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.

A Component-Based Framework For Ontology Evolution

Abstract: Support for ontology evolution becomes extremely important in distributed development and use of ontologies. Information about change can be represented in many different ways. We describe these different representations and propose a framework that integrates them. We show how different representations in the framework are related by describing some techniques and heuristics that supplement information in one representation with information from other representations. We present an ontology of change operations, which is the kernel of our framework. 1 Support for Ontology Evolution Ontologies are increasing in popularity, and researchers and developers use them in more and more application areas. Ontologies are used as shared vocabularies, to improve information retrieval, or to help data integration. Neither the ontology development itself nor its product—the ontology— is a single-person enterprise. Large standardized ontologies are often developed by several researchers in parallel (e.g. SUO1 [9]); a number of ontologies grow in the context of peer-to-peer applications (e.g. Edutella [5]); other ontologies are constructed dynamically [2]. Successful applications of ontologies in such uncontrolled, de-centralized and distributed environments require substantial support for change management in ontologies and ontology evolution [7]. Given an ontology O and its two versions, Vold and Vnew, a complete support for change management in an ontology environment includes support for the following tasks.2 Data Transformation: When an ontology version Vold is changed to Vnew, data described by Vold might need to translated to bring it in line with Vnew. For example, if we merge two concepts A and B from Vold into C in Vnew, we must combine instances of A and B as well. http://suo.ieee.org/ Note that Vnew is not necessarily a unique replacement for Vold. There might be several new versions based on the old version, and all of them could exist in parallel. The labels are just used to refer to two versions of an ontology where Vnew has evolved from Vold. Data Access: Even if data is not being transformed, if there exists data conforming to Vold, we often want to access this data and interpret it correctly via Vnew. That is, we should be able to retrieve all data that was accessible via queries in terms of Vold with queries in terms of Vnew. Furthermore, instances of concepts in Vold should be instances of equivalent concepts in Vnew. This task is a very common one in the context of the Semantic Web, where ontologies describe pieces of data on the web. Ontology Update: When we adapt a remote ontology to specific local needs, and the remote ontology changes, we must propagate the changes in the remote ontology to the adapted local ontology [8]. Consistent Reasoning: Ontologies, being formal descriptions, are often used as logical theories. When ontology changes occur, we must analyze the changes to determine whether specific axioms that were valid in Vold are still valid in Vnew. For example, it might be useful to know that a change does not affect the subsumption relationship between two concepts: if A v B is valid in Vold it is also valid in Vnew. While a change in the logical theory will always affects reasoning in general, answers to specific queries may remain unchanged. Verification and Approval: Sometimes developers need to verify and approve ontology changes. This situation often happens when several people are developing a centralized ontology, or when developers want to apply changes selectively. There must be a user interface that simplifies such verification and allows developers to accept or reject specific changes, enabling execution of some changes and rolling back of others. This list of tasks is not exhaustive. The tools that exist today support these tasks in isolation. For example, the KAON framework [10] supports evolution strategies, allowing developers to specify strategies for updating data when changes in an ontology occur. The SHOE versioning system specifies which versions of the ontology the current version is backward compatible with [3]. Many ontology-editing environments (e.g., Protege [1]) provide logs of changes between versions. While these tools support some of the ontologyevolution tasks, there is no interaction or sharing of information among the tools. However, many of these tasks require the same elements in the representation of change. Imple-

Tiers of ontology and consistency constraints in geographical information systems

Abstract: Consistency constraints placed on a database to assure, that values incorporated in the database are consistent, are a well known foundation of Geographical Information Systems. Unfortunately in real situations rules for consistency constraints are not so clear, and inconsistent ontologies are common place, not least in geographical information, covering as it does a much wider realm than many other information systems I have suggested elsewhere 5-tiers of ontology for GIS. Such an ontology can integrate different ontological approaches in a unified system. In this paper the relation of the 5-tier ontology and consistency constraints is explored, and it is shown that different constraints are appropriate to different tiers.

The state of the art in ontology learning: a framework for comparison

Abstract: In recent years there have been some efforts to automate the ontology acquisition and construction process. The proposed systems differ from each other in some factors and have many features in common. This paper presents the state of the art in Ontology Learning (OL) and introduces a framework for classifying and comparing OL systems. The dimensions of the framework concern what to learn, from where to learn it and how it may be learnt. They include features of the input, the methods of learning and knowledge acquisition, the elements learned, the resulting ontology and also the evaluation process. To extract this framework, over 50 OL systems or modules thereof that have been described in recent articles are studied here and seven prominent ones, which illustrate the greatest differences, are selected for analysis according to our framework. In this paper after a brief description of the seven selected systems we describe the dimensions of the framework. Then we place the representative ontology learning systems into our framework. Finally, we describe the differences, strengths and weaknesses of various values for our dimensions in order to present a guideline for researchers to choose the appropriate features to create or use an OL system for their own domain or application.

Semantic conflict resolution ontology (SCROL): an ontology for detecting and resolving data and schema-level semantic conflicts

Abstract: Establishing semantic interoperability among heterogeneous information sources has been a critical issue in the database community for the past two decades. Despite the critical importance, current approaches to semantic interoperability of heterogeneous databases have not been sufficiently effective. We propose a common ontology called semantic conflict resolution ontology (SCROL) that addresses the inherent difficulties in the conventional approaches, i.e., federated schema and domain ontology approaches. SCROL provides a systematic method for automatically detecting and resolving various semantic conflicts in heterogeneous databases. SCROL provides a dynamic mechanism of comparing and manipulating contextual knowledge of each information source, which is useful in achieving semantic interoperability among heterogeneous databases. We show how SCROL is used for detecting and resolving semantic conflicts between semantically equivalent schema and data elements. In addition, we present evaluation results to show that SCROL can be successfully used to automate the process of identifying and resolving semantic conflicts.

NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse

Abstract: A new ontology development paradigm has started; its emphasis lies on the reuse and possible subsequent reengineering of knowledge resources, on the collaborative and argumentative ontology development, and on the building of ontology networks; this new trend is the opposite of building new ontologies from scratch. To help ontology developers in this new paradigm, it is important to provide strong methodological support. This thesis presents some contributions to the methodological area of the Ontology Engineering field that we are sure will improve the development and building of ontologies networks, and thus, - It proposes the NeOn Glossary of Processes and Activities, which identifies and defines the processes and activities potentially involved when ontology networks are collaboratively built. - It defines a set of two ontology network life cycle models. - It identifies and describes a collection of nine scenarios for building ontology networks. - It provides some methodological guidelines for performing the ontology requirements specification activity, to obtain the requirements that the ontology should fulfil. - It offers some methodological guidelines for obtaining the ontology network life cycle for a concrete ontology network, as part of scheduling ontology projects. Additionally, the thesis provides the technological support to these guidelines: a tool called gOntt. - It also proposes some methodological guidelines for the reuse of ontological resources at two different levels of granularity: as a whole (general ontologies and domain ontologies) and using ontology statements.