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The KBSA will improve software practices by providing machinemediated support to decision makers, formalizing the processes associated with software development and project management, and providing a corporate memory for projects. The KBSA will utilize artificial intelligence, automatic programming, knowledge-based engineering, and software environment technology to achieve the [project] goal[s]. Major Subfields and Communities 1. Domain modeling 2.

Knowledge representation and reasoning 3.

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Model-driven software development 5. Semantic technology ontologies and the semantic web 18 Model-driven software sevelopment 5. Semantic technology ontologies and the semantic web 21 Visual Programming With AMPHION A planetary scientist draws diagrams that are close to their own way of thinking to specify the objects relationships and functions to get the desired results.

The final program largely consists of calls to pre-coded subroutine. Courtesy of NASA. Semantic technology ontologies and the semantic web 29 Semantic technology ontologies and the semantic web 34 UML vs.

Ontology-Driven Software Development

This enables locations from several datasets to be placed on the same map. Figure courtesy of TopQuadrant. User vs. The process for transferring the Business knowledge from the ontology to the programming language is by automatic generation of source code. The power of ODASE is that the model specification, the code generation, and the runtime reasoning use the same formal description. Visual Application Development 55 How Far have we Come? In the Future Application development environments that have all of those characteristics.

All Linked Together 67 All Linked Together In a transactional store 68 Semantic Computing -- Challenges Complex system architectures, nothing integrates with anything. Ontology versioning and evolution.

Ontology-based data stores with full complement of reasoning, do not scale. The Future 1.

Resourceome Tutorial - Ontology driven knowledge management - Part 1/4

Applications are manifest as giant networks of semantic agents. Query languages in MDSD can be used to query models in order to retrieve facts from them. In the next chapter, we will introduce the ontology languages and their underpinnings—description logics.

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  • In Chap. Chapter 3 Ontology Languages and Description Logics. Abstract Ontology-driven software technology is expected to improve MDSD with better facilities for modelling, better understanding of relationships between artefacts and better handling of complexity, via ontology-based knowledge repre- sentation KR techniques and reasoning techniques. In modern information technology, especially in the Semantic Web, an ontology is a model of some aspects of the world, which introduces key vocabulary such as concepts and relations of a target domain and their meanings.

    This chapter introduces the standard ontology language family web ontology language OWL and its underpinnings—description logics. Then in Sect. They play an important role in scalable reasoning services cf. Section 3. DL [11] is a family of KR formalisms that represent the knowledge structure of an application domain, in terms of concepts, roles, individuals, and their relationships. The TBox stands for terminology box, which is a collection of declarations also called axioms describing general properties of concepts and roles.

    For example, the. Zhao et al. The ABox stands for assertional box, which contains assertions of specific individuals in a problem domain.

    Ontology-driven software development

    In DL roles are used as a binary predicates to state a relationship between individuals. In DL languages, atomic concepts and atomic roles are the elementary descrip- tions. Constructors are used to build complex descriptions complex concepts and complex roles. In general, in abstract notation, we use the letters A and B for atomic concepts, the letter R for atomic roles, and the letters C and D for concept descriptions.

    Ontology-Driven Software Development | D&R - Kültür, Sanat ve Eğlence Dünyası

    DLs languages are distinguished by the constructors they are applying. In the following we use a basic description language AL as an example, to briefly introduce the syntax and semantics of DL languages. Readers can refer [11] for more details of DLs and DL languages. The formal semantics of AL is given by its model theory. AL is a very basic description language. Table 3. Because of the semantical equivalences C t D :. In general reasonings detect implicit knowledge in logic-based information systems. Here we briefly explain the basic reasoning tasks in DL. They make up the foundations of the ontology services presented in Sect. For example, to check if concept Professor v Vegetarian is entailed from O. For example, if O entails Vegetarian v?

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    For example, to find all instances of Professor. For example, to find all of the postgraduate students and their supervisors who both like swimming, the query could be:. For each language in the DL family, there is trade-offs between the expressivity of the language and the efficiency of the most relevant reasoning tasks. In this book, we always consider reasoning tasks introduced in Sect. Following common understanding we use computational complexity to evaluate the efficiency of a DL or ontology language in this book.

    Here we explain the terms on computational complexities we used in this book. In general there are two popular types of complexity: the time complexity of a problem equal to the number of steps that it takes to solve an instance of the problem as a function of the size of the input e. The complexity classes introduced in here cover both of them. In computational complexity theory , the complexity class P is the set of decision problems that can be solved by a deterministic Turing machine in polynomial time. The complexity class NP is the set of decision problems that can be solved by a non-deterministic Turing machine in polynomial time.

    For example, the Boolean satisfiability problem and the Hamiltonian path problem are typical NP problems. Formally, a problem that is complete for a class C is said to be C-complete, and the class of all problems complete for C is denoted C-complete. The first complete class to be defined and the most well known is NP-complete.

    LogSpace is the complexity class containing decision problems which can be solved by a deterministic Turing machine using a logarithmic amount of memory space. PSPACE is the class of all problems which can be solved by programs which only need a polynomial in the number of facts in one ontology amount of memory to run. We express the complexity as a function of the size of the ontology.

    Currently it enjoys the W3C Recommendation status. The aim is to extend the expressiveness of OWL specification by introducing new constructs. Although the full language of OWL 2 is very powerful in the sense of expressive- ness, its computational cost is also high.

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    The new features include extra syntactic sugar, additional property and qualified cardinality constructors, extended data-type support, simple metamodelling, and extended annotations []. This ability can be used in defining new data types that can be used in the ontology. Which says that brother of parent is uncle. An overview of OWL 2 features is given in Table 3. All examples from Sects.