Ontology-based data integration

About: Ontology-based data integration is a research topic. Over the lifetime, 11065 publications have been published within this topic receiving 216888 citations.

Ontology for database design and application development

Abstract: A system and method lets a user create or import ontologies and create databases and related application software. These databases can be specially tuned to suit a particular need, and each comes with the same error-detection rules to keep the data clean. Such databases may be searched based on meaning, rather than on words-that-begin-with-something. And multiple databases, if generated from the same basic ontology can communicate with each other without any additional effort. Ontology management and generation tools enable enterprises to create databases that use ontologies to improve data integration, maintainability, quality, and flexibility. Only the relevant aspects of the ontology are targeted, extracting out a sub-model that has the power of the full ontology restricted to objects of interest for the application domain. To increase performance and add desired database characteristics, this sub-model is translated into a database system. Java-based object-oriented and relational application program interfaces (APIs) are then generated from this translation, providing application developers with an API that exactly reflects the entity types and relations (classes and methods) that are represented by the database. This generation approach essentially turns the ontology into a set of integrated and efficient databases.

Bootstrapping ontology alignment methods with APFEL

Abstract: Ontology alignment is a prerequisite in order to allow for interoperation between different ontologies and many alignment strategies have been proposed to facilitate the alignment task by (semi-)automatic means. Due to the complexity of the alignment task, manually defined methods for (semi-)automatic alignment rarely constitute an optimal configuration of substrategies from which they have been built. In fact, scrutinizing current ontology alignment methods, one may recognize that most are not optimized for given ontologies. Some few include machine learning for automating the task, but their optimization by machine learning means is mostly restricted to the extensional definition of ontology concepts. With APFEL (Alignment Process Feature Estimation and Learning) we present a machine learning approach that explores the user validation of initial alignments for optimizing alignment methods. The methods are based on extensional and intensional ontology definitions. Core to APFEL is the idea of a generic alignment process, the steps of which may be represented explicitly. APFEL then generates new hypotheses for what might be useful features and similarity assessments and weights them by machine learning approaches. APFEL compares favorably in our experiments to competing approaches.

Repairing ontology mappings

Abstract: Automatically discovering semantic relations between ontologies is an important task with respect to overcoming semantic heterogeneity on the semantic web. Existing ontology matching systems, however, often produce erroneous mappings. In this paper, we address the problem of errors in mappings by proposing a completely automatic debugging method for ontology mappings. The method uses logical reasoning to discover and repair logical inconsistencies caused by erroneous mappings. We describe the debugging method and report experiments on mappings submitted to the ontology alignment evaluation challenge that show that the proposed method actually improves mappings created by different matching systems without any human intervention.

Learning domain ontologies for Web service descriptions: an experiment in bioinformatics

Abstract: The reasoning tasks that can be performed with semantic web service descriptions depend on the quality of the domain ontologies used to create these descriptions. However, building such domain ontologies is a time consuming and difficult task.We describe an automatic extraction method that learns domain ontologies for web service descriptions from textual documentations attached to web services. We conducted our experiments in the field of bioinformatics by learning an ontology from the documentation of the web services used in myGrid, a project that supports biology experiments on the Grid. Based on the evaluation of the extracted ontology in the context of the project, we conclude that the proposed extraction method is a helpful tool to support the process of building domain ontologies for web service descriptions.

Towards unbiased evaluation of uncertainty reasoning: The URREF ontology

Abstract: Current advances in technology, sensor collection, data storage, and data distribution have afforded more complex, distributed, and operational information fusion systems (IFSs). IFSs notionally consist of low-level (data collection, registration, and association in time and space) and high-level information fusion (user coordination, situational awareness, and mission control), which require a common ontology for effective communication and data processing. In this paper, we describe the ontology reference model developed as part of the uncertainty representation and reasoning evaluation framework (URREF). The URREF ontology is intended to provide guidance for defining the actual concepts and criteria that together comprise the comprehensive uncertainty evaluation framework being developed by the Evaluation of Technologies for Uncertainty Representation Working Group (ETURWG).