Domain Knowledge Representation and Processing

This paper describes an approach and an implementation for the semantic integration of social and domain ontologies in a collaborative platform. The platform, built based on the Drupal CMS framework, aims to offer SMEs possibilities to access specific knowledge by means of a collaborative community. A model to semantically express the socio-collaborative activities of the platform connected with the domain knowledge related to the project is presented. The model is based on existing W3C ontologies such as FOAF, SIOC and SIOC-types for the socio-collaborative semantics and SKOS to describe the domain knowledge. An architecture to support the model and related applications of the semantic metadata generated in the platform are also described.

The target of this paper is to explore the determinants of accounting manipulations under chief executive officer (CEO) perception in Tunisian listed firms. In fact, we aim to explore the effect of manager's psychological biases on accounts manipulations. Using the cognitive mapping technique, a mental model is presented respecting both classical and behavioural approaches. Besides the traditional variables, our results highlight that psychological variables lead also to manipulate accounting results. The most relevant ones are the level of optimism and overconfidence. These findings provide an important insight and recommendations for investors, auditors, financial analysts and regulators.

One key issue about Web service application is how to discover the desired services. Discovery Service is a key technology in service-oriented Web software development. Considering that the current Web services registered on the public UDDI lack semantic description, the researches on service discovery add semantic description about Web services. In actual applications, however, a lot of difficulties are found when using these methods. The goal of domain ontology is to capture the related domain knowledge, to provide common understanding about the domain knowledge, to determine the conceptions commonly recognized in this domain, and to well define these conceptions and the relationship between these conceptions. In this way, errors and failures generated due to misunderstanding the conceptions are reduced, function-based services are much easier to be discovered and combined, and in the meantime, the deficiency in keyword-based search technology of UDDI is made up. In view of the above, this document suggests a method to build domain ontology. Based on the domain ontology, this method presents the retrieve process of service components and implements a domain-ontology-based service search engine prototype system. This method has many benefits: refine and expand users' initial query, support fuzzy query, and improve recall and precision. Especially, with the Internet becoming a reusable software resource library, component query is sure to be supported by the search engines.

Ontology method has become an important method in computer science, and it has been used in many domains, such as semantic web, search engine and knowledge management. Ontology plays an important role in semantic retrieval because of its specification, its good concept hierarchy and its support for logical reasoning. Domain ontology can be used to extend the user’s retrieval words and index domain knowledge. Under which we can achieve semantic retrieval for domain knowledge. The semantic extension of user’s retrieval words is the more important step in semantic retrieval system. First, this paper designed the model of semantic query expansion based on domain ontology and gave five kinds of semantic expansion methods. Then the model of semantic query expansion is applied in semantic retrieval system. The designed system consists of four main components: ontologies building, retrieval words semantic extension, semantic precomputation for document and semantic retrieval. Finally, this paper makes an experiment on high-speed railway domain. The experimental results show that the developed semantic retrieval system can improve the recall and precision.

The last two decades have witnessed an ever increasing penetration of digital media initially in the management and, subsequently, in the study of culture. From collections management, object documentation, domain knowledge representation and reasoning, to supporting the creative synthesis and re-interpretation of data in the framework of digital productions, significant progress has been achieved in the development of relevant knowledge and software tools. Developing a standard ontology for the cultural domain stands out as the most prominent such development.As a consequence of this progress, digital repositories are created that aim at serving as digital cultural memories, while a process of convergence has started among the different kinds of memory institutions, i.e., museums, archives, and libraries, in what concerns their information functions. The success of digital cultural memories will be decided against rivals with centuries-long tradition. The advantages offered by technology, mass storage, copying, and the ease of searching and quantitative analysis, will not suffice unless reliability, long-term preservation, and the ability to re-use, re-combine and re-interpret digital content are ensured. To this end digital curation is exercised.In this talk we will examine the development of digital cultural memories using digital curation. More specifically, we will discuss issues of knowledge representation and reasoning, we will present some examples of interesting research and development efforts, and will refer to certain current trends.

Domain ontology is proposed as an instrument for controlling of e-learning course results. This approach instead of traditional testing more objectively reflects the structure of studentsâ?? knowledge. Use of ontologies for knowledge representation provides interoperability of the used testing systems. The domain knowledge has been found and reuse in the semantic web world. Domain ontology developing is based on the principles of ontological analysis. The method of itâ??s use for evaluation of the learnersâ?? knowledge results is based on matching of the learnersâ?? ontology with the reference ontology proposed by the trainer/tutor. A multi-agent system for e-learning (M(e)L ) has been defined and a prototype system has been developed. In a case, the domain ontology and the semantic web representation has been applied in two e-learning university courses.

This paper reports a domain ontology-driven approach to data mining on a medical database containing clinical data on patients undergoing treatment for chronic kidney disease. Each record within the dataset is comprised of a large number (up to 96) of quantitative and qualitative metrics which represent the physiological state of a particular patient on a particular day of treatment. One of the challenges of mining such a dataset is that the meaning of many of the metrics/attributes is not easily understood by someone who is not familiar with the domain of kidney disease and treatment, and it is not clear which of the attributes are useful in data mining.This paper explores the possibility of utilizing a medical domain ontology as a source of domain knowledge to aid in both extracting knowledge and expressing the extracted knowledge in a useful format. We describe an approach in which the domain ontology is used to categorize attributes in preparation for mining 'association rules' in the data; the mined rules were then reviewed by comparison to domain knowledge derived from a domain expert in order to gauge their 'usefulness'. We conclude that domain ontology driven data mining can obtain more meaningful results than naive mining.

A methodology for content filtering based on ontology similarity is presented, which uses Domain Ontology to describe the characteristic of knowledge of a specific domain. By analyzing the context of the Word of Concern (WoC), the justifiability and accuracy of filter is improved. Firstly, a content ontology based on Domain Ontology is defined, including WoCs and their context constrain rules. Secondly, the concepts of WoC similarity and context similarity are given and the definition of similarity fuzzy set is proposed. Finally, a content filtering mechanism based on ontology similarity is designed to prove the feasibility of the methodology.

The Defense Threat Reduction Agency (DTRA) is the Department of Defense’s (DOD) official Combat Support Agency for countering weapons of mass destruction (WMD). DTRA focuses on WMD and mitigating the consequences of a chemical, biological, radiological, nuclear and high yield explosive threat (CBRNE). The initial direct effects of a CBRNE incident are well defined and documented; however, the second and third order effect’s are complex and not thoroughly understood or documented. Consequence analysis is the practice of analyzing the effects of major events such as a CBRNE event and can assist in predicting the second and third order effects. Currently there is no method to predict or analyze the second and third order effects of CBRNE events. This research focused on identifying the entities associated with a CBRNE event initially. The use of experts and surveys developed an exhaustive list of entities and associated realtionships. The follow-on research focused on the type and strength of the entity relationships. Next, Fuzzy Cognitive Mapping (FCM) techniques identify and evaluate the complex relationships of the second and third order effects. Using a mind mapping computer program, FCM techniques produced second and third order effect relationships. The final product provided a solid first attempt at analyzing a CBRNE event and the associated second and third order effects. Subsequent research will require greater effort to employ system dynamics techniques to enhance the product and develop a more thorough model.

The development of research related to the use of domain ontology in requirement elicitation in recent years has enabled improvement on process effectiveness and efficiency. The implementation of domain ontology has enhanced requirement elicitation process in many ways, among others: in defining requirements specification and constructing test cases. However, building a domain ontology requires great effort by considering the quantity and type of input data. Our study proposes a methodology in building domain ontology by utilizing Business Process Modeling Notation (BPMN), a popular semi-formal modeling language. Ontology classes are built by finding potential term from BPMN components and defining semantic relation among the components. This study produces two outputs: the proposed methodology in domain ontology development with BPMN as data input and the domain ontology as the implementation of the proposed methodology implementation in university’s academic process as a case study.

Improving device access efficiency using a device protocol matching model

The objective of this paper is to apply cognitive map-related techniques to extract causal knowledge from a specific problem domain. This paper proposes to draw an average cognitive map in order to identify the failure factors of the Tunisian small investors. Our paper extends traditional and behavioral finance and previous research by proposing a new approach to building an average cognitive map for the explanation of small investors' failure in the stock market.

This descriptive study discusses cognitive mapping as a technique for analyzing ethical sensitivity, examines whether the method allows comparisons between people, compares the ethical sensitivity levels of participants from three organizations, examines which indicators of ethical sensitivity are most salient to members of specific organizations, and examines whether education level or organizational membership is the best predictor of an individual’s ethical sensitivity level. Subjects from three organizations read background information, listened to two audiotaped scenarios containing multiple ethical issues related to organizational communication, responded to focused interview questions, and completed questionnaires. The interviews were taped, transcribed, and analyzed using cognitive mapping techniques. Significant differences in levels of ethical sensitivity were found between the members of the three organizations. However, a hierarchical regression model demonstrated that the difference was likely due to the differences in level of education in each organization. Organizational membership seemed to affect the particular aspects of the scenarios that participants noticed.

Ontology is a useful method that can share a common understanding of the structure of information, make domain assumption explicit and analyze the domain knowledge. Representation of territorial knowledge based on the ontology is an approach which explains the nature and reasoning of this knowledge for sustainability. This paper proposes a descriptive ontology for territorial knowledge (DOTK) based on foundation ontology. DOTK ontology is an ontology of domain and its aim is enhancing the sustainable knowledge of actors within industries according to territorial knowledge. Extracting the concepts of the domain is from scientific study. This ontology is a guide for contracting of operational ontology and semantic graph demonstrate the rules between concepts of territorial knowledge. This work is ongoing research for applying of this ontology in the special domain. This study will be considered which part of DOTK and how can help to actors of hierarchical level within industries.

PurposeWeb applications are subject to continuous changes and rapid evolution triggered by increasing competition, especially in commercial domains such as electronic commerce. Unfortunately, usually they are implemented without producing any useful documentation for subsequent maintenance and evolution. Thereof, the maintenance of such systems becomes a challenging problem as the complexity of the web application grows. Reverse engineering has been heralded as one of the most promising technologies to support effective web application maintenance. This paper aims to present a reverse engineering approach that helps understanding existing undocumented web applications to be maintained or evolved.Design/methodology/approachThe proposed approach provides reverse engineering rules to generate a conceptual schema from a given domain ontology by using a set of transformation rules. The reverse engineering process consists of four phases: extracting useful information; identifying a set of ontological constructs representing the concepts of interest; enriching the identified set by additional constructs; and finally deriving a conceptual schema.FindingsThe advantage of using ontology for conceptual data modeling is the reusability of domain knowledge. As a result, the conceptual data model will be made faster, easier and with fewer errors than creating it in usual way. Designers can use the extracted conceptual schema to gain a better understanding of web applications and to assist in their maintenance.Originality/valueThe strong point of this approach is that it relies on a very rich semantic reference that is domain ontology. However, it is not possible to make a straightforward transformation of all elements from a domain ontology into a conceptual data model because ontology is semantically richer than data conceptual models.