Add to ORKGKnowledge graphs, as understood within the Semantic Web and Knowledge Representation communities, are more than just graph data. OWL-based knowledge graphs offer the benefits of being based on an ecosystem of open W3C standards that are implemented in a range of reusable existing resources (e.g. curated ontologies, software tools, web-wide linked data) and that also permit researchers to tailor resources for their unique needs (e.g. custom ontologies). Additionally, OWL-based knowledge graphs offer the benefits of formal, logical symbolic reasoning (e.g. reliable inference of new knowledge based on Description Logics, semantic consistency checking, extensions via user-defined Datalog rules). These capabilities allow OWL-based knowledge grap...
Knowledge graphs have, for the past decade, been a hot topic both in public and private domains, typ...
Learning the underlying patterns in data goes beyondinstance-based generalization to external knowle...
Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful inf...
Remarkable progress in research has shown the efficiency of Knowledge Graphs (KGs) in extracting val...
Knowledge graphs (KGs) express relationships between entity pairs, and many real-life problems can b...
The increasingly pervasive nature of the Web, expanding to devices and things in everyday life, alo...
Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving h...
Making complex decisions in areas like science, government policy, finance, and clinical treatments ...
The internet is constantly expanding across millions of web pages. Using the internet effectively is...
Abstract Background Ontologies are representations of a conceptualization of a domain. Traditionally...
Learning the underlying patterns in data goes beyond instance-based generalization to external knowl...
We argue that the field of neural-symbolic integra-tion is in need of identifying application scenar...
A significant and recent development in neural-symbolic learning are deep neural networks that can r...
Human beings have always tried to pass down knowledge to preserve it and let further generations exp...
The ontology knowledge base (KB) can be divided into two parts: TBox and ABox, where the former mode...
Knowledge graphs have, for the past decade, been a hot topic both in public and private domains, typ...
Learning the underlying patterns in data goes beyondinstance-based generalization to external knowle...
Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful inf...
Remarkable progress in research has shown the efficiency of Knowledge Graphs (KGs) in extracting val...
Knowledge graphs (KGs) express relationships between entity pairs, and many real-life problems can b...
The increasingly pervasive nature of the Web, expanding to devices and things in everyday life, alo...
Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving h...
Making complex decisions in areas like science, government policy, finance, and clinical treatments ...
The internet is constantly expanding across millions of web pages. Using the internet effectively is...
Abstract Background Ontologies are representations of a conceptualization of a domain. Traditionally...
Learning the underlying patterns in data goes beyond instance-based generalization to external knowl...
We argue that the field of neural-symbolic integra-tion is in need of identifying application scenar...
A significant and recent development in neural-symbolic learning are deep neural networks that can r...
Human beings have always tried to pass down knowledge to preserve it and let further generations exp...
The ontology knowledge base (KB) can be divided into two parts: TBox and ABox, where the former mode...
Knowledge graphs have, for the past decade, been a hot topic both in public and private domains, typ...
Learning the underlying patterns in data goes beyondinstance-based generalization to external knowle...
Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful inf...