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This systematic literature review (SLR) paper presents the overview and analysis of the existing ontologies application in the SE domain. It discusses the main challenges in terms of its ontologies development and highlights the key knowledge areas for subdomains in the SE domain that provides a direction to develop ontologies application for SE systematically. The SE is not as straightforward as the traditional economy. It transforms the existing economy ecosystem through peer-to-peer collaborations mediated by the technology. Hence, the complexity of the SE domain accentuates the need to make the SE domain knowledge more explicit.

For the review, the authors only focus on the journal articles published from 2010 to 2020 and mentioned ontology as a solution to overcome the issues specific for the SE domain. The initial identification process produced 3,326 papers from 10 different databases.

After applying the inclusion and exclusion criteria, a final set of 11 articles were then analyzed and classified. In SE, good ontology design and development is essential to manage digital platforms, deal with data heterogeneity and govern the interoperability of the SE systems. Yet the preference to build an application ontology, lack of perdurant design and minimal use of the existing standard for building SE common knowledge are deterring the ontology development in this domain. From this review, an anatomy of the SE key subdomain areas is visualized as a reference to further develop the domain ontology for the SE domain systematically.

With the arrival of the Fourth Industrial Revolution (4IR), the sharing economy (SE) has become one of the important domains whose impact has been explosive, and its domain knowledge is complex. Yet, a comprehensive overview and analysis of the ontology applications in the SE domain is not available or well presented to the research community.

This paper investigates the semantic association mechanisms of weak demand signals that facilitate innovative product development in terms of conceptual and temporal precedence, despite their inherent ambiguity and uncertainty.

To address this challenge, a domain ontology approach is proposed to construct a customer demand scenario-based framework that eliminates the blind spots in weak demand signal identification. The framework provides a basis for identifying such signals and introduces evaluation indices, such as depth, novelty and association, which are integrated to propose a three-dimensional weak signal recognition model based on domain ontology that outperforms existing research.

Empirical analysis is carried out based on customer comments of new energy vehicles on car platform such as “Auto Home” and “Bitauto”. Results demonstrate that in terms of recognition quantity, the three-dimensional weak demand signal recognition model, based on domain ontology, can accurately identify six demand weak signals. Conversely, the keyword analysis method exhibits a recognition quantity of four weak signals; in terms of recognition quality, the three-dimensional weak demand signal recognition model based on domain ontology can exclude non-demand signals such as “charging technology”, while keyword analysis methods cannot. Overall, the model proposed in this paper has higher sensitivity.

This paper proposes a novel method for identifying weak demand signals that considers the frequency of the signal's novelty, depth and relevance to the target demand. To verify its effectiveness, customer review data for new energy vehicles is used. The results provide a theoretical reference for formulating government policies and identifying weak demand signals for businesses.

The purpose of this paper is to identify the benefits of an approach in which document management systems (DMSs) are based on a formal and explicit document model, primarily in terms of facilitating domain-specific customization.

Within this paper, a generic document model is proposed. The model consists of two layers. A general purpose layer, which represents common features of the documents, and a domain-specific layer, modeling properties particular to application domain. The general purpose layer is based on ISO 82045, providing high degree of interoperability with other systems developed with respect to this set of standard.

Splitting document model into the layers enables DMSs to be tailored for each particular domain of application, depending on the general purpose layer. The existence of domain-specific layer allows documents to be interpreted differently in different domains of application.

In order to enable customization of DMS for a particular domain, the implementation of domain-specific document layer is required. Also, the proposed model does not explicitly deal with document dynamics.

The proposed document ontology is general enough to provide the representation of documents not depending on a specific scope of application, yet flexible enough to enable extensions through which domain-specific document features can be expressed. The separation of document model enables development of core DMS offering services relying explicitly on the general purpose layer on one hand, as well as domain-specific customization of DMS on the other.

Web site recommendation systems help to get high quality information. The modelling of recommendation systems involves the combination of many features: metrics of quality, quality criteria, recommendation criteria, user profile, and specific domain concepts, among others. At the moment of the specification of a recommendation system it must be guaranteed a right interrelation of all of these features. The purpose of this paper is to model a web site quality‐based recommendation system by an ontology network.

In this paper, the authors propose an ontology network based process for web site recommendation modelling. The ontology network conceptualizes the different domains (web site domain, quality assurance domain, user context domain, recommendation criteria domain, specific domain) in a set of interrelated ontologies. Particularly, this approach is illustrated for the health domain.

Basically, this work introduces the semantic relationships that were used to construct this ontology network. Moreover, it shows the usefulness of this ontology network for the detection of possible inconsistencies when specifying recommendation criteria.

Recommendation systems based on ontologies that model the user profile and the domain of resources to be recommended are quite common. However, it is uncommon to find models that explicitly represent the criteria used by the recommender systems, that express the quality dimensions of resources and on which criteria are applied, and consider the user context at the moment of the query.

The purpose of the research is to speed up the process of semantic web services by transformation of current Web services into semantic web services. This can be achieved by applying ontology learning techniques to automatically extract domain ontologies.

The work here presents a Service Ontology Learning Framework (SOLF), the core aspect of which extracts Structured Interpretation Patterns (SIP). These patterns are used to automate the acquisition (from production domain specific Web Services) of ontological concepts and the relations between those concepts.

A Semantic Web of accessible and re‐usable software services is able to support the increasingly dynamic and time‐limited development process. This is premised on the efficient and effective creation of supporting domain ontology.

Though WSDL documents provide important application level service description, they alone are not sufficient for OL however, as: they typically provide technical descriptions only; and in many cases, Web services use XSD files to provide data type definitions. The need to include (and combine) other Web service resources in the OL process is therefore an important one.

Web service domain ontologies are the general means by which semantics are added to Web services; typically used as a common domain model and referenced by annotated or externally described Web artefacts (e.g. Web services). The development and deployment of Semantic Web services by enterprises and the wider business community has the potential to radically improve planned and ad‐hoc service re‐use. The reality is slower however, in good part because the development of an appropriate ontology is an expensive, error prone and labor intensive task. The proposed SOLF framework is aimed to overcome this problem by contributing a framework and a tool that can be used to build web service domain ontologies automatically.

The output of the SOLF process is an automatically generated OWL domain ontology, a basis from which a future Semantic Web Services can be delivered using existing Web services. It can be seen that the ontology created moves beyond basic taxonomy – extracting and relating concepts at a number of levels. More importantly, the approach provides integrated knowledge (represented by the individual WSDL documents) from a number of domain experts across a group of banks.

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.

This paper aims to propose a brand new ontology development methodology, called Yet Another Methodology for Ontology (YAMO) and demonstrate, step by step, the building of a formally defined large-scale faceted ontology for food.

YAMO is motivated by facet analysis and an analytico-synthetic classification approach. The approach ensures quality of the system precisely; it makes the system flexible, hospitable, extensible, sturdy, dense and complete. YAMO consists of two-way approaches: top-down and bottom-up. Based on YAMO, domain food, formally defined as large-scale ontology, is designed. To design the ontology and to define the scope and boundary of the domain, a group of people were interviewed to get a practical overview, which provided more insight to the theoretical understanding of the domain.

The result obtained from evaluating the ontology is a very impressive one. Based on the study, it was found that 94 per cent of the user’s queries were successfully met. This shows the efficiency and effectiveness of the YAMO methodology. An evaluator opined that the ontology is very deep and exhaustive.

The authors envision that the current work will have great implications on ontology developers and practitioners. YAMO will allow ontologists to construct a very deep, high-quality and large-scale ontology.

This paper illustrates a brand new ontology development methodology and demonstrates how the methodology can be applied to build a large-scale high-quality domain ontology.

In e-government domain, an efficient semantic interoperability of services is a big challenge. In this context, semantic technologies play a fundamental role in numerous e-government programs. Indeed, these technologies enable enriching the public service description provided by different public administrations with additional semantic information, which allows automatic services processing and information exchange between involved members in comprehensive and interpretable manner, thus facilitating service integration and cooperation. The most frequently used technologies in this area are semantic web services (SWS) technology and ontology. Thus, for modelling efficiently public services, the authors have exploring, studying and analyzing some of e-government researches and projects that applied semantic technologies.

In this paper, the authors address how semantic technologies transform the e-government domain through exploring and studying the use of SWS and ontology in numerous researches and projects applying these technologies in this area. Then, the authors have compared them through a set of criteria defined according to e-government requirements such as the SWS modelling approach for describing semantically public services, ontologies for representing governmental specific features (e.g. regulation and organizational structures) and developed portal for supporting user ' s perspectives. Based on this study, the authors have identified the appropriate standards, frameworks, and models for developing the framework.

The authors have developed a framework for public services which enhances existing approaches with additional aspects. Thus, the approach comparing to other works improves the service ontology with a cognitive semantic approach to support the multiple usage contexts and situations of services. This semantic is developed in form of a Sit/Ctx ontology developed based on CSs model represented through conceptual graphs theory. The authors have used this model besides OWL-S, improving thus the level of automation of discovery and composition process of public services, which become more efficient with taken into consideration their interrelations and situations.

The approach comparing to other works improves the service ontology with a cognitive semantic approach to support the multiple usage contexts and situations of public services, providing thus the end-users (people and enterprises) a better orientation in discovery process.

The purpose of this paper is to discuss sentiment search, which not only retrieves data related to submitted keywords but also identifies sentiment opinion implied in the retrieved data and the subject targeted by this opinion.

The authors propose a retrieval framework known as Cross-Domain Sentiment Search (CSS), which combines the usage of domain ontologies with specific linguistic rules to handle sentiment terms in textual data. The CSS framework also supports incrementally enriching domain ontologies when applied in new domains.

The authors found that domain ontologies are extremely helpful when CSS is applied in specific domains. In the meantime, the embedded linguistic rules make CSS achieve better performance as compared to data mining techniques.

The approach has been initially applied in a real social monitoring system of a professional IT company. Thus, it is proved to be able to handle real data acquired from social media channels such as electronic newspapers or social networks.

The authors have placed aspect-based sentiment analysis in the context of semantic search and introduced the CSS framework for the whole sentiment search process. The formal definitions of Sentiment Ontology and aspect-based sentiment analysis are also presented. This distinguishes the work from other related works.

The purpose of this article is to examine the effectiveness of the unified medical language system (UMLS) semantic network as a seed ontology for building a medical field ontology.

The information extraction process known as the knowledge engineering approach was used to extract concepts and their semantic relations from documents on “colon cancer treatment”. The UMLS semantic network was used as a seed ontology, and was extended and enriched with the extracted concepts and semantic relations using Protégé.

Only half of the semantic relations extracted manually were defined (or inferable) in the UMLS semantic network. The remaining half could be added to the network to extend its coverage. In addition, two semantic types in the network were found to be too general and four new sublevel semantic types were proposed to make them more specific.

Since only 109 research paper abstracts in the “colon cancer treatment” domain were analyzed in this study, more abstracts from the colon cancer treatment domain as well as from other cancer treatment domains (such as breast cancer treatment) can be analyzed to give a better generalization of our findings.

This study shares our findings on the effectiveness of the UMLS semantic network as a seed ontology for building a medical domain ontology, and also provides the basic guidelines for building or extending a medical domain ontology using the UMLS.