MultiFarm: A benchmark for multilingual ontology matching

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish – we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism

Моделирование взвешенного агрегирования ранжированных объектов по произвольной совокупности других объектов на примере ученых-экономистов и экономических журналов

Алгоритмизирован и апробирован метод взвешенного агрегирования ранжированных объектов, которые названы объектами первого рода, по некоторой меньшей совокупности других объектов, которые названы объектами второго рода. Метод апробирован на примере ранжировок ученых экономистов и научных экономических журналов (объекты первого рода), распределенных по регионам России (объекты второго рода). Разработан универсальный алгоритм и программа на языке C++ для решения такого рода зада

Взвешенное агрегирование ранжированных объектов по произвольной совокупности других объектов

Предложен универсальный метод распределения ранжированных объектов по меньшему числу других объектов, который можно использовать при проведении экономических, педагогических и других исследовани

The Weighted Aggregation of Ranked Objects by the Arbitrary Totality of Other Objects

The task of distribution of the ranked objects by the smaller number of other objects is set. The first objects are named objects of the first kind, the second objects are the objects of the second kind. The rating of the totality of objects of the first kind, included on the attribute of belonging in the object of the second kind, is suggested to be calculated through the procedure of the weighted aggregation which represents a product of number of the above mentioned objects of the first kind and the average weight coefficient calculated through the average rank (rating) of the totality of the objects of the first kind. An example of such a task is the distribution of ranked universities by the world countries according to one of the global world ratings. The task is extended to the calculation of the integral rank (rating) for an arbitrary number of rankings of different objects of the first kind, distributed on the given number of objects of the second kind

Intelligent blockchain management for distributed knowledge graphs in IoT 5G environments

This article introduces a new problem of distributed knowledge graph, in IoT 5G setting. We developed an end-to-end solution for solving such problem by exploring the blockchain management and intelligent method for producing the better matching of the concepts and relations of the set of knowledge graphs. The concepts and the relations of the knowledge graphs are divided into several components, each of which contains similar concepts and relations. Instead of exploring the whole concepts and the relations of the knowledge graphs, only the representative of these components is compared during the matching process. The framework has outperformed state-of-the-art knowledge graph matching algorithms using different scenarios as input in the experiments. In addition, to confirm the usability of our suggested framework, an in-depth experimental analysis has been done; the results are very promising in both runtime and accuracy.publishedVersio

An ontology matching approach for semantic modeling: A case study in smart cities

This paper investigates the semantic modeling of smart cities and proposes two ontology matching frameworks, called Clustering for Ontology Matching-based Instances (COMI) and Pattern mining for Ontology Matching-based Instances (POMI). The goal is to discover the relevant knowledge by investigating the correlations among smart city data based on clustering and pattern mining approaches. The COMI method first groups the highly correlated ontologies of smart-city data into similar clusters using the generic k-means algorithm. The key idea of this method is that it clusters the instances of each ontology and then matches two ontologies by matching their clusters and the corresponding instances within the clusters. The POMI method studies the correlations among the data properties and selects the most relevant properties for the ontology matching process. To demonstrate the usefulness and accuracy of the COMI and POMI frameworks, several experiments on the DBpedia, Ontology Alignment Evaluation Initiative, and NOAA ontology databases were conducted. The results show that COMI and POMI outperform the state-of-the-art ontology matching models regarding computational cost without losing the quality during the matching process. Furthermore, these results confirm the ability of COMI and POMI to deal with heterogeneous large-scale data in smart-city environments.publishedVersio

Matching Weak Informative Ontologies

Most existing ontology matching methods utilize the literal information to discover alignments. However, some literal information in ontologies may be opaque and some ontologies may not have sufficient literal information. In this paper, these ontologies are named as weak informative ontologies (WIOs) and it is challenging for existing methods to matching WIOs. On one hand, string-based and linguistic-based matching methods cannot work well for WIOs. On the other hand, some matching methods use external resources to improve their performance, but collecting and processing external resources is still time-consuming. To address this issue, this paper proposes a practical method for matching WIOs by employing the ontology structure information to discover alignments. First, the semantic subgraphs are extracted from the ontology graph to capture the precise meanings of ontology elements. Then, a new similarity propagation model is designed for matching WIOs. Meanwhile, in order to avoid meaningless propagation, the similarity propagation is constrained by semantic subgraphs and other conditions. Consequently, the similarity propagation model ensures a balance between efficiency and quality during matching. Finally, the similarity propagation model uses a few credible alignments as seeds to find more alignments, and some useful strategies are adopted to improve the performance. This matching method for WIOs has been implemented in the ontology matching system Lily. Experimental results on public OAEI benchmark datasets demonstrate that Lily significantly outperforms most of the state-of-the-art works in both WIO matching tasks and general ontology matching tasks. In particular, Lily increases the recall by a large margin, while it still obtains high precision of matching results