Schema Evolution publication categorizer

The book edited by Bellahsene, Bonifati and Rahm provides an overview of the ways in which the schema and ontology matching and mapping tools have addressed the above requirements and points to the open technical challenges. The contributions from leading experts are structured into three parts: large-scale and knowledge-driven schema matching, quality-driven schema mapping and evolution, and evaluation and tuning of matching tasks.

We present the OAEI 2012 evaluation results for the matching system GOMMA developed at the University of Leipzig. The original application focus of GOMMA has been the life science domain but as a generic tool it can also match ontologies from other areas. It could thus participate in all OAEI tracks running on the SEALS platform. GOMMA supports several methods for efficiently matching large ontologies in particular parallel matching on multiple cores or machines, reducing the search space as well as reusing and composing previous mappings to related ontologies.

Most existing ontology matching methods are based on the linguistic information. However, some ontologies have not sufficient or regular linguistic information such as natural words and comments, so the linguistic-based methods can not work. Structure-based methods are more practical for this situation. Similarity propagation is a feasible idea to realize the structure-based matching. But traditional propagation does not take into consideration the ontology features and will be faced with effectiveness and performance problems.

In open networked systems a varying number of nodes interact each other just on the basis of their own independent ontologies and of knowledge discovery requests submitted to the network. Ontology matching techniques are essential to enable knowledge discovery and sharing in order to determine mappings between semantically related concepts of different ontologies. In this paper, we describe the H-Match algorithm and related techniques for performing matching of independent ontologies in open networked systems.

Ontology matching plays a key role for semantic interoperability. Many
methods have been proposed for automatically finding the alignment between
heterogeneous ontologies. However, in many real-world applications, finding the
alignment in a completely automatic way is highly infeasible. Ideally, an ontology
matching system would have an interactive interface to allow users to provide
feedbacks to guide the automatic algorithm. Fundamentally, we need answer the
following questions: How can a system perform an efficiently interactive process

Ontology matching is a crucial task to enable interoperation between Web applications using different but related ontologies. Today, most of the ontology matching techniques are targeted to find 1:1 mappings. However, block mappings are in fact more pervasive. In this paper, we discuss the block matching problem and suggest that both the mapping quality and the partitioning quality should be considered in block matching. We propose a novel partitioning-based approach to address the block matching issue.

In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e., using instance information of the ontology to enrich the original ontology and calculate similarities between concepts in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported

Ontology matching is a promising step towards the solution to the interoperability problem of the Semantic Web. Instance-based methods have the advantage of focusing on the most active parts of the ontologies and reflect concept semantics as they are actually being used. Previous instance-based mapping techniques were only applicable to cases where a substantial set of instances shared by both ontologies. In this paper, we propose to use a lexical search engine to map instances from different ontologies.

Despite serious research efforts, automatic ontology matching still suffers from severe problems with respect to the quality of matching results. Existing matching systems trade-off precision and recall and have their specific strengths and weaknesses. This leads to problems when the right matcher for a given task has to be selected. In this paper, we present a method for improving matching results by not choosing a specific matcher but applying machine learning techniques on an ensemble of matchers.

It has been a formidable task to achieve efficiency and scalability for the alignment between two massive, conceptually similar ontologies. Here we assume, an ontology is typically given in RDF (Resource Description Framework) or OWL (Web Ontology Language) and can be represented by a directed graph. A straightforward approach to the alignment of two ontologies entails an O(N2) computation by comparing every combination of pairs of nodes from given two ontologies, where N denotes the average number of nodes in each ontology.

Ontology mapping, or matching, aims at identifying correspondences among entities in different ontologies. Several strands of research come up with algorithms often combining multiple mapping strategies to improve the mapping accuracy. However, few approaches have systematically investigated the requirements of a mapping system both from the functional (i.e., the features that are required) and user point of view (i.e., how the user can exploit these features).

This paper presents an analysis of similarity measures for identifying ontology mapping. Using discriminant analysis, we investigated forty-eight similarity measures such as string matching and knowledge based similarities that have been used in previous systems. As a result, we extracted twenty-two effective similarity measures for identifying ontology mapping out of forty-eight possible similarity measures. The extracted measures vary widely in the type in similarity.

Ontology mapping seeks to find semantic correspondences between similar elements of different ontologies. It is a key challenge to achieve semantic interoperability in building the Semantic Web. This paper proposes a new generic and adaptive ontology mapping approach, called the PRIOR+, based on propagation theory, information retrieval techniques and artificial intelligence. The approach consists of three major modules, i.e., the IR-based similarity generator, the adaptive similarity filter and weighted similarity aggregator, and the neural network based constraint satisfaction solver.

In this paper, we introduce a general overview of Falcon-AO: a practical ontology matching system with acceptable to good performance and a number of remarkable features. Furthermore, Falcon-AO is one of the best systems in all kinds of tests in the latest three years’ OAEI campaigns. Falcon-AO is written in Java, and is open source.

Many existing ontology matching tools are not well scalable.
In this paper, we present the Malasco system, which serves as a frame-
work for reusing existing, non-scalable matching systems on large-scale
ontologies. The results achieved with dierent combinations of partition-
ing and matching tools are discussed, and optimization techniques are
examined. It is shown that the loss of result quality when matching with
partitioned data can be reduced to less than 5% compared to matching
with unpartitioned data.

It has been a formidable task to achieve efficiency and scalability for the alignment between two massive, conceptually similar ontologies. Here we assume, an ontology is typically given in RDF (Resource Description Framework) or OWL (Web Ontology Language) and can be represented by a directed graph. A straightforward approach to the alignment of two ontologies entails an O(N2) computation by comparing every combination of pairs of nodes from given two ontologies, where N denotes the average number of nodes in each ontology.

This paper aims at analyzing the key trends and challenges of the ontology matching field. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which is robust enough to be the basis for future development, and which is usable by non expert users. In this paper we first provide the basics of ontology matching with the help of examples.

Ontology alignment identifies semantically matching entities in different ontologies. Various ontology alignment strategies have been proposed; however, few systems have explored how to automatically combine multiple strategies to improve the matching effectiveness. This paper presents a dynamic multistrategy ontology alignment framework, named RiMOM. The key insight in this framework is that similarity characteristics between ontologies may vary widely.

In this paper, we propose a multi-strategic matching and merging approach to find correspondences between ontologies based on the syntactic or semantic characteristics and constraints of the Topic Maps. Our multi-strategic matching approach consists of a linguistic module and a Topic Map constraints-based module. A linguistic module computes similarities between concepts using morphological analysis, string normalization and tokenization and language-dependent heuristics.

Ontology merging and alignment is one of the effective methods for ontology sharing and reuse on the Semantic Web. A number of ontology merging and alignment tools have been developed, many of those tools depend mainly on concept (dis)similarity measure derived from linguistic cues. We present in this paper a linguistic information based approach to ontology merging and alignment.

Ontology matching, aiming to obtain semantic correspondences between two ontologies, has played a key role in
data exchange, data integration and metadata management. Among numerous matching scenarios, especially the applications cross multiple domains, we observe an important problem, denoted as unbalanced ontology matching which requires to find the matches between an ontology describing a local domain knowledge and another ontology covering the information over multiple domains, is not well studied in the community.

We present the AgreementMaker system for matching real-world schemas and ontologies, which may consist of hundreds or even thousands of concepts. The end users of the system are sophisticated domain experts whose needs have driven the design and implementation of the system: they require a responsive, powerful, and extensible framework to perform, evaluate, and compare matching methods. The system comprises a wide range of matching methods addressing dierent levels of granularity of the components being matched (conceptual vs.

Instance-based ontology mapping is a promising family of
solutions to a class of ontology alignment problems. It crucially depends
on measuring the similarity between sets of annotated instances. In this
paper we study how the choice of co-occurrence measures affects the
performance of instance-based mapping.
To this end, we have implemented a number of different statistical co-occurrence measures. We have prepared an extensive test case using vocabularies of thousands of terms, millions of instances, and hundreds
of thousands of co-annotated items. We have obtained a human Gold

Ontologies proliferate with the progress of the Semantic Web. Ontology matching is an important way of establishing interoperability between (Semantic) Web applications that use different but related ontologies. Due to their sizes and monolithic nature, large ontologies regarding real world domains bring a new challenge to the state of the art ontology matching technology. In this paper, we propose a divide-and-conquer approach to matching large ontologies.

Data integration is an old but still open issue in the database research area, where Semantic Web technologies, such as ontologies, may be of great help. Aim of the Context-ADDICT project is to provide support for the integration and context-aware reshaping of data coming from heterogeneous data sources. Within this framework, we use ontology extraction, alignment and tailoring to find and solve conflicts due to data source heterogeneity. In this paper we present X-SOM: an ontology mapping tool developed within the Context-ADDICT project.