Matching large schemas: Approaches and evaluation

A multi-strategy approach for the merging of multiple taxonomies

Taxonomy merging is an important work to provide a uniform schema for several heterogeneous taxonomies. Previous studies primarily focus on merging two taxonomies in a specific domain, while the merging of multiple taxonomies has been neglected. This article proposes a taxonomy merging approach to automatically merge multiple source taxonomies into a target taxonomy in an asymmetric manner. The approach adopts a strategy of breaking up the whole into parts to decrease the complexity of merging multiple taxonomies and employs a block-based method to reduce the scale of measuring semantic relations between concept pairs. In addition, for the problem of multiple inheritance, a method of topical coverage is proposed. Experiments conducted on synthetic and real-world scenarios indicate that the proposed merging approach is feasible and effective to merge multiple taxonomies. In particular, the proposed approach works well in the aspects of limiting the semantic redundancy and establishing high-quality hierarchical relations between concepts.

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Geographic knowledge bases (GKBs) with multiple sources and forms are of obvious heterogeneity, which hinders the integration of geographic knowledge. Entity alignment provides an effective way to find correspondences of entities by measuring the multidimensional similarity between entities from different GKBs, thereby overcoming the semantic gap. Thus, many efforts have been made in this field. This paper initially proposes basic definitions and a general framework for the entity alignment of GKBs. Specifically, the state-of-the-art of algorithms of entity alignment of GKBs is reviewed from the three aspects of similarity metrics, similarity combination, and alignment judgement; the evaluation procedure of alignment results is also summarized. On this basis, eight challenges for future studies are identified. There is a lack of methods to assess the qualities of GKBs. The alignment process should be improved by determining the best composition of heterogeneous features, optimizing alignment algorithms, and incorporating background knowledge. Furthermore, a unified infrastructure, techniques for aligning large-scale GKBs, and deep learning-based alignment techniques should be developed. Meanwhile, the generation of benchmark datasets for the entity alignment of GKBs and the applications of this field need to be investigated. The progress of this field will be accelerated by addressing these challenges.

Conceptual and Biofunctional Embodiment: A Long Story on the Transience of the Enduring Mind

We examine how embodiment in biological activity is different from conceptual embodiment as reflected in classic, modern, and postmodern perspectives on tacit knowledge. The central theme of the essay is how understanding is embodied conceptually and biofunctionally. We focus (a) on how biofunctional understanding (BU) is different from conceptual understanding (CU) and (b) on how the overall differences between these two types of embodied understanding are complementary. We show here from a conceptual perspective that embodiment theories have diverged on the meaning of embodiment; but convergence may be more likely across future perspectives if we first redefine the construct of tacit knowledge as tacit understanding and then define (explicit) CU as being directly grounded in tacit understanding, for the purpose of comparison with BU defined as being grounded in biological activity. We illustrate the complementary differences between conceptual and biofunctional embodiment of understanding first in the absence of language and then using a particular statement format and the implicit analogy of biofunctional embodiment in other bodily systems. We conclude with a suggestion about the directly uncovered but highly related embodiment of language in a section on future research.

A novel ontology matching approach using key concepts

Purpose

– Ontologies are used to formally describe the concepts within a domain in a machine-understandable way. Matching of heterogeneous ontologies is often essential for many applications like semantic annotation, query answering or ontology integration. Some ontologies may include a large number of entities which make the ontology matching process very complex in terms of the search space and execution time requirements. The purpose of this paper is to present a technique for finding degree of similarity between ontologies that trims down the search space by eliminating the ontology concepts that have less likelihood of being matched.

Design/methodology/approach

– Algorithms are written for finding key concepts, concept matching and relationship matching. WordNet is used for solving synonym problems during the matching process. The technique is evaluated using the reference alignments between ontologies from ontology alignment evaluation initiative benchmark in terms of degree of similarity, Pearson’s correlation coefficient and IR measures precision, recall and F-measure.

Findings

– Positive correlation between the degree of similarity and degree of similarity (reference alignment) and computed values of precision, recall and F-measure showed that if only key concepts of ontologies are compared, a time and search space efficient ontology matching system can be developed.

Originality/value

– On the basis of the present novel approach for ontology matching, it is concluded that using key concepts for ontology matching gives comparable results in reduced time and space.

Metamodel Matching Techniques

During the last decade, Model Driven Engineering (MDE) has been proposed for supporting the development, maintenance and evolution of software systems. Model Driven Architecture (MDA), Software Factories and Eclipse Modeling Framework (EMF) are among the most representatives MDE approaches. Nowadays, it is well recognized that model transformation is at the heart of MDE approaches and, consequently represents one of the most important operations in MDE. However, despite the multitude of model transformation language proposals emerging from academic world and industry, these transformations are often manually specified; which is a tedious and error-prone task, and therefore an expensive process. Matching operation between metamodels is the keystone toward a (semi-)automatic transformation process. In this paper, the authors review metamodel matching techniques of the literature and then analyze their pros and cons in order to show how they can be useful for a semi-automatic transformation process. The result is a comparison of metamodel matching techniques, highlighting their similarities and differences in terms of information used for matching, demonstrating significant similarities between these techniques. Next, the authors compare four well-known metamodel matching techniques namely Similarity flooding, SAMT4MDE+ (extended Semi-Automatic Matching Tool for Model Driven Engineering), ModelCVS and AML (AtlanMod Matching Language) on ten couples of metamodels. For this comparison, the authors define a set of six criteria inspired from the database schema matching. One among these criteria is relevant to the quality of matching and for which we define a quality measure metrics. Furthermore, the authors develop a plug-in under Eclipse to support our comparison using ten couples of metamodels.

XML document-grammar comparison: related problems and applications

XML document comparison is becoming an ever more popular research issue due to the increasingly abundant use of XML. Likewise, a growing interest fosters the development of XML grammar matching and comparison, due to the proliferation of heterogeneous XML data sources, particularly on the Web. Nonetheless, the process of comparing XML documents with XML grammars, i.e., XML document and grammar similarity evaluation, has not yet received the attention it deserves. In this paper, we provide an overview on existing research related to XML document/grammar comparison, presenting the background and discussing the various techniques related to the problem. We also discuss some prominent application domains, ranging over document classification and clustering, document transformation, grammar evolution, selective dissemination of XML information, XML querying, as well as alert filtering in intrusion detection systems and Web Services matching and communications.

Towards A Semi-Automatic Transformation Process in MDA

Model Driven Engineering (MDE) has been proposed for supporting the development, maintenance and evolution of software systems. Model Driven Architecture (MDA), Software Factories, and the Eclipse Modeling Framework (EMF) are representative MDE approaches. These MDE approaches have concepts and techniques in common such as modeling, metamodels, and model transformation. Today, it is well recognized that model transformation is the most important of MDE approaches and is one of the most important operations in MDE. Despite the multitude of model transformation language proposals emerging from university and industry, these transformations are often created manually, which is a tedious and error-prone task and therefore an expensive process. In this paper, the authors propose an extended architecture that aims to semi-automate the process of transformation in the context of MDA. This architecture involves concepts and techniques of metamodel matching, inspired and borrowed from schema matching techniques that are an important issue in the database and ontology domain. This architecture is enforced by a methodology which details the different steps leading to a semi-automatic transformation process. Matching foundations and the authors’ approach for metamodel matching are discussed.

A Survey on Uncertainty Management in Data Integration

In the last few years, uncertainty management has come to be recognized as a fundamental aspect of data integration. It is now accepted that it may not be possible to remove uncertainty generated during data integration processes and that uncertainty in itself may represent a source of relevant information. Several issues, such as the aggregation of uncertain mappings and the querying of uncertain mediated schemata, have been addressed by applying well-known uncertainty management theories. However, several problems lie unresolved. This article sketches an initial picture of this highly active research area; it details existing works in the light of a homogeneous framework, and identifies and discusses the leading issues awaiting solutions.

Semantic Synchronization in B2B Transactions

Despite the development of e-business standards, the integration of business processes and business information systems is still a non-trivial issue if business partners use different e-business standards for formatting and describing information to be processed. Since those standards can be understood as ontologies, ontological engineering technologies can be applied for processing, especially ontology matching for reconciling them. However, as e-business standards tend to be rather large-scale ontologies, scalability is a crucial requirement. To serve this demand, we present our ORBI Ontology Mediator. It is linked with our Malasco system for partition-based ontology matching with currently available matching systems, which so far do not scale well, if at all. In our case study we show how to provide dynamic semantic synchronization between business partners using different e-business standards without initial ramp-up effort, based on ontological mapping technology combined with interactive user participation.

Biological data integration using Semantic Web technologies

Current research in biology heavily depends on the availability and efficient use of information. In order to build new knowledge, various sources of biological data must often be combined. Semantic Web technologies, which provide a common framework allowing data to be shared and reused between applications, can be applied to the management of disseminated biological data. However, due to some specificities of biological data, the application of these technologies to life science constitutes a real challenge. Through a use case of biological data integration, we show in this paper that current Semantic Web technologies start to become mature and can be applied for the development of large applications. However, in order to get the best from these technologies, improvements are needed both at the level of tool performance and knowledge modeling.