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The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure.

The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part.

The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain.

In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers.

The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

The purpose of this paper is to present an automated ontology mapping and merging algorithm, namely OntoDNA, which employs data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities among distributed data sources in organizational memory and subsequently generate a merged ontology to facilitate resource retrieval from distributed resources for organizational decision making.

The OntoDNA employs unsupervised data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities to integrate distributed data sources in organizational memory. Unsupervised methods are needed as an alternative in the absence of prior knowledge for managing this knowledge. Given two ontologies that are to be merged as the input, the ontologies' conceptual pattern is discovered using FCA. Then, string normalizations are applied to transform their attributes in the formal context prior to lexical similarity mapping. Mapping rules are applied to reconcile the attributes. Subsequently, SOM and K‐means are applied for semantic similarity mapping based on the conceptual pattern discovered in the formal context to reduce the problem size of the SOM clusters as validated by the Davies‐Bouldin index. The mapping rules are then applied to discover semantic similarity between ontological concepts in the clusters and the ontological concepts of the target ontology are updated to the source ontology based on the merging rules. Merged ontology in a concept lattice is formed.

In experimental comparisons between PROMPT and OntoDNA ontology mapping and merging tool based on precision, recall and f‐measure, average mapping results for OntoDNA is 95.97 percent compared to PROMPT's 67.24 percent. In terms of recall, OntoDNA outperforms PROMPT on all the paired ontology except for one paired ontology. For the merging of one paired ontology, PROMPT fails to identify the mapping elements. OntoDNA significantly outperforms PROMPT due to the utilization of FCA in the OntoDNA to capture attributes and the inherent structural relationships among concepts. Better performance in OntoDNA is due to the following reasons. First, semantic problems such as synonymy and polysemy are resolved prior to contextual clustering. Second, unsupervised data mining techniques (SOM and K‐means) have reduced problem size. Third, string matching performs better than PROMPT's linguistic‐similarity matching in addressing semantic heterogeneity, in context it also contributes to the OntoDNA results. String matching resolves concept names based on similarity between concept names in each cluster for ontology mapping. Linguistic‐similarity matching resolves concept names based on concept‐representation structure and relations between concepts for ontology mapping.

The OntoDNA automates ontology mapping and merging without the need of any prior knowledge to generate a merged ontology. String matching is shown to perform better than linguistic‐similarity matching in resolving concept names. The OntoDNA will be valuable for organizations interested in merging ontologies from distributed or different organizational memories. For example, an organization might want to merge their organization‐specific ontologies with community standard ontologies.

The purpose of this paper is to present ontology alignment as a basis for mobile service integration and invocation.

This paper presents an automated schema‐based approach to align the ontologies from interacting devices as a basis for mobile service invocation. When the ontologies are ambiguous about the services provided, compatibility vectors are introduced as a means of maintaining ontology quality and deciding which service to choose to reduce the ambiguity.

Both precision and recall measurements are applied in the evaluation of the alignment approach, with promising results. In addition, for the compatibility vector system, it is not only proved theoretically that the approach is both precise and efficient, but it also shows promising results experimentally.

In cases where sufficient resources are not available and only a certain number of mobile devices can be chosen for interaction, this approach increases the efficiency by choosing suitable mobile device(s).

This current approach makes use of a center ontology, but introduces the problem of how to handle the vulnerability issue inherent in this centralized solution. To analyze and solve this problem is a potential research direction.

Hybrid cloud computing is considered a viable and cost-effective approach to satisfy the inability of a private cloud to meet the user requirements. The information status of the selected public cloud service may change at runtime which should be reflected at the broker server database. This research illustrates a mechanism to assist the IaaS discovery system to assess the status change of the selected public cloud service and to update the broker server database. The paper aims to discuss these issues.

A prototype was developed with the broker server as the main component in the service discovery system containing the status information of the selected public cloud service. The merge-ontology and patch-update methods were proposed, the processing cost details of each were measured, and the methods were evaluated.

Experimental results showed that in the merge update, the merging process incurred much longer time than its required communication, contributing to long overall time. Relatively, the patch update method incurred much less time than its counterpart.

The proposed mechanism is experimental with some ideal assumptions, and so further work in real conditions is needed for its improvement.

This research is believed to be the first proposal to investigate ontology merge/patch methods to support ontology update in the broker server database of a hybrid cloud and will serve as a reference to researchers in the field.

There is an increasing recognition of the value of effective information and knowledge management (KM) in the construction project delivery process. Many architecture, engineering and construction (AEC) organisations have invested heavily in information technology and KM systems that help in this regard. While these have been largely successful in supporting intra‐organisational business processes, interoperability problems still persist at the project organisation level due to the heterogeneity of the systems used by the different organisations involved. Ontologies are seen as an important means of addressing these problems. The purpose of this paper is to explore the role of ontologies in the construction project delivery process, particularly with respect to information and KM.

A detailed technical review of the fundamental concepts and related work has been undertaken, with examples and case studies of ontology‐based information and KM presented to illustrate the key concepts. The specific issues and technical difficulties in the design and construction context are highlighted, and the approaches adopted in two ontology‐based applications for the AEC sector are presented.

The paper concludes that there is considerable merit in ontology‐based approaches to information and KM, but that significant technical challenges remain. Middleware applications, such as semantic web‐based information management system, are contributing in this regard but more needs to be done particularly on integrating or merging ontologies.

The value of the paper lies in the detailed exploration of ontology‐based information and KM within a design and construction context, and the use of appropriate examples and applications to illustrate the key issues.

Ontologies are prone to wide semantic variability due to subjective points of view of their composers. The purpose of this paper is to propose a new approach for maximal unification of diverse ontologies for controversial domains by their relations.

Effective matching or unification of multiple ontologies for a specific domain is crucial for the success of many semantic web applications, such as semantic information retrieval and organization, document tagging, summarization and search. To this end, numerous automatic and semi-automatic techniques were proposed in the past decade that attempt to identify similar entities, mostly classes, in diverse ontologies for similar domains. Apparently, matching individual entities cannot result in full integration of ontologies’ semantics without matching their inter-relations with all other-related classes (and instances). However, semantic matching of ontological relations still constitutes a major research challenge. Therefore, in this paper the authors propose a new paradigm for assessment of maximal possible matching and unification of ontological relations. To this end, several unification rules for ontological relations were devised based on ontological reference rules, and lexical and textual entailment. These rules were semi-automatically implemented to extend a given ontology with semantically matching relations from another ontology for a similar domain. Then, the ontologies were unified through these similar pairs of relations. The authors observe that these rules can be also facilitated to reveal the contradictory relations in different ontologies.

To assess the feasibility of the approach two experiments were conducted with different sets of multiple personal ontologies on controversial domains constructed by trained subjects. The results for about 50 distinct ontology pairs demonstrate a good potential of the methodology for increasing inter-ontology agreement. Furthermore, the authors show that the presented methodology can lead to a complete unification of multiple semantically heterogeneous ontologies.

This is a conceptual study that presents a new approach for semantic unification of ontologies by a devised set of rules along with the initial experimental evidence of its feasibility and effectiveness. However, this methodology has to be fully automatically implemented and tested on a larger dataset in future research.

This result has implication for semantic search, since a richer ontology, comprised of multiple aspects and viewpoints of the domain of knowledge, enhances discoverability and improves search results.

To the best of the knowledge, this is the first study to examine and assess the maximal level of semantic relation-based ontology unification.

The purpose of this research is to describe a system to support querying across distributed digital libraries created in heterogeneous metadata schemas, without requiring the availability of a global schema.

The advantages and weaknesses of ontology based applications were investigated and have justified the utility of inferential rules in expressing complex relations between metadata terms in different metadata schemas. A process for combining ontologies and rules for specifying complex relations between metadata schemas were designed. The process was collapsed into a set of working phases and provides examples to illustrate how to interrelate two similar bibliographic ontology fragments for further query reformulation.

Equipping ontologies with inferencing power can help describe more complex relations between metadata terms. This approach is critical for properly interpreting queries from one ontology to another.

A prototype system was built based on examples instead of practical experience.

The approach assumes that relations between metadata sets, or ontologies in the approach, are provided by domain experts with/without ontology tools.

A new approach has been proposed for facilitating heterogeneous metadata interoperation in digital libraries as a way of empowering ontologies with rich reasoning capabilities. The traditional approach assumes a global schema controlled by a central or virtual server to provide mapping between local and external metadata schemas. A more flexible and dynamic environment was studied, i.e. P2P‐based digital libraries, where peers may join and leave freely.

By providing interoperability users can be supported in sharing and reusing vocabularies and knowledge. Ontology alignment plays an important role in the context of semantic interoperability. Usually ontology alignment tools generate results that are difficult to understand or assess. In order to enable users to check and improve alignment results and to understand their consequences information visualization techniques are used. The purpose of this paper is to discuss the relevant quality aspects in ontology alignment as well as current activities and available tools.

Based on a literature study quality measures for ontology alignment identified and requirements for visual ontology alignment are defined. As a proof of concepts a prototype called AlViz was developed.

Information visualization offers appropriate methods for the assessment of ontology alignment results. Different levels of detail and overview help users to navigate and understand the alignments. The assessment of semi‐structured resources by users involves learning activities. The neighborhood of the entity under investigation bears relevant semantic information. Therefore, assessment may include crisscrossing acquisition of knowledge representations and their semantics.

Along a comprehensive framework alignment assessment tasks are identified and visualization tool is introduced and applied which aims at making ontology alignment results manageable and comprehensible.

Ontologies are defined as consensual formal conceptualisation of shared knowledge. However, the explicit overlap between diverse ontologies is usually very low since they are typically constructed by different experts. Hence, the purpose of this paper is to suggest to exploit “wisdom of crowds” to assess the maximal potential for inter-ontology agreement on controversial domains.

The authors propose a scheme where independent ontology users can explicitly express their opinions on the specified set of ontologies. The collected user opinions are further employed as features for machine classification algorithm to distinguish between the consensual ontological relations and the controversial ones. In addition, the authors devised new evaluation methods to measure the reliability and accuracy of the presented scheme.

The accuracy of the relation classification (90 per cent) and the reliability of user agreement annotations were quite high (over 90 per cent). These results indicate a fair ability of the scheme to learn the maximal set of consensual relations out of the specified set of diverse ontologies.

The data sets and the group of participants in our experiments were of limited size and thus the presented results are promising but cannot be generalised at this stage of research.

A diversity of opinions expressed by different ontologies has to be resolved in order to digitise many domains of knowledge (e.g. cultural heritage, folklore, medicine, economy, religion, history, art). This work presents a methodology to formally represent this diverse knowledge in a rich semantic scheme where there is a need to distinguish between the commonly shared and the controversial relations.

To the best of the knowledge this is a first proposal to consider crowd-based evaluation and classification of ontological relations to maximise the inter-ontology agreement.

The purpose of this research is to articulate an analysis framework and a method for the cross‐national business‐to‐business integration electronic commerce (B2Bi EC) by exploring an ontology‐assisted schema and semantics resolution in the business process alignment with electronic commerce standards.

The paper presents an ontology‐assisted analysis method and alignment model in the implementation of the B2B electronic commerce standard specification over the existing trading partners' public processes in the syntactic and semantic integration and interoperability. An application of the Unified Modeling Language is made to analyze the public process in the domain and in the standard. Terms, concepts, relations, and links are created from the analysis results and converted into an ontology representation. Web Ontology Language is introduced to formulate the analyzed knowledge and experience to align the domain and the standard. There are correspondences and conflicts in the process of alignment. They are resolved via the shared and reusable ontology which is a convergence of the domain ontology and the standard ontology. The converged and shared ontology is achieved via a set of rules and heuristics that are created in the research.

The key of success in the B2Bi EC lies in the ability to accomplish the process interoperability and the schema comparability. Three main tasks have to be achieved to fulfill the requirements. This research constructs a prototype to implement the method. The prototype is used to illustrate the feasibility and validity of the method. A set of starter experiments has been conducted in use of a straight‐through example of a purchase order process in the alignment with the RosettaNet standard and the ebXML standard. The starter experiment serves as the baseline to demonstrate that the method is feasible and valid.

A syntactic and semantic analysis method and alignment model are developed and demonstrated in the research. Integration and interoperability are accomplished in use of the systematic and analytic method.