Ontology Matching Techniques Research Articles

Definition of an Ontology Matching Algorithm for Context Integration in Smart Cities.

In this paper we describe a novel proposal in the field of smart cities: using an ontology matching algorithm to guarantee the automatic information exchange between the agents and the smart city. A smart city is composed by different types of agents that behave as producers and/or consumers of the information in the smart city. In our proposal, the data from the context is obtained by sensor and device agents while users interact with the smart city by means of user or system agents. The knowledge of each agent, as well as the smart city's knowledge, is semantically represented using different ontologies. To have an open city, that is fully accessible to any agent and therefore to provide enhanced services to the users, there is the need to ensure a seamless communication between agents and the city, regardless of their inner knowledge representations, i.e., ontologies. To meet this goal we use ontology matching techniques, specifically we have defined a new ontology matching algorithm called OntoPhil to be deployed within a smart city, which has never been done before. OntoPhil was tested on the benchmarks provided by the well known evaluation initiative, Ontology Alignment Evaluation Initiative, and also compared to other matching algorithms, although these algorithms were not specifically designed for smart cities. Additionally, specific tests involving a smart city's ontology and different types of agents were conducted to validate the usefulness of OntoPhil in the smart city environment.

Automatic background knowledge selection for matching biomedical ontologies.

Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of background knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains such as the life sciences. However, in most ontology matching systems, the background knowledge sources are either predefined by the system or have to be provided by the user. In this paper, we present a novel methodology for automatically selecting background knowledge sources for any given ontologies to match. This methodology measures the usefulness of each background knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which we call the mapping gain. We implemented this methodology in the AgreementMakerLight ontology matching framework, and evaluate it using the benchmark biomedical ontology matching tasks from the Ontology Alignment Evaluation Initiative (OAEI) 2013. In each matching problem, our methodology consistently identified the sources of background knowledge that led to the highest improvements over the baseline alignment (i.e., without background knowledge). Furthermore, our proposed mapping gain parameter is strongly correlated with the F-measure of the produced alignments, thus making it a good estimator for ontology matching techniques based on background knowledge.

An adaptive semantic based mediation system for data interoperability among Health Information Systems.

Heterogeneity in the management of the complex medical data, obstructs the attainment of data level interoperability among Health Information Systems (HIS). This diversity is dependent on the compliance of HISs with different healthcare standards. Its solution demands a mediation system for the accurate interpretation of data in different heterogeneous formats for achieving data interoperability. We propose an adaptive AdapteR Interoperability ENgine mediation system called ARIEN, that arbitrates between HISs compliant to different healthcare standards for accurate and seamless information exchange to achieve data interoperability. ARIEN stores the semantic mapping information between different standards in the Mediation Bridge Ontology (MBO) using ontology matching techniques. These mappings are provided by our System for Parallel Heterogeneity (SPHeRe) matching system and Personalized-Detailed Clinical Model (P-DCM) approach to guarantee accuracy of mappings. The realization of the effectiveness of the mappings stored in the MBO is evaluation of the accuracy in transformation process among different standard formats. We evaluated our proposed system with the transformation process of medical records between Clinical Document Architecture (CDA) and Virtual Medical Record (vMR) standards. The transformation process achieved over 90 % of accuracy level in conversion process between CDA and vMR standards using pattern oriented approach from the MBO. The proposed mediation system improves the overall communication process between HISs. It provides an accurate and seamless medical information exchange to ensure data interoperability and timely healthcare services to patients.

An evaluation of ontology matching techniques on geospatial ontologies

Standardization is one of the pillars of interoperability. In this context, efforts promoted by the Open Geospatial Consortium, such as CityGML (Technical University, Berlin), a standard for exchanging three-dimensional models or urban city objects, are welcomed. However, information from other domains of interest (e.g. energy efficiency or building information modeling) is needed for tasks such as land planning, large-scale flooding analysis, or demand/supply energy simulations. CityGML allows extension in order to integrate information from other domains, but the development process is expensive because there is no way to perform it automatically. The discovery of correspondences between CityGML concepts and other domains concepts poses a significant challenge.Ontology matching is the research field emerged from the Semantic Web to address automatic ontology integration. Using the ontology underlying CityGML and the ontologies which model other domains of interest, ontology matching would be able to find the correspondences that would permit the integration in a more automatic manner than it is done now.In this paper, we evaluate if ontology matching techniques allow performing an automatic integration of geospatial information modeled from different viewpoints. In order to achieve this, an evaluation methodology was designed, and it was applied to the discovery of relationships between CityGML and ontologies coming from the building information modeling and Geospatial Semantic Web domains. The methodology and the results of the evaluation are presented. The best results have been achieved using string-based techniques, while matching systems give the worst precision and recall. Only in a few cases the values are over 50%, which shows the limitations when these techniques are applied to ontologies with a partial overlap.

Facilitating the Automatic Mapping of IEC 61850 Signals and CIM Measurements

One of the main interoperability issues in electric power systems concerns the interactions between IEC 61850 and CIM standards. The state of the art includes solutions to facilitate the exchange of configuration files between the two standards. Nevertheless, there are no contributions in the literature aimed at facilitating the run-time interactions between IEC 61850-based and CIM-based systems. Thus, at present, the signal mapping between these systems must be carried out manually. This paper presents a new methodology that helps to automate this process by utilizing ontology matching techniques. The proposed methodology was successfully evaluated with case studies based on four representative substation architectures.

A Framework for Alignment of Data and Processes Architectures Applied in a Government Institution

The alignment of business processes and information technologies (IT) is among the top concerns of IT management surveys, because it has a direct impact on the organization’s agility and flexibility to change in response to business needs. Previous works have proposed frameworks and methodologies for alignment management; however, they suppose manual tasks such as applying surveys or comparing artifacts. Enterprise architecture is a valuable instrument to assess and achieve such alignment. In that direction, we introduce a framework to support the alignment of business architecture (BA) and information architecture (IA) via the comparison of their constituent components applying ontology matching techniques. Our key objectives are: (1) to support the process of evaluating alignment BA–IA and (2) to detect potential alignments and misalignments between BA and IA. We apply this approach in the Colombian Institute for the Evaluation of Education through the evaluation of misalignment heuristics.

Building linked ontologies with high precision using subclass mapping discovery

The creation of links between schemas of published datasets is a key part of the Linked Open Data (LOD) paradigm. The ability to discover these links "on the go" requires that ontology matching techniques achieve good precision and recall within acceptable execution times. In this paper, we add similarity-based and mediator-based ontology matching methods to the Agreementmaker ontology matching system, which aim to efficiently discover high precision subclass mappings between LOD ontologies. Similarity-based matching methods discover subclass mappings by extrapolating them from a set of high quality equivalence mappings and from the interpretation of compound concept names. Mediator-based matching methods discover subclass mappings by comparing polysemic lexical annotations of ontology concepts and by considering external web ontologies. Experiments show that when compared with a leading LOD approach, Agreementmaker achieves considerably higher precision and F-measure, at the cost of a slight decrease in recall.

Semantic Heterogeneity Issues on the Web

To operate effectively, the Semantic Web must be able to make explicit the semantics of Web resources via ontologies, which software agents use to automatically process these resources. The Web's natural semantic heterogeneity presents problems, however - namely, redundancy and ambiguity. The authors' ontology matching, clustering, and disambiguation techniques aim to bridge the gap between syntax and semantics for Semantic Web construction. Their approach discovers and represents the intended meaning of words in Web applications in a nonredundant way, while considering the context in which those words appear.

Ontology Matching Approaches for eRecruitment

researches have been carried out in many diverse research areas in the past decade for numerous purposes especially in the eRecruitment domain. In this article, we would like to take a closer look on the current work of such domain of ontologies such as eRecruitment. Ontology application for e-Recruitment is becoming an important task for matching job postings and applicants semantically in a Semantic web technology using ontology and ontology matching techniques. Most of the reviewed papers used currently (existing) available widespread standards and classifications to build human resource ontology that provide a way of semantic representation for positions offered and candidates to fulfil, some of other researches have been done created their own HR ontologies to build recruitment prototype. We have reviewed number of articles and identified few purposes for which ontology matching techniques are applied so far and how these matching techniques used in that respective domain. KeywordsMatching, Human Resource Ontology, eRecruitment

Ontology Mapping for Dynamic Multiagent Environment

Ontologies are essential for the realization of the Semantic Web, which in turn relies on the ability of systems to identify and exploit relationships that exist between and within ontologies. As ontologies can be used to represent different domains, there is a high need for efficient ontology matching techniques that can allow information to be easily shared between different heterogeneous systems. There are various systems were proposed recently for ontology mapping. Ontology mapping is a prerequisite for achieving heterogeneous data integration on the Semantic Web. The vision of the Semantic Web implies that a large number of ontologies present on the web need to be aligned before one can make use of them. At the same time, these ontologies can be used as domain-specific background knowledge by the ontology mapping systems to increase the mapping precision. However, these ontologies can differ in representation, quality, and size that pose different challenges to ontology mapping. In this paper, we analyzed the various challenges of recently introduced Multi-Agent Ontology Mapping Framework, DSSim and we have integrated an efficient feature called QoS-Web Services Composition with DSSsim. ie we have improved this framework with QoS based Service Compositions Mechanism. From our experimental results, it is established that this developed QoS based Web Services Compositions Mechanism for Multiagent Ontology Mapping Framework minimizing uncertain reasoning and improves matching time, which are encouraging results of our proposed work..

Multimedia ontology matching by using visual and textual modalities

Ontologies have been intensively applied for improving multimedia search and retrieval by providing explicit meaning to visual content. Several multimedia ontologies have been recently proposed as knowledge models suitable for narrowing the well known semantic gap and for enabling the semantic interpretation of images. Since these ontologies have been created in different application contexts, establishing links between them, a task known as ontology matching, promises to fully unlock their potential in support of multimedia search and retrieval. This paper proposes and compares empirically two extensional ontology matching techniques applied to an important semantic image retrieval issue: automatically associating common-sense knowledge to multimedia concepts. First, we extend a previously introduced textual concept matching approach to use both textual and visual representation of images. In addition, a novel matching technique based on a multi-modal graph is proposed. We argue that the textual and visual modalities have to be seen as complementary rather than as exclusive sources of extensional information in order to improve the efficiency of the application of an ontology matching approach in the multimedia domain. An experimental evaluation is included in the paper.

A large dataset for the evaluation of ontology matching

AbstractRecently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large-scale evaluation datasets. In fact, the number of possible correspondences between two ontologies grows quadratically with respect to the numbers of entities in these ontologies. This often makes the manual construction of the evaluation datasets demanding to the point of being infeasible for large-scale matching tasks. In this paper, we present an ontology matching evaluation dataset composed of thousands of matching tasks, called TaxME2. It was built semi-automatically out of the Google, Yahoo, and Looksmart web directories. We evaluated TaxME2 by exploiting the results of almost two-dozen of state-of-the-art ontology matching systems. The experiments indicate that the dataset possesses the desired key properties, namely it iserror-free,incremental,discriminative,monotonic, andhardfor the state-of-the-art ontology matching systems.

Semantically routing queries in peer-based systems: the H-Link approach

AbstractA challenging issue to advance the existing P2P semantic routing protocols is related to the capability of developing mechanisms for focused selection of the query recipients by taking into account a semantically rich description of the context of each peer. In this article, we present the H-Link semantic routing approach designed to exploit the results of an ontology matchmaking process for providing a semantic overlay network where peers having similar contexts are recognized and interlinked as semantic neighbors. In particular, H-Linkaims at advancing the existing semantic routing protocols by combining ontology-based peer context descriptions and ontology matching techniques for providing query forwarding on a real semantic basis, in a completely decentralized way.