A learning-based ontology alignment approach using inductive logic programming
•A new approach to find ontology mapping using inductive logic programming.•The ability to use background knowledge, as an input to induction algorithm.•Can resolve structural inconsistencies between two different ontologies.•Generating generalized logical rules based on background knowledge as mapp...
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| Veröffentlicht in: | Expert systems with applications Jg. 125; S. 412 - 424 |
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| Abstract | •A new approach to find ontology mapping using inductive logic programming.•The ability to use background knowledge, as an input to induction algorithm.•Can resolve structural inconsistencies between two different ontologies.•Generating generalized logical rules based on background knowledge as mappings.•Achieving high, more acceptable and efficient F-Measure for ontology alignment.
Ontologies are key concepts in the semantic web and have an impressive role which comprise the biggest and the most prominent part of the infrastructure in this realm of web research. By fast growth of the semantic web and also, the variety of its applications, ontology mapping (ontology alignment) has been transformed into a crucial issue in the realm of computer science. Several approaches are introduced for ontology alignment during these last years, but developing more accurate and efficient algorithms and finding new effective techniques and algorithms for this problem is an interesting research area since real-world applications with respect to their more complicated concepts need more efficient algorithms. In this paper, we illustrated a new ontology mapping method based on learning using Inductive Logic Programming (ILP), and show how the ILP can be used to solve the ontology mapping problem. As a matter of fact, in this approach, an ontology which is described in OWL format is interpreted to first-order logic. Then, with the use of learning based on inductive logic, the existing hidden rules and relationships between concepts are discovered and presented. Since the inductive logic has high flexibility in solving problems such as discovering relationships between concepts and links, it also can be performed effectively in solving the ontology alignment problem. Our experimental results show that this technique yield to more accurate results comparing to other matching algorithms and systems, achieving an F-measure of 95.6% and 91% on two well-known reference datasets the Anatomy and the Library, respectively. |
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| AbstractList | Ontologies are key concepts in the semantic web and have an impressive role which comprise the biggest and the most prominent part of the infrastructure in this realm of web research. By fast growth of the semantic web and also, the variety of its applications, ontology mapping (ontology alignment) has been transformed into a crucial issue in the realm of computer science. Several approaches are introduced for ontology alignment during these last years, but developing more accurate and efficient algorithms and finding new effective techniques and algorithms for this problem is an interesting research area since real-world applications with respect to their more complicated concepts need more efficient algorithms. In this paper, we illustrated a new ontology mapping method based on learning using Inductive Logic Programming (ILP), and show how the ILP can be used to solve the ontology mapping problem. As a matter of fact, in this approach, an ontology which is described in OWL format is interpreted to first-order logic. Then, with the use of learning based on inductive logic, the existing hidden rules and relationships between concepts are discovered and presented. Since the inductive logic has high flexibility in solving problems such as discovering relationships between concepts and links, it also can be performed effectively in solving the ontology alignment problem. Our experimental results show that this technique yield to more accurate results comparing to other matching algorithms and systems, achieving an F-measure of 95.6% and 91% on two well-known reference datasets the Anatomy and the Library, respectively. •A new approach to find ontology mapping using inductive logic programming.•The ability to use background knowledge, as an input to induction algorithm.•Can resolve structural inconsistencies between two different ontologies.•Generating generalized logical rules based on background knowledge as mappings.•Achieving high, more acceptable and efficient F-Measure for ontology alignment. Ontologies are key concepts in the semantic web and have an impressive role which comprise the biggest and the most prominent part of the infrastructure in this realm of web research. By fast growth of the semantic web and also, the variety of its applications, ontology mapping (ontology alignment) has been transformed into a crucial issue in the realm of computer science. Several approaches are introduced for ontology alignment during these last years, but developing more accurate and efficient algorithms and finding new effective techniques and algorithms for this problem is an interesting research area since real-world applications with respect to their more complicated concepts need more efficient algorithms. In this paper, we illustrated a new ontology mapping method based on learning using Inductive Logic Programming (ILP), and show how the ILP can be used to solve the ontology mapping problem. As a matter of fact, in this approach, an ontology which is described in OWL format is interpreted to first-order logic. Then, with the use of learning based on inductive logic, the existing hidden rules and relationships between concepts are discovered and presented. Since the inductive logic has high flexibility in solving problems such as discovering relationships between concepts and links, it also can be performed effectively in solving the ontology alignment problem. Our experimental results show that this technique yield to more accurate results comparing to other matching algorithms and systems, achieving an F-measure of 95.6% and 91% on two well-known reference datasets the Anatomy and the Library, respectively. |
| Author | Kamandi, Ali Karimi, Hamed |
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| Cites_doi | 10.21105/joss.00892 10.1109/TKDE.2011.253 10.1002/int.20517 10.1017/S0269888903000651 10.3233/SW-2012-0081 10.1109/TKDE.2015.2475755 10.1016/j.ins.2010.08.013 10.1016/j.datak.2015.07.003 10.1007/s10489-015-0648-z 10.1016/j.chb.2016.12.039 10.1016/j.eswa.2014.08.032 10.1186/gb-2005-6-3-r29 10.1016/j.jksuci.2014.03.010 10.1145/2816839.2816928 10.1007/BF03037227 10.1007/s11633-012-0649-x 10.1007/s10619-017-7206-0 10.1142/S1793351X1000095X 10.1109/MHS.1995.494215 10.1155/2018/2309587 |
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| Keywords | Semantic web Ontology matching Ontology mapping learning Ontology alignment Inductive logic programming |
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