Updating Ontology Alignment on the Instance Level Based on Ontology Evolution

Ontology alignment plays a key role for information interconnectivity between computational systems relying on ontologies described in different natural languages. Existing approaches for ontology matching usually provide equivalent type of relation in the generated mappings. In this article, we propose a refinement technique to enable the update of the semantic type of the mapping such as “is-a”, “part-of”, etc. Our approach relies on information from the ontology evolution to apply refinement actions. We formalize the refinement actions and procedures, as well as apply the proposal in application scenarios.

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A faster and less aggressive algorithm for correcting conservativity violations in ontology alignments

Applied Ontology ◽

10.3233/ao-210243 ◽

2021 ◽

pp. 1-20

Author(s):

Cauã Roca Antunes ◽

Alexandre Rademaker ◽

Mara Abel

Keyword(s):

State Of The Art ◽

Time Algorithm ◽

The State ◽

Ontology Alignment ◽

Ontology Alignment Evaluation Initiative ◽

Multiple Ontologies

Ontologies are computational artifacts that model consensual aspects of reality. In distributed contexts, applications often need to utilize information from several distinct ontologies. In order to integrate multiple ontologies, entities modeled in each ontology must be matched through an ontology alignment. However, imperfect alignments may introduce inconsistencies. One kind of inconsistency, which is often introduced, is the violation of the conservativity principle, that states that the alignment should not introduce new subsumption relations between entities from the same source ontology. We propose a two-step quadratic-time algorithm for automatically correcting such violations, and evaluate it against datasets from the Ontology Alignment Evaluation Initiative 2019, comparing the results to a state-of-the-art approach. The proposed algorithm was significantly faster and less aggressive; that is, it performed fewer modifications over the original alignment when compared to the state-of-the-art algorithm.

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INSTANCE MIGRATION BETWEEN ONTOLOGIES HAVING STRUCTURAL DIFFERENCES

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213011000553 ◽

2011 ◽

Vol 20 (06) ◽

pp. 1127-1156 ◽

Cited By ~ 3

Author(s):

MAXIM DAVIDOVSKY ◽

VADIM ERMOLAYEV ◽

VYACHESLAV TOLOK

Keyword(s):

Knowledge Management ◽

Life Cycle ◽

Structural Changes ◽

Software Framework ◽

Ontology Alignment ◽

Transformation Rules ◽

Structural Differences ◽

Prototype Software ◽

Descriptive Theories ◽

Future Work

Ontology instance migration is one of the complex and not fully solved problems in knowledge management. A solution is required when the ontology schema evolves in the life cycle and the assertions have to be transferred to the newer version. The problem may become more complex in distributed settings when, for example, several autonomous software entities use and exchange partial assertional knowledge in a domain that is formalized by different though semantically overlapping descriptive theories. Such an exchange is essentially the migration of the assertional part of an ontology to other ontologies belonging to or used by different entities. The paper presents our method and tool for migrating instances between the ontologies that have structurally different but semantically overlapping schemas. The approach is based on the use of the manually coded transformation rules describing the changes between the input and the output ontologies. The tool is implemented as a plug-in for the ProjectNavigator prototype software framework. The article also reports the results of our three evaluation experiments. In these experiments we evaluated the degree of complexity in the structural changes to which our approach remains valid. We also chose the ontology sets in one of the experiments to make the results comparable with the ontology alignment software. Finally we checked how well our approach scales with the increase of the quantity of the migrated ontology instances to the numbers that are characteristic to industrial ontologies. In our opinion the evaluation results are satisfactory and suggest some directions for the future work.

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A mixed approach for automated spatial ontology alignment

Journal of Spatial Science ◽

10.1080/14498596.2010.521974 ◽

2010 ◽

Vol 55 (2) ◽

pp. 237-255 ◽

Cited By ~ 4

Author(s):

Reza Khatami ◽

Ali Asghar Alesheikh ◽

Majid Hamrah

Keyword(s):

Ontology Alignment ◽

Mixed Approach

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An Integer Linear Programming-Based Method for the Extraction of Ontology Alignment

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2021040102 ◽

2021 ◽

Vol 16 (2) ◽

pp. 25-44

Author(s):

Naima El Ghandour ◽

Moussa Benaissa ◽

Yahia Lebbah

Keyword(s):

Linear Programming ◽

Semantic Web ◽

Integer Linear Programming ◽

New Method ◽

Ontology Matching ◽

Ontology Alignment ◽

Programming Techniques ◽

Data Heterogeneity ◽

Series Of Experiments

The Semantic Web uses ontologies to cope with the data heterogeneity problem. However, ontologies become themselves heterogeneous; this heterogeneity may occur at the syntactic, terminological, conceptual, and semantic levels. To solve this problem, alignments between entities of ontologies must be identified. This process is called ontology matching. In this paper, the authors propose a new method to extract alignment with multiple cardinalities using integer linear programming techniques. The authors conducted a series of experiments and compared them with currently used methods. The obtained results show the efficiency of the proposed method.

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