The dataset provides embeddings for entities and relations in DBpedia (English) and Wikidata. The two knowledge graphs are first merged using a novel approach that we developed by leveraging the sameAs links between them. Then, we used the state-of-the-art embedding model ConEx to compute embeddings of the merge. Our embeddings are called universal knowledge graph embeddings
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entit...
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entit...
We propose an entity-agnostic representation learning method for handling the problem of inefficient...
The dataset provides embeddings for entities and relations in DBpedia (English) and Wikidata. The tw...
With the growing popularity of multi-relational data on the Web, knowledge graphs (KGs) have become...
We examine the embedding approach to reason new relational facts from a large-scale knowledge graph ...
We examine the embedding approach to reason new relational facts from a large-scale knowledge graph ...
The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs ...
Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet ...
Knowledge graph completion aims to perform link pre-diction between entities. In this paper, we cons...
Knowledge graph completion aims to perform link prediction between entities. In this paper, we consi...
The evolution of the Web of documents into a Web of services and data has resulted in an increased a...
The internet is constantly expanding across millions of web pages. Using the internet effectively is...
Intelligent systems are expected to make smart human-like decisions based on accumulated commonsense...
Knowledge graphs are used to represent relational information in terms of triples. To enable learnin...
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entit...
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entit...
We propose an entity-agnostic representation learning method for handling the problem of inefficient...
The dataset provides embeddings for entities and relations in DBpedia (English) and Wikidata. The tw...
With the growing popularity of multi-relational data on the Web, knowledge graphs (KGs) have become...
We examine the embedding approach to reason new relational facts from a large-scale knowledge graph ...
We examine the embedding approach to reason new relational facts from a large-scale knowledge graph ...
The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs ...
Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet ...
Knowledge graph completion aims to perform link pre-diction between entities. In this paper, we cons...
Knowledge graph completion aims to perform link prediction between entities. In this paper, we consi...
The evolution of the Web of documents into a Web of services and data has resulted in an increased a...
The internet is constantly expanding across millions of web pages. Using the internet effectively is...
Intelligent systems are expected to make smart human-like decisions based on accumulated commonsense...
Knowledge graphs are used to represent relational information in terms of triples. To enable learnin...
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entit...
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entit...
We propose an entity-agnostic representation learning method for handling the problem of inefficient...