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Matching relevant ontology data for integration is vitally important as the amount of ontology data increases along with the evolving Semantic web, in which data are published from different individuals or organizations in a decentralized environment. For any domain that has developed a suitable ontology, its ontology annotated data (or simply ontology data) from different sources often overlaps and needs to be integrated. The purpose of this paper is to develop intelligent web ontology data matching method and framework for data integration.

This paper develops an intelligent matching method to solve the issue of ontology data matching. Based on the matching method, it also proposes a flexible peer‐to‐peer framework to address the issue of ontology data integration in a distributed Semantic web environment.

The proposed matching method is different from existing data matching or merging methods applied to data warehouse in that it employs a machine learning approach and more similarity measurements by exploring ontology features.

The proposed method and framework will be further tested for some more complicated real cases in the future.

The experiments show that this proposed intelligent matching method increases ontology data matching accuracy.

Computer matching is a mass surveillance technique involving the comparison of data about many people, which have been acquired from multiple sources. Its use offers potential benefits, particularly financial savings. It is also error‐prone, and its power results in threats to established patterns and values. The imperatives of efficiency and equity demand that computer matching be used, and the information privacy interest demands that it be used only where justified, and be subjected to effective controls. Provides background to this important technique, including its development and application in the USA and in Australia, and a detailed technical description. Contends that the technique, its use, and controls over its use are very important issues which demand research. Computing, telecommunications and robotics artefacts which have the capacity to change society radically need to be subjected to early and careful analysis, not only by sociologists, lawyers and philosophers, but also by information technologists themselves.

The semantic and structural heterogeneity of large Extensible Markup Language (XML) digital libraries emphasizes the need of supporting approximate queries, i.e. queries where the matching conditions are relaxed so as to retrieve results that possibly partially satisfy the user's requests. The paper aims to propose a flexible query answering framework which efficiently supports complex approximate queries on XML data.

To reduce the number of relaxations applicable to a query, the paper relies on the specification of user preferences about the types of approximations allowed. A specifically devised index structure which efficiently supports both semantic and structural approximations, according to the specified user preferences, is proposed. Also, a ranking model to quantify approximations in the results is presented.

Personalized queries, on one hand, effectively narrow the space of query reformulations, on the other hand, enhance the user query capabilities with a great deal of flexibility and control over requests. As to the quality of results, the retrieval process considerably benefits because of the presence of user preferences in the queries. Experiments demonstrate the effectiveness and the efficiency of the proposal, as well as its scalability.

Future developments concern the evaluation of the effectiveness of personalization on queries through additional examinations of the effects of the variability of parameters expressing user preferences.

The paper is intended for the research community and proposes a novel query model which incorporates user preferences about query relaxations on large heterogeneous XML data collections.

This paper aims to address the problem of enhancing the selection of titles offered by a digital library, by analysing the differences in these titles when they are cited by local authors in their publications and when they are listed in the digital library offer.

Text mining techniques were used to identify duplicate references. Moreover, the process of identifying syntactically different data was improved with the automated discovery of thesauri from correctly matched data, and the generated thesaurus was further used in semantic clustering. The results were effectively visually represented.

The paper finds that the function based on the Jaro‐Winkler algorithm may be efficiently used in the de‐duplication process. A generated thesaurus that utilises domain‐specific knowledge can also be used in the semantic clustering of references. It was shown that semantic clustering may be most useful in partitioning data, which is particularly significant when dealing with large amounts of data, which is usually the case. Moreover, those references that have the same or similar scores may be considered as candidate matches in the further de‐duplication process. Finally, it proved to be a more efficient way of visually representing the results.

This function can be implemented to enhance the selection of titles to be offered by a digital library, in terms of making that offer more compliant with what the library users frequently cite.

We investigate how career disruptions in terms of job loss may impact morbidity for individuals diagnosed with type 2 diabetes (T2D). Combining unique, high-quality longitudinal data from the Swedish National Diabetes Register (NDR) with matched employer–employee data, we focus on individuals diagnosed with T2D, who are established on the labor market and who lose their job in a mass layoff. Using a conditional difference-in-differences evaluation approach, our results give limited support for job loss having an impact on health behavior, diabetes progression, and cardiovascular risk factors.

Detecting and tracking the position of a mobile user has become one of the important subjects in many mobile applications. Such applications use location based services (LBS) for learning and training user movements in different places (cities, markets, airports, stations) along different modes of transport (bus, car, cycle, walk). To date, GPS is the key solution to all LBS but repeated GPS querying is not economical in terms of the battery life of the mobile phone. The purpose of this paper is to study how cheap and energy‐efficient air pressure sensors measuring the altitude could be used, as a complement to the dominant GPS system. The location detection and route tracking task is then accomplished by matching the collected altitude traces with the altitude curves of stored data to find the best matching routes.

The cornerstone of the authors' approach is that a huge amount of route data, collected with GPS devices, is available in various cloud services. In order to evaluate the mechanism of matching routes with altitude data, the authors build a prototype system of crowd‐sourced database containing only altitude data of different routes along different modes of transport. How accurately this stored altitude data could be matched with the collected altitude traces is the key question of this study.

Results show that, within a certain level of accuracy, older repeated routes can be detected from newly tracked altitude traces. Further, the level of accuracy varies depending on the length of path traversed, route curvature, speed of travel and sensor used for tracking.

The new contribution in this paper is to propose an alternative route detection mechanism which minimizes the use of GPS query. This concept will help in retrieving the GPS coordinates of already traversed routes stored in a large database by matching them with currently tracked altitude curves.

The purpose of this paper is to evaluate the state funded business counselling on firm growth.

A quasi-experimental difference-in-difference estimation of treatment effects, using a matched sample of comparable untreated firms.

Firms that have been granted counselling vouchers have a higher growth in value added than comparable untreated firms. This effect is mainly due to increased use of labour and capital, rather than increased efficiency. Results are upwardly biased due to sample selection among treated firms.

An improved strategy for identifying potential comparison firms from the pool of all firms may be necessary for further impact evaluations on business development programmes.

Policy makers may have to reconsider the programme design, since the programme currently suffer from a large potential for crowding-out, and low additional value of business counselling.

The paper uses a matching procedure in order to infer causal effects of business counselling and compares the effect of, respectively, contamination and selection on estimated impact on firm growth and survival. The data used are an original, rich micro-level data set on state investment support to businesses.