Alin: improving interactive ontology matching by interactively revising mapping suggestions

Abstract Ontology matching aims at discovering mappings between the entities of two ontologies. It plays an important role in the integration of heterogeneous data sources that are described by ontologies. Interactive ontology matching involves domain experts in the matching process. In some approaches, the expert provides feedback about mappings between ontology entities, that is, these approaches select mappings to present to the expert who replies which of them should be accepted or rejected, so taking advantage of the knowledge of domain experts towards finding an alignment. In this paper, we present Alin, an interactive ontology matching approach which uses expert feedback not only to approve or reject selected mappings but also to dynamically improve the set of selected mappings, that is, to interactively include and to exclude mappings from it. This additional use for expert answers aims at increasing in the benefit brought by each expert answer. For this purpose, Alin uses four techniques. Two techniques were used in the previous versions of Alin to dynamically select concept and attribute mappings. Two new techniques are introduced in this paper: one to dynamically select relationship mappings and another one to dynamically reject inconsistent selected mappings using anti-patterns. We compared Alin with state-of-the-art tools, showing that it generates alignment of comparable quality.

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An Iterative Automatic Final Alignment Method in the Ontology Matching System

Journal of information and organizational sciences ◽

10.31341/jios.42.1.3 ◽

2018 ◽

Vol 42 (1) ◽

pp. 39-61 ◽

Cited By ~ 1

Author(s):

Marko Gulić ◽

Marin Vuković

Keyword(s):

Heterogeneous Data ◽

Data Sources ◽

Ontology Matching ◽

Alignment Method ◽

Automatic Adjustment ◽

Matching Process ◽

Heterogeneous Data Sources ◽

Final Alignment

Ontology matching plays an important role in the integration of heterogeneous data sources that are described by ontologies. In order to determine correspondences between ontologies, a set of matchers can be used. After the execution of these matchers and the aggregation of the results obtained by these matchers, a final alignment method is executed in order to select appropriate correspondences between entities of compared ontologies. The final alignment method is an important part of the ontology matching process because it directly determines the output result of this process. In this paper we improve our iterative final alignment method by introducing an automatic adjustment of final alignment threshold as well as a new rule for determining false correspondences with similarity values greater than adjusted threshold. An evaluation of the method is performed on the test ontologies of the OAEI evaluation contest and a comparison with other final alignment methods is given.

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Enriching Ontology-based Data Access with Provenance

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/224 ◽

2019 ◽

Author(s):

Diego Calvanese ◽

Davide Lanti ◽

Ana Ozaki ◽

Rafael Penaloza ◽

Guohui Xiao

Keyword(s):

State Of The Art ◽

Data Access ◽

Heterogeneous Data ◽

Data Sources ◽

Conjunctive Query ◽

Heterogeneous Data Sources ◽

Idempotent Semirings ◽

Database Theory ◽

Extensive Evaluation ◽

Practical Feasibility

Ontology-based data access (OBDA) is a popular paradigm for querying heterogeneous data sources by connecting them through mappings to an ontology. In OBDA, it is often difficult to reconstruct why a tuple occurs in the answer of a query. We address this challenge by enriching OBDA with provenance semirings, taking inspiration from database theory. In particular, we investigate the problems of (i) deciding whether a provenance annotated OBDA instance entails a provenance annotated conjunctive query, and (ii) computing a polynomial representing the provenance of a query entailed by a provenance annotated OBDA instance. Differently from pure databases, in our case, these polynomials may be infinite. To regain finiteness, we consider idempotent semirings, and study the complexity in the case of DL-LiteR ontologies. We implement Task (ii) in a state-of-the-art OBDA system and show the practical feasibility of the approach through an extensive evaluation against two popular benchmarks.

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Machine Learning and Constraint Programming for Relational-To-Ontology Schema Mapping

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/178 ◽

2018 ◽

Cited By ~ 3

Author(s):

Diego De Uña ◽

Nataliia Rümmele ◽

Graeme Gange ◽

Peter Schachte ◽

Peter J. Stuckey

Keyword(s):

Machine Learning ◽

Constraint Programming ◽

Steiner Tree ◽

State Of The Art ◽

Heterogeneous Data ◽

Data Sources ◽

Schema Mapping ◽

Heterogeneous Data Sources ◽

Side Constraints ◽

Flexible Framework

The problem of integrating heterogeneous data sources into an ontology is highly relevant in the database field. Several techniques exist to approach the problem, but side constraints on the data cannot be easily implemented and thus the results may be inconsistent. In this paper we improve previous work by Taheriyan et al. [2016a] using Machine Learning (ML) to take into account inconsistencies in the data (unmatchable attributes) and encode the problem as a variation of the Steiner Tree, for which we use work by De Uña et al. [2016] in Constraint Programming (CP). Combining ML and CP achieves state-of-the-art precision, recall and speed, and provides a more flexible framework for variations of the problem.

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Predicting Synthetic Lethal Interactions using Heterogeneous Data Sources

10.1101/660092 ◽

2019 ◽

Cited By ~ 1

Author(s):

Herty Liany ◽

Anand Jeyasekharan ◽

Vaibhav Rajan

Keyword(s):

Drug Targets ◽

State Of The Art ◽

Matrix Completion ◽

Heterogeneous Data ◽

Data Sources ◽

Potential Candidate ◽

Synthetic Lethal ◽

Heterogeneous Data Sources ◽

Synthetic Lethal Interactions ◽

Latent Representations

AbstractMotivationA synthetic lethal (SL) interaction is a relationship between two functional entities where the loss of either one of the entities is viable but the loss of both entities is lethal to the cell. Such pairs can be used as drug targets in targeted anticancer therapies, and so, many methods have been developed to identify potential candidate SL pairs. However, these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels; and hence are limited in their ability to learn from complex associations in heterogeneous data sources.ResultsIn this paper we develop techniques that can seamlessly integrate multiple heterogeneous data sources to predict SL interactions. Our approach obtains latent representations by collective matrix factorization based techniques, which in turn are used for prediction through matrix completion. Our experiments, on a variety of biological datasets, illustrate the efficacy and versatility of our approach, that outperforms state-of-the-art methods for predicting SL interactions and can be used with heterogeneous data sources with minimal feature engineering.AvailabilitySoftware available athttps://github.com/[email protected]

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