Meraka Institute
Meraka Institute
Meraka Institute
Alessandro Artale
Alessandro Artale
9559e5eedaa732e6b078a247cb3bf38263fa509c
Alessandro Artale
Mathieu d'Aquin
e61cc68181adeb9fbbddc539a6fa01dd24b299c8
Mathieu d'Aquin
Mathieu d’Aquin
Mathieu d’Aquin
Mathieu d'Aquin
Mathieu d’Aquin
Grigoris Antoniou
Grigoris Antoniou
f44cd7769f416e96864ac43498b082155196829e
Grigoris Antoniou
James Delgrande
James Delgrande
James Delgrande
612fd5e0f87bbfa4e40f864689eeed6477daa54e
FORTH
Institute of Computer Science, Greece
Institute of Computer Science, Greece
FORTH
FORTH
Institute of Computer Science, Greece
Valentina Presutti
Valentina
Presutti
Jeff Z. Pan
Jeff Z. Pan
Jeff Z. Pan
Supporting the Evolution of SHIQ Ontologies with Inductive Logic Programming -- A Preliminary Study
Supporting the Evolution of SHIQ Ontologies with Inductive Logic Programming -- A Preliminary Study
Supporting the Evolution of SHIQ Ontologies with Inductive Logic Programming -- A Preliminary Study
The definition of new concepts or roles for which extensional knowledge become available can turn out to be necessary to make a DL ontology evolve. In this paper we reformulate this task as a machine learning problem and study a solution based on techniques borrowed from that form of logic-based machine learning known under the name of Inductive Logic Programming (ILP). More precisely, we propose to adapt previous ILP results to the knowledge representation framework of DL+log in order to learn rules to be used for changing SHIQ ontologies.
The definition of new concepts or roles for which extensional knowledge become available can turn out to be necessary to make a DL ontology evolve. In this paper we reformulate this task as a machine learning problem and study a solution based on techniques borrowed from that form of logic-based machine learning known under the name of Inductive Logic Programming (ILP). More precisely, we propose to adapt previous ILP results to the knowledge representation framework of DL+log in order to learn rules to be used for changing SHIQ ontologies.
Enrico Motta
28a0f82609671f47d811e6bee865afb23abfb8db
Enrico Motta
Enrico Motta
University of Mannheim
University of Mannheim
University of Mannheim
Simon Fraser University
Simon Fraser University
Simon Fraser University
Zhisheng Huang
238a59a17bd96fbb93f39aa9dba2f6847a8d261c
Zhisheng Huang
Zhisheng Huang
Free University of Bozen-Bolzano
Free University of Bozen-Bolzano
Free University of Bozen-Bolzano
Dataset about iwod2008-alignments.
Tue May 03 19:03:57 CEST 2016
Holger Wache
612ab1009b9f9bb7c31f53982a6de846e572dfff
Holger Wache
Holger Wache
Anna Lisa Gentile
Anna Lisa
Gentile
University of Aberdeen
University of Aberdeen
University of Aberdeen
Dimitris Plexousakis
Dimitris Plexousakis
Dimitris Plexousakis
University of Applied Sciences Northwestern Switzerland
University of Applied Sciences Northwestern Switzerland
University of Applied Sciences Northwestern Switzerland
Oakland University
Oakland University
Oakland University
Andrea Giovanni Nuzzolese
Andrea Giovanni
Nuzzolese
Nicolas Rotstein
f6854301d1429ff6e2b7b3251227203ed4195220
Nicolas Rotstein
Nicolas Rotstein
Guilin Qi
7618a76dbd015fd744d2a2d5ef46642e65764490
Guilin Qi
Guilin Qi
090fcb89fa62a44e520a5a2bc2aa800b3125931e
Vijayan Sugumaran
Vijayan Sugumaran
Vijayan Sugumaran
Francesca Lisi
Francesca Lisi
Francesca Lisi
082657c852d69248417b8795e8e81298c554da83
37df258c12bd6cff2ef621f28549b5e48334e3d1
Peter Haase
Peter Haase
Peter Haase
Floriana Esposito
Floriana Esposito
e11d3c3bab0abac8725bcfe1aaa2511f49f79713
Floriana Esposito
defd58582d0124ecc531d61cd00fb7f2a6016df1
Daniel Sonntag
Daniel Sonntag
Daniel Sonntag
Aldo Gangemi
Aldo
Gangemi
ee30decb84ca12092dac4b6a6e960a348363646a
Jon Atle Gulla
Jon Atle Gulla
Jon Atle Gulla
Marta Sabou
Marta Sabou
Marta Sabou
19f8655880a3531e2a1d6eb329f44d5d6b88324b
Universita degli Studi di Bari
Universita degli Studi di Bari
Universita degli Studi di Bari
e7996a4e8321c123cb491771157e9cc24a3cfbbd
Marcelo Falappa
Marcelo Falappa
Marcelo Falappa
Universidad Nacional del Sur (UNS)
Universidad Nacional del Sur (UNS)
Universidad Nacional del Sur (UNS)
Martin Moguillansky
Martin Moguillansky
Martin Moguillansky
05879450ba4781510619a0bcce12a72a51ceaf91
Giorgos Flouris
dc808b065733afaf3aa11d334f478b26f7ef527d
Giorgos Flouris
Giorgos Flouris
Thomas Meyer
Thomas Meyer
b76b053d4974b2d025df52989b8d9af9f0bb9ca7
Thomas Meyer
German Research Center for Artificial Intelligence (DFKI)
German Research Center for Artificial Intelligence (DFKI)
German Research Center for Artificial Intelligence (DFKI)
Renata Dividino
Renata Dividino
e0924a593798bdbaa39fd04ee8e9d9d5e1dd18ad
Renata Dividino
Fraunhofer Institute for Computer Graphics Research (IGD)
Fraunhofer Institute for Computer Graphics Research (IGD)
Fraunhofer Institute for Computer Graphics Research (IGD)
The Open University
The Open University
The Open University
A Theoretical Model to Handle Ontology Debugging & Change Through Argumentation
A dynamic argumentation framework based on ALC description logics is presented by extending notions from argumentation. Since argumentation frameworks reason over graphs that relate arguments through attack, a methodology is proposed to bridge ontology-specific concepts to argumentation notions. In this way, inconsistency in the former will be represented as an attack in the latter. This approach benefits from (argumentation) acceptability semantics to restore consistency to ontologies. Finally, a model of ontology change is presented along with a rational characterization.
A Theoretical Model to Handle Ontology Debugging & Change Through Argumentation
A Theoretical Model to Handle Ontology Debugging & Change Through Argumentation
A dynamic argumentation framework based on ALC description logics is presented by extending notions from argumentation. Since argumentation frameworks reason over graphs that relate arguments through attack, a methodology is proposed to bridge ontology-specific concepts to argumentation notions. In this way, inconsistency in the former will be represented as an attack in the latter. This approach benefits from (argumentation) acceptability semantics to restore consistency to ontologies. Finally, a model of ontology change is presented along with a rational characterization.
Heiner Stuckenschmidt
f072571d8c8abd21e637a9dec2c5e835165c8ece
Heiner Stuckenschmidt
Heiner Stuckenschmidt
Fouad Zablith
388132c508c443bcb261ded283b98383f4f4149f
Fouad Zablith
Fouad Zablith
Universidade de São Paulo
Universidade de São Paulo
Universidade de São Paulo
One of the current bottlenecks for automating ontology evolution is resolving the right links between newly arising information and the existing knowledge in the ontology. Most of existing approaches mainly rely on the user when it comes to capturing and representing new knowledge. Our ontology evolution framework intends to reduce or even eliminate user input through the use of background knowledge. In this paper, we show how various sources of background knowledge could be exploited for relation discovery. We perform a relation discovery experiment focusing on the use of WordNet and Semantic Web ontologies as sources of background knowledge. We back our experiment with a thorough analysis that highlights various issues on how to improve and validate relation discovery in the future, which will directly improve the task of automatically performing ontology changes during evolution.
Using Background Knowledge for Ontology Evolution
Using Background Knowledge for Ontology Evolution
One of the current bottlenecks for automating ontology evolution is resolving the right links between newly arising information and the existing knowledge in the ontology. Most of existing approaches mainly rely on the user when it comes to capturing and representing new knowledge. Our ontology evolution framework intends to reduce or even eliminate user input through the use of background knowledge. In this paper, we show how various sources of background knowledge could be exploited for relation discovery. We perform a relation discovery experiment focusing on the use of WordNet and Semantic Web ontologies as sources of background knowledge. We back our experiment with a thorough analysis that highlights various issues on how to improve and validate relation discovery in the future, which will directly improve the task of automatically performing ontology changes during evolution.
Using Background Knowledge for Ontology Evolution
Norwegian University of Science and Technology
Norwegian University of Science and Technology
Norwegian University of Science and Technology
Vrije Universiteit Amsterdam
Vrije Universiteit Amsterdam
Vrije Universiteit Amsterdam
Ontology evaluation is an important issue that must be addressed during the lifecycle of an ontology because it assures that the ontology reflects the desired requirements during selection, design, population, evolution, usage, or other processes of the ontology lifecycle. Ontology evaluation aims at assessing the quality and the adequacy of an ontology from the point of view of a particular predefined criterion, for use in a specific context, for a specific purpose. Here, we demonstrate that evaluation is an important issue for ontology evolution. To capture the different nature of evaluation and evolution criteria, we use a tool (S-OntoEval) aiming at assessing ontology quality by implementing existing quality metrics that draw upon semiotic theory. An evaluation of the SWIntO ontology of the SmartWeb project shows its controlled evolution by quality measurement and improvement.
Ontology evaluation is an important issue that must be addressed during the lifecycle of an ontology because it assures that the ontology reflects the desired requirements during selection, design, population, evolution, usage, or other processes of the ontology lifecycle. Ontology evaluation aims at assessing the quality and the adequacy of an ontology from the point of view of a particular predefined criterion, for use in a specific context, for a specific purpose. Here, we demonstrate that evaluation is an important issue for ontology evolution. To capture the different nature of evaluation and evolution criteria, we use a tool (S-OntoEval) aiming at assessing ontology quality by implementing existing quality metrics that draw upon semiotic theory. An evaluation of the SWIntO ontology of the SmartWeb project shows its controlled evolution by quality measurement and improvement.
Controlled Ontology Evolution through Semiotic-based Ontology Evaluation
Controlled Ontology Evolution through Semiotic-based Ontology Evaluation
Controlled Ontology Evolution through Semiotic-based Ontology Evaluation
University of Karlsruhe
University of Karlsruhe
University of Karlsruhe
Renata Wassermann
a70ec591561a81e38107ddc7de18167a32775608
Renata Wassermann
Renata Wassermann
An Ontology Creation Methodology: A Phased Approach
Ontology Creation
Semantic Web
Ontologies
Semantic Web
Text Mining
Ontologies
An Ontology Creation Methodology: A Phased Approach
Existing ontology construction workbenches have severe limitations in creating ontologies from representative text collections. This paper presents a new workbench-supported ontology creation methodology that is specially designed for non-ontology experts with little knowledge of semantic markup languages. The workbench generates OWL models and is implemented as part of a search project for the entertainment domain. New techniques are gradually being added to the workbench, and early testing indicates that the ontology creation approach can be used by domain experts without much guidance from ontology experts.
Existing ontology construction workbenches have severe limitations in creating ontologies from representative text collections. This paper presents a new workbench-supported ontology creation methodology that is specially designed for non-ontology experts with little knowledge of semantic markup languages. The workbench generates OWL models and is implemented as part of a search project for the entertainment domain. New techniques are gradually being added to the workbench, and early testing indicates that the ontology creation approach can be used by domain experts without much guidance from ontology experts.
Text Mining
Ontology Creation
An Ontology Creation Methodology: A Phased Approach