Add to ORKGAcquiring and maintaining Semantic Web rules is very demanding and can be automated though partially by applying Machine Learning algorithms. In this paper we show that the form of Machine Learning known under the name of Inductive Logic Programming (ILP) can help. In particular, we take a critical look at two ILP proposals based on knowledge representation frameworks that integrate Description Logics and Horn Clausal Logic and draw from them general conclusions that can be considered as guidelines for further ILP research of interest to the Semantic Web
The definition of new concepts or roles for which extensional knowledge become available can turn ou...
This chapter aims at demonstrating that inductive logic programming (ILP), a recently established s...
Mining the layers of ontologies and rules provides an interesting testbed for inductive reasoning on...
Acquiring and maintaining Semantic Web rules is very demanding and can be automated though partially...
Building rules on top of ontologies is the goal of the logical layer of the Semantic Web. The syste...
The use of background knowledge and the adoption of Horn clausal logic as a knowledge representation...
Abstract The last three decades has seen the development of Computational Logic techniques within Ar...
Building rules on top of ontologies is the ultimate goal of the logical layer of the Semantic Web. T...
AbstractInductive Logic Programming (ILP) is the area of AI which deals with the induction of hypoth...
In spite of the increasing effort spent on building ontologies for the Semantic Web, little attentio...
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a...
In this paper we consider the problem of having ontologies as prior conceptual knowledge in Inductiv...
Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypo...
Inductive Logic Programming (ILP) is a new discipline which investigates the inductive construction ...
Inductive logic programming (ILP) is built on a foundation laid by research in machine learning and ...
The definition of new concepts or roles for which extensional knowledge become available can turn ou...
This chapter aims at demonstrating that inductive logic programming (ILP), a recently established s...
Mining the layers of ontologies and rules provides an interesting testbed for inductive reasoning on...
Acquiring and maintaining Semantic Web rules is very demanding and can be automated though partially...
Building rules on top of ontologies is the goal of the logical layer of the Semantic Web. The syste...
The use of background knowledge and the adoption of Horn clausal logic as a knowledge representation...
Abstract The last three decades has seen the development of Computational Logic techniques within Ar...
Building rules on top of ontologies is the ultimate goal of the logical layer of the Semantic Web. T...
AbstractInductive Logic Programming (ILP) is the area of AI which deals with the induction of hypoth...
In spite of the increasing effort spent on building ontologies for the Semantic Web, little attentio...
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a...
In this paper we consider the problem of having ontologies as prior conceptual knowledge in Inductiv...
Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypo...
Inductive Logic Programming (ILP) is a new discipline which investigates the inductive construction ...
Inductive logic programming (ILP) is built on a foundation laid by research in machine learning and ...
The definition of new concepts or roles for which extensional knowledge become available can turn ou...
This chapter aims at demonstrating that inductive logic programming (ILP), a recently established s...
Mining the layers of ontologies and rules provides an interesting testbed for inductive reasoning on...