Integration of Domain and Social Ontologies in a CMS Based Collaborative Platform

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.

From this chapter, we present the technical part of this book including Chapters 6, 7, 8 and 9. The technical part deals with problems related to specific methods, algorithms, and techniques which are the implementation of the cognition-driven decision process (CDDP) model. This chapter discusses two kinds of domain knowledge used in the CDDP model: ontology and experience. A domain ontology is represented as a class tree and a class graph based on two types of relationships between classes: subsumption and property-share. Experience is elicited and represented as experience maps using the cognitive mapping technique. The domain ontology is used for extracting semantic information of SA. The extracted SA semantics together with experience are then used for query construction. This chapter will focus on domain ontology and experience.

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.

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.

Knowledge formalization for experts’ selection into a collaborative maintenance platform

The aim of transfer learning is to reuse learnt knowledge across different contexts. In the particular case of cross-domain transfer (also known as domain adaptation), reuse happens across different but related knowledge domains. While there have been promising first results in combining learning with symbolic knowledge to improve cross-domain transfer results, the singular ability of ontologies for providing classificatory knowledge has not been fully exploited so far by the machine learning community. We show that ontologies, if properly designed, are able to support transfer learning by improving generalization and discrimination across classes. We propose an architecture based on direct attribute prediction for combining ontologies with a transfer learning framework, as well as an ontology-based solution for cross-domain generalization based on the integration of top-level and domain ontologies. We validate the solution on an experiment over an image classification task, demonstrating the system’s improved classification performance.

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 development and verification of software models that are applicable across multiple domains remains a difficult problem. We propose a novel approach to model-driven software development based on ontologies and Semantic Web technology. Our approach uses three ontologies to define software models: a task ontology, a domain ontology and a top-level ontology. The task ontology serves as the conceptual model for the software, the domain ontology provides domain-specific knowledge and the top-level ontology integrates the task and domain ontologies. Our method allows the verification of these models both for consistency and ontological adequacy. This verification can be performed both at development and runtime. Domain ontologies are replaceable modules, which enables the comparison and application of the models built using our method across multiple domains. We demonstrate the viability of our approach through the design and implementation of a semantic wiki and a social tagging system, and compare it with model-driven software development to illustrate its benefits.

After introducing the new field of Semantic Time Series Processing, we take our findings one step further and investigate ontology mapping and reasoning in semantic time series processing. This paper shows how ontologies for time series from different areas can be mapped together and how additional meta-information can be generated by using reasoning methods. For ontology mapping we use the bridging concept and present a general time series ontology, which allows the integration of new domain ontologies. In reasoning a popular open source reasoner is used to show generation of new time series meta-information. Additionally, we discuss our results together with a description of the validation for all previously introduced methods.

In most cases, over 80% of a system's cost is already committed during the preliminary design review (PDR) of the life cycle (Claxton 1993 and NASA 2017). Overruns often occur because requirements of certain stakeholders, such as those related to maintainability, are not involved in the system design process early enough. Consequently, a collaborative effort during the design stage should involve the requirements of key stakeholders and decision‐makers. In this perspective, domain ontologies and the sharing of needs should facilitate a common agreement. Systems engineers are key stakeholders to guarantee the viability of the solution system. The integration of domain ontologies within a model‐based systems engineering framework will enable the interconnection of the heterogeneous data needed to integrate maintenance issues into collaborative system design. Adding maintenance requirements earlier in the design process will help limit additional costs and delays.

In recent years, ontology has become the de facto standard for knowledge representation on the semantic web. The need to design and develop suitable ontologies that can support the operations and functionality of different types of intelligent systems is compelling. This paper presents the notion of decision support for healthcare practitioners in the treatment of gait-related diseases by using a domain ontology. The objective of the domain ontology is to create a knowledgebase artefact that can be leveraged for decision-making by healthcare workers during the prognosis, diagnosis and treatment of diseases that are associated with human gait impairment and gait abnormality. Gait impairment generally affects a patient’s mobility and ability to walk normally. The impact of gait-related diseases can vary from old age to more severe neurological and physical disorders, which needs to be thoroughly examined and investigated for accurate diagnosis and prognosis. This paper discusses how a domain ontology, that can facilitate decision support to diagnose and treat gait-related diseases, can be developed. At completion, the ontology would be able to support intelligent operations such as semantic processing of data, intelligent reasoning, information search and retrieval, and intelligent recommendations that provide valuable support to healthcare practitioners in decision-making when dealing with gait-related diseases.

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.

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.

Ontologies have been designed for the purpose of representing a domain of knowledge in a way that makes it understandable by machines and humans. Ontologies have been used in many areas in computer sciences such as knowledge engineering, semantic web, and information retrieval. Religious studies is one of the areas were ontologies are in demand. In this paper, we present the design of domain ontology about prophets and messengers in Islam from trustworthy resources: the Quran and Hadith. The ontology can be used to better understanding all life aspects of prophets and messengers in Islam. The classes, relations and properties of this special domain ontology were gathered and integrated to help researchers to identify and explore knowledge about prophets in Islam such as their lives, books and teachings, the nature of the messages they brought to their nations and tribes and much more.