Semantic web domain knowledge representation using software engineering modeling technique

The semantic web offers a great deal of deviation from the way in which the current search engines which are based on the traditional information search theory work. Semantic search is carried out by ontology based intelligent information retrieval. So a good semantic search needs a good ontology. The ontology developers need more familiar notations and tools for a uniform representation of ontologies. UML being a standard modeling language in software engineering, it is better supported in terms of expertise and the tools as compared to the upcoming semantic web languages. This work proposes a representation technique which is based on software engineering standards namely UML for modeling domain knowledge of the Semantic Web. The ontology for Company Domain has been presented using this software engineering modeling technique. The UML tool like Rational Rose tool can be used to provide support for modeling complex ontologies of the given domain

Semantic Web Domain Knowledge Representation Using Software Engineering Modeling Technique

ICSD2007, BangaloreThe semantic web offers a great deal of deviation from the way in which the current search engines which are based on the traditional information search theory work. Semantic search is carried out by ontology based intelligent information retrieval. So a good semantic search needs a good ontology. The ontology developers need more familiar notations and tools for a uniform representation of ontologies. UML being a standard modeling language in software engineering, it is better supported in terms of expertise and the tools as compared to the upcoming semantic web languages. This work proposes a representation technique which is based on software engineering standards namely UML for modeling domain knowledge of the Semantic Web. The ontology for Company Domain has been presented using this software engineering modeling technique. The UML tool like Rational Rose tool can be used to provide support for modeling complex ontologies of the given domain.DRTC,ISI,Bangalore,INDI

Metamodeling of Bayesian networks for decision-support systems development

The knowledge modeling and software modeling phases in Knowledge-Based System development are not integrable, in terms of representation, due to the different languages needed at the steps of the development. This paper focuses on bring closer these languages. By one hand, we define a meta model which contains the key concepts used in the definition of a knowledge model as a Bayesian network. On the other hand, we define an extension of UML using profiles that can bridge the gap in representation and facilitate the seamless incorporation of a knowledge model, as Bayesian network, in the context of a knowledge-based software development

Design of a Multidimensional Model Using Object Oriented Features in UML

A data warehouse is a single repository of data which includes data generated from various operational systems. Conceptual modeling is an important concept in the successful design of a data warehouse. The Unified Modeling Language (UML) has become a standard for object modeling during analysis and design steps of software system development. The paper proposes an object oriented approach to model the process of data warehouse design. The hierarchies of each data element can be explicitly defined, thus highlighting the data granularity. We propose a UML multidimensional model using various data sources based on UML schemas. We present a conceptual-level integration framework on diverse UML data sources on which OLAP operations can be performed. Our integration framework takes into account the benefits of UML (its concepts, relationships and extended features) which is more close to the real world and can model even the complex problems easily and accurately. Two steps are involved in our integration framework. The first one is to convert UML schemas into UML class diagrams. The second is to build a multidimensional model from the UML class diagrams. The white-paper focuses on the transformations used in the second step. We describe how to represent a multidimensional model using a UML star or snowflake diagram with the help of a case study. To the best of our knowledge, we are the first people to represent a UML snowflake diagram that integrates heterogeneous UML data sources

Modeling functional requirements using tacit knowledge: a design science research methodology informed approach

The research in this paper adds to the discussion linked to the challenge of capturing and modeling tacit knowledge throughout software development projects. The issue emerged when modeling functional requirements during a project for a client. However, using the design science research methodology at a particular point in the project helped to create an artifact, a functional requirements modeling technique, that resolved the issue with tacit knowledge. Accordingly, this paper includes research based upon the stages of the design science research methodology to design and test the artifact in an observable situation, empirically grounding the research undertaken. An integral component of the design science research methodology, the knowledge base, assimilated structuration and semiotic theories so that other researchers can test the validity of the artifact created. First, structuration theory helped to identify how tacit knowledge is communicated and can be understood when modeling functional requirements for new software. Second, structuration theory prescribed the application of semiotics which facilitated the development of the artifact. Additionally, following the stages of the design science research methodology and associated tasks allows the research to be reproduced in other software development contexts. As a positive outcome, using the functional requirements modeling technique created, specifically for obtaining tacit knowledge on the software development project, indicates that using such knowledge increases the likelihood of deploying software successfully

Evaluating the layout quality of UML class diagrams using machine learning

UML is the de facto standard notation for graphically representing software. UML diagrams are used in the analysis, construction, and maintenance of software systems. Mostly, UML diagrams capture an abstract view of a (piece of a) software system. A key purpose of UML diagrams is to share knowledge about the system among developers. The quality of the layout of UML diagrams plays a crucial role in their comprehension. In this paper, we present an automated method for evaluating the layout quality of UML class diagrams. We use machine learning based on features extracted from the class diagram images using image processing. Such an automated evaluator has several uses: (1) From an industrial perspective, this tool could be used for automated quality assurance for class diagrams (e.g., as part of a quality monitor integrated into a DevOps toolchain). For example, automated feedback can be generated once a UML diagram is checked in the project repository. (2) In an educational setting, the evaluator can grade the layout aspect of student assignments in courses on software modeling, analysis, and design. (3) In the field of algorithm design for graph layouts, our evaluator can assess the layouts generated by such algorithms. In this way, this evaluator opens up the road for using machine learning to learn good layouting algorithms. Approach.: We use machine learning techniques to build (linear) regression models based on features extracted from the class diagram images using image processing. As ground truth, we use a dataset of 600+ UML Class Diagrams for which experts manually label the quality of the layout. Contributions.: This paper makes the following contributions: (1) We show the feasibility of the automatic evaluation of the layout quality of UML class diagrams. (2) We analyze which features of UML class diagrams are most strongly related to the quality of their layout. (3) We evaluate the performance of our layout evaluator. (4) We offer a dataset of labeled UML class diagrams. In this dataset, we supply for every diagram the following information: (a) a manually established ground truth of the quality of the layout, (b) an automatically established value for the layout-quality of the diagram (produced by our classifier), and (c) the values of key features of the layout of the diagram (obtained by image processing). This dataset can be used for replication of our study and others to build on and improve on this work. Editor\u27s note: Open Science material was validated by the Journal of Systems and Software Open Science Board