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With the increasing spread of ontologies in various domains, units have gradually become an essential part of ontologies and units ontologies have been developed to offer a better expression ability for the practical usage. From the perspectives of architecture, comparison and reuse, the purpose of this paper is to provide a comprehensive survey on four mainstream units ontologies: quantity-unit-dimension-type, quantities, units, dimensions and values, ontology of units of measure and units ontology (UO) of the open biomedical ontologies, in order to address well the state of the art and the reuse strategies of the UO.

An architecture of units ontologies is presented, in which the relations between key factors (i.e. units of measure, quantity and dimension) are discussed. The criteria for comparing units ontologies are developed from the perspectives of organizational structure, pattern design and application scenario. Then, the authors compare four typical units ontologies based on the proposed comparison criteria. Furthermore, how to reuse these units ontologies is discussed in materials science domain by utilizing two reuse strategies of partial reference and complete reference.

Units ontologies have attracted high attention in the scientific domain. Based on the comparison of four popular units ontologies, this paper finds that different units ontologies have different design features from the perspectives of basis structure, units conversion and axioms design; a UO is better to be applied to the application areas that satisfy its design features; and many challenges remain to be done in the future research of the UO.

This paper makes an extensive review on units ontologies, by defining the comparison criteria and discussing the reuse strategies in the materials domain. Based on this investigation, guidelines are summarized for the selection and reuse of units ontologies.

Nowadays, experience bases are widely used by project companies in designing software-intensive systems (SISs). The efficiency of such informational sources is defined by the “nature” of modeled experience units and approaches that apply to their systematization. This paper aims to increase the efficiency of designing the SISs by the use of an ontological support for interactions with an accessible experience, models of which are understood as intellectually processed conditioned reflexes.

Both of the base of experience (BE) and ontological support in interactions with its units are oriented on precedents built in accordance with the offered normative schema when the occupational work is fulfilled by a team of designers. In creating the BE and the ontology as part of the BE, the team should use a reflection of an operational space of solved tasks on a specialized semantic memory intended for simulating the applied reasoning of the question-answer type.

If the occupational space of designing is reflected on the semantic memory with a programmable shell, then this environment can be adjusted on simulating the intellectual mechanisms flown in a human consciousness when designers ontologically interact with the BE and tasks being solved. The use of simulating the process in consciousness in accordance with their nature facilitates increasing the efficiency of designing the SIS.

An orientation on a precedent model as a basic type of experience unit and an ontological approach to their systematization are defined by the specificity of the study described in this paper. Models of precedents are constructed in accordance with the normative schema when the occupational work is fulfilled by a team of designers.

Investigated and developed means of ontological support are oriented on effective designing of the SISs with the use of the toolkit Working In Questions and Answers (WIQA) by the team of designers. The achieved effects are aimed at increasing the level of success in collaborative designing of SISs.

Offered solutions are applicable in designing the systems which supported different relations of a human with artificial and natural environment. They facilitate the naturalness in interactions of a human with computerized world.

An orientation on the precedent model as a basic type of experience unit and the ontological approach to their systematization are defined by the specificity of the study described in this paper. The novelty of this approach is defined by the framework for the precedent model, understood as the intellectually processed conditioned reflex, in which a reflection on the semantic memory (of the question-answer type) is programmable in a conceptually algorithmic language. The ontological support is implemented in the environment of programming.

The purpose of this paper is to describe the development of a knowledgebase supporting a prototype Europe‐wide time‐spatial search interface for historical resources. It discusses how this structure could be used to access other types of digital heritage content.

By using a relational database with spatial capabilities, a multi‐lingual search structure has been created which supports a temporal map and a facetted browser, collaborative facilities and a heritage resource viewer, with links to online catalogues.

Combining data from three states with very different histories identified the strengths and weaknesses of the approach. The framework's flexibility means it could easily be re‐purposed to support front ends to other types of cultural content.

Due to the variability in the administrative unit source data, its initial integration required significant manipulation to achieve consistency; however, the benefits of data assimilation ensure the base framework is as efficient as possible.

Usage levels of on‐line cultural resources will be far higher if ordinary users can access them via access routes which have meaning for them, such as by locality/place. Ontology‐based geographical frameworks are much easier to search by place name than are conventional GIS systems, and vastly more accessible to search engines.

This interface demonstrates the potential for integrating data from any number of different national organisations into a single user tool. It also highlights the potential for utilising the underlying structure in multiple contexts.

Managing components' resources plays a critical role in the success of systems' architectures designed for cyber–physical systems (CPS). Performing the selection of candidate components to pursue a specific application's needs also involves identifying the relationships among architectural components, the network and the physical process, as the system characteristics and properties are related.

Using a Model-Driven Engineering (MDE) approach is a valuable asset therefore. Within this context, the authors present the so-called Systems Architecture Ontology (SAO), which allows the representation of a system architecture (SA), as well as the relationships, characteristics and properties of a CPS application.

SAO uses a common vocabulary inspired by the Architecture Analysis and Design Language (AADL) standard. To demonstrate SAO's applicability, this paper presents its use as an MDE approach combined with ontology-based modeling through the Ontology Web Language (OWL). From OWL models based on SAO, the authors propose a model transformation tool to extract data related to architectural modeling in AADL code, allowing the creation of a components' library and a property set model. Besides saving design time by automatically generating many lines of code, such code is less error-prone, that is, without inconsistencies.

To illustrate the proposal, the authors present a case study in the aerospace domain with the application of SAO and its transformation tool. As result, a library containing 74 components and a related set of properties are automatically generated to support architectural design and evaluation.

The purpose of this paper is to present a conceptual model for coding and dissemination of data associated with historical photographic archives. The model is based on Linked Open Data technology and seeks to exhaustively represent the most relevant characteristics for the tasks of contextualization of the documentary groupings and units, management, document retrieval, dissemination and sharing of data about the historical photographs.

An OWL ontology, called Ontophoto, was constructed following an adaptation of the methodology proposed by Uschold and Gruninger and Gruninger and Fox. The ontology was implemented using Protégé 5.5 software. Next a Graph DB® graph database application (Ontotext) was created to generate a query system based on the SPARQL language. To validate the consistency and effectiveness of the model and ontology, a competency questions methodology has been applied using a sample from the Skogler photographic archive.

The model facilitates the generation of systems for dissemination and retrieval of iconographic data for historical research, overcoming some of the limitations with respect to the design of methods of content and contextual information representation for heritage photographic archives.

This study is based on a sample. Future work should consider the implementation of the model on the totality of a photographic collection.

This paper presents a comprehensive ontological model that allows the creation of distributed systems of knowledge representation, which can be queried through SPARQL language.

This study aims to the OQuaRE quality model to the developed methodology.

Ontologies are formal, well-defined and flexible representations of knowledge related to a specific domain. They provide the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. Then, it is necessary to define how to compare such ontologies to decide which one is the most suitable for the specific needs of users/developers. As the emerging development of ontologies, several studies have proposed criteria to evaluate them.

In a previous study, the authors propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural and Domain Knowledge levels, considering correctness and quality perspectives. As the evaluation methods of the proposal are based on a golden-standard, it can be customized to compare ontologies in any domain.

To show the suitability of the proposal, the authors apply the methodological approach to conduct comparative studies of ontologies in two different domains, one in the robotic area, in particular for the simultaneous localization and mapping (SLAM) problem; and the other one, in the cultural heritage domain. With these cases of study, the authors demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domains.

Using these metrics and the quality model from OQuaRE, the authors are incorporating a standard of software engineering at the quality validation into the Semantic Web.

The purpose of this study is to achieve accurate matching of new process cases to historical process cases and then complete the reuse of process knowledge and assembly experience.

By integrating case-based reasoning (CBR) and ontology technology, a multilevel assembly ontology is proposed. Under the general framework, the knowledge of the assembly domain is described hierarchically and associatively. On this basis, an assembly process case matching method is developed.

By fully considering the influence of ontology individual, case structure, assembly scenario and introducing the correction factor, the similarity between non-correlated parts is significantly reduced. Compared with the Triple Matching-Distance Model, the degree of distinction and accuracy of parts matching are effectively improved. Finally, the usefulness of the proposed method is also proved by the matching of four practical assembly cases of precision components.

The process knowledge in historical assembly cases is expressed in a specific ontology framework, which makes up for the defects of the traditional CBR model. The proposed matching method takes into account all aspects of ontology construction and can be used well in cross-ontology similarity calculations.

Large collections of patent documents disclosing novel, non-obvious technologies are publicly available and beneficial to academia and industries. To maximally exploit its potential, searching these patent documents has increasingly become an important topic. Although much research has processed a large size of collections, a few studies have attempted to integrate both patent classifications and specifications for analyzing user queries. Consequently, the queries are often insufficiently analyzed for improving the accuracy of search results. This paper aims to address such limitation by exploiting semantic relationships between patent contents and their classification.

The contributions are fourfold. First, the authors enhance similarity measurement between two short sentences and make it 20 per cent more accurate. Second, the Graph-embedded Tree ontology is enriched by integrating both patent documents and classification scheme. Third, the ontology does not rely on rule-based method or text matching; instead, an heuristic meaning comparison to extract semantic relationships between concepts is applied. Finally, the patent search approach uses the ontology effectively with the results sorted based on their most common order.

The experiment on searching for 600 patent documents in the field of Logistics brings better 15 per cent in terms of F-Measure when compared with traditional approaches.

The research, however, still requires improvement in which the terms and phrases extracted by Noun and Noun phrases making less sense in some aspect and thus might not result in high accuracy. The large collection of extracted relationships could be further optimized for its conciseness. In addition, parallel processing such as Map-Reduce could be further used to improve the search processing performance.

The experimental results could be used for scientists and technologists to search for novel, non-obvious technologies in the patents.

High quality of patent search results will reduce the patent infringement.

The proposed ontology is semantically enriched by integrating both patent documents and their classification. This ontology facilitates the analysis of the user queries for enhancing the accuracy of the patent search results.

Semantic Web is an extension of the World Wide Web by tagging content with “meaning”. In general, question answering systems based on semantic Web face a number of difficult issues. This paper aims to design an experimental environment with custom rules and scalable data sets and evaluate the performance of a proposed optimized backward chaining ontology reasoning system. This study also compares the experimental results with other ontology reasoning systems to show the performance and scalability of this ontology reasoning system.

The authors proposed a semantic question answering system. This system has been built using ontological knowledge base including optimized backward chaining ontology reasoning system and custom rules. With custom rules, the proposed semantic question answering system will be able to answer questions that contain qualitative descriptors such as “groundbreaking” resesarch and “tenurable at university x”. Scalability has been one of the difficult issues faced by an optimized backward chaining ontology reasoning system and semantic question answering system. To evaluate the proposed ontology reasoning system, first, the authors design a number of innovative custom rule sets and corresponding query sets. The innovative custom rule sets and query sets will contribute to the future research on evaluating ontology reasoning systems as well. Then they design an experimental environment including ontologies and scalable data sets and metrics. Furthermore, they evaluate the performance of the proposed optimized backward chaining reasoning system on supporting custom rules. The evaluation results have been compared with other ontology reasoning systems as well.

The proposed innovative custom rules and query sets can be effectively employed for evaluating ontology reasoning systems. The evaluation results show that the scalability of the proposed backward chaining ontology reasoning system is better than in-memory reasoning systems. The proposed semantic question answering system can be integrated in sematic Web applications to solve scalability issues. For light weight applications, such as mobile applications, in-memory reasoning systems will be a better choice.

This paper fulfils an identified need for a study on evaluating an ontology reasoning system on supporting custom rules with and without external storage.

Applied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications.

The underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing an analysis of the widely used Basic Formal Ontology (BFO) through digital methods and visualizations, and presents historical research alongside unstructured interview data with leading experts in BFO development.

The author found that ACOs are products of communal deliberation and decision making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data.

The paper presents a new approach for studying ACOs, the social impact of ACO work, and describes methods that may be used to produce further applied ontology studies.