graph Embeddings Tutorial Recorded at ECAI-2020. https://kge-tutorial-ecai2020.github.io/ Knowledge graph embeddings (KGE) are ...

Dataset for "The Effect of Semantic Knowledge ... - DataCite Commons

Anonymous. (2024). Dataset for "The Effect of Semantic Knowledge Graph Richness on Embedding Based Recommender Systems" [Data set].

Use of word and graph embedding to measure semantic ...

Graph embeddings were generated by the graph convolutional networks and 4 knowledge graph embedding models, using graphs built from UMLS ...

Learning semantic Image attributes using Image recognition ... - arXiv

Structured semantic representation of the content of an image and knowledge graph embeddings can provide a unique representation of semantic relationships ...