Marko Gulić

CroMatcher: An ontology matching system based on automated weighted aggregation and iterative final alignment

In order to perform ontology matching with high accuracy, while at the same time retaining applicability to most diverse input ontologies, the matching process generally incorporates multiple methods. Each of these methods is aimed at a particular ontology component, such as annotations, structure, properties or instances. Adequately combining these methods is one of the greatest challenges in designing an ontology matching system. In a parallel composition of basic matchers, the ability to dynamically set the weights of the basic matchers in the final output, thus making the weights optimal for the given input, is the key breakthrough for obtaining first-rate matching performance. In this paper we present CroMatcher, an ontology matching system, introducing several novelties to the automated weight calculation process. We apply substitute values for matchers that are inapplicable for the particular case and use thresholds to eliminate low-probability alignment candidates. We compare the alignments produced by the matchers and give less weight to the matchers producing mutually similar alignments, whereas more weight is given to those matchers whose alignment is distinct and rather unique. We also present a new, iterative method for producing one-to-one final alignment of ontology structures, which is a significant enhancement of similar non-iterative methods proposed in the literature. CroMatcher has been evaluated against other state-of-the-art matching systems at the OAEI evaluation contest. In a large number of test cases it achieved the highest score, which puts it among the state-of-the-art leaders.

Automated weighted aggregation in an ontology matching system

Ontology matching is the process of finding semantic relationships between entities of different ontologies. It plays an important role in the integration of heterogeneous data sources. To find semantic correspondences between entities, various matchers can be used. When multiple matchers are integrated into a matching system, the issue of combining these matchers together and determining the importance of every basic matcher arises. In a parallel composition of basic matchers, several matchers are used independently and then their results are aggregated. In this paper, we propose a method for automatically determining weights of basic matchers in their parallel composition and the architecture of the matching system. An evaluation of the method is performed on case study examples concerning the matching of four ontology pairs and a comparison with other aggregation methods is given. Additionally, the architecture of the matching system based on our automatic weighted aggregation method is presented.

Automatically Specifying Parallel Composition of Matchers in Ontology Matching Process

Ontology matching plays an important role in the integration of heterogeneous data sources. In order to find semantic correspondences between entities of different ontologies, various matchers can be used. When multiple matchers are integrated into a matching system, the issue of combining these matchers together and determining the importance of every basic matcher arises. In a parallel composition of basic matchers, several matchers are used independently, and then their results are aggregated. In this paper we propose a method for automatically determining weights of basic matchers in their parallel composition. An evaluation of the method is performed on case study examples concerning the matching of three ontology pairs, and a comparison with other aggregation methods is given.

Ontology Matching Using TF/IDF Measure with Synonym Recognition

Ontology matching is an important process for integration of heterogeneous data sources. A large number of different matchers for comparing ontologies exist. They can be classified into element-level and structure-level matchers. The element-level matchers compare entities ignoring their relations with other entities, while the structure-level matchers consider these relations. The TF/IDF (term frequency / inverse document frequency) measure is useful for specifying key terms weights in documents. In our matching system we use the TF/IDF measure for comparing documents that store data about ontology entities. However, the TF/IDF does not take synonyms into account, and it may occur that the terms that describe two entities the best are synonyms. In this paper we propose a matcher that combines the TF/IDF measure with synonym recognition when determining key term weights, in order to improve the results of ontology matching. Evaluation of the matcher is performed on case study examples.

The Role of Internet of Things on the Development of Ports as a Holder in the Supply Chain

The new technology greatly affects the way of production, consumption, communication, service delivery and ultimately on the entire supply chain. All stakeholders in the business process must invest in new knowledge and develop new business models to adapt to the changing business environment. Connecting devices over internet (Internet of things) and stakeholders’ synergy open up opportunities for new market achievements as well as for the improvement of business processes both in the supply chain and in ports. The development of information technologies has an impact on the reduction of errors, costs, time of information transfer and transport, inventory reduction and thus on better customization. There should be no weak links in the supply chain, which is especially related to the port and port processes that are the basis of the supply chain network. The port is the core of all activities of the supply chain and is also a place where supply and demand meet.

An Iterative Automatic Final Alignment Method in the Ontology Matching System

Ontology matching plays an important role in the integration of heterogeneous data sources that are described by ontologies. In order to determine correspondences between ontologies, a set of matchers can be used. After the execution of these matchers and the aggregation of the results obtained by these matchers, a final alignment method is executed in order to select appropriate correspondences between entities of compared ontologies. The final alignment method is an important part of the ontology matching process because it directly determines the output result of this process. In this paper we improve our iterative final alignment method by introducing an automatic adjustment of final alignment threshold as well as a new rule for determining false correspondences with similarity values greater than adjusted threshold. An evaluation of the method is performed on the test ontologies of the OAEI evaluation contest and a comparison with other final alignment methods is given.

Automatically Specifying a Parallel Composition of Matchers in Ontology Matching Process by Using Genetic Algorithm

Today, there is a rapid increase of the available data because of advances in information and communications technology. Therefore, many mutually heterogeneous data sources that describe the same domain of interest exist. To facilitate the integration of these heterogeneous data sources, an ontology can be used as it enriches the knowledge of a data source by giving a detailed description of entities and their mutual relations within the domain of interest. Ontology matching is a key issue in integrating heterogeneous data sources described by ontologies as it eases the management of data coming from various sources. The ontology matching system consists of several basic matchers. To determine high-quality correspondences between entities of compared ontologies, the matching results of these basic matchers should be aggregated by an aggregation method. In this paper, a new weighted aggregation method for parallel composition of basic matchers based on genetic algorithm is presented. The evaluation has confirmed a high quality of the new aggregation method as this method has improved the process of matching two ontologies by obtaining higher confidence values of correctly found correspondences and thus increasing the quality of matching results.