308 research outputs found

    Research on conceptual modeling: Themes, topics, and introduction to the special issue

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    Conceptual modeling continues to evolve as researchers and practitioners reflect on the challenges of modeling and implementing data-intensive problems that appear in business and in science. These challenges of data modeling and representation are well-recognized in contemporary applications of big data, ontologies, and semantics, along with traditional efforts associated with methodologies, tools, and theory development. This introduction contains a review of some current research in conceptual modeling and identifies emerging themes. It also introduces the articles that comprise this special issue of papers from the 32nd International Conference on Conceptual Modeling (ER 2013).This article was supported, in part, by the J. Mack Robinson College of Business at the Georgia State University, the Marriott School of Management at Brigham Young University (EB-201313), and by the GEODAS-BI (TIN2012-37493-C03-03) project from the Spanish Ministry of Education and Competitivity

    Documenting Data Integration Using Knowledge Graphs

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    With the increasing volume of data on the Web and the proliferation of published knowledge graphs, there is a growing need for improved data management and information extraction. However, heterogeneity issues across the data sources, i.e., various formats and systems, negatively impact efficient access, manage, reuse, and analyze the data. A data integration system (DIS) provides uniform access to heterogeneous data sources and their relationships; it offers a unified and comprehensive view of the data. DISs resort to mapping rules, expressed in declarative languages like RML, to align data from various sources to classes and properties defined in an ontology. This work defines a knowledge graph where data integration systems are represented as factual statements. The aim of this work is to provide the basis for integrated analysis of data collected from heterogeneous data silos. The proposed knowledge graph is also specified as a data integration system, that integrates all data integration systems. The proposed solution includes a unified schema, which defines and explains the relationships between all elements in the data integration system DIS=⟨G, S, M, F⟩. The results suggest that factual statements from the proposed knowledge graph, improve the understanding of the features that characterize knowledge graphs declaratively defined like data integration systems

    Semantic derivation of enterprise information architecture from riva-based business process architecture

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    Contemporary Enterprise Information Architecture (EIA) design practice in the industry still suffers from issues that hamper the investment in the EIA design. First and foremost of these issues is the shortcoming of EIA design research to bridge the gap between business and systems (or information) architectures. Secondly, contemporary developed business process architecture methods, and in particular object-based ones have not been fully exploited for EIA design and thus widening the gap between business processes and systems. In practice, knowledge-driven approaches have been thoroughly influencing EIA design. Thirdly, the lack of using knowledge representation methods adversely affected the automation (or semi-automation) of the EIA design process. Software Engineering (SE) technologies and Knowledge Representation using ontologies continue to prove instrumental in the design of domain knowledge. Finally, current EIA development methods have often resulted in complex designs that hampered both adopting and exploiting EIA in medium to large scale organisations.This research is aimed at investigating the derivation of the EIA from a given semantic representation of object-based Business Process Architecture (BPA), and in particular Riva-based BPA using the design science research-based methodology. The key design artefact of this research is the development of the BPAOntoEIA framework that semantically derives EIA from a semantic representation of Riva-based BPA of an enterprise. In this framework, EIA elements were derived from the semantic Riva BPA elements and associated business process models, with forward and backward traceability from/to the derived EIA to/from the original BPA. The BPAOntoEIA framework has been evaluated using the semantic Cancer Care and Registration BPA in Jordan. This framework has been validated using an authentic concern-based evaluation framework employing both static and dynamic validation approaches.The BPAOntoEIA framework contributes to bridging the gap between the business and systems world by providing a business/IT alignment through the EIA derivation process, and using the semantic knowledge of business processes within the resultant EIA. A major novel contribution is the introduction of new evaluation metrics for EIA design, which are quantitative, and are not only indicative of the quality of the semantic EIA derivation from the associated BPA but also the extent of utilisingbusiness process knowledge and traceability amongst EIA elements.Amongst other novel contributions is the semantic EIA derivation process that comprises a suite of the Semantic Web Rules Language (SWRL) rules applied on the semantic BPA elements. The derivation scheme utilises the generic EIA (gEIAOnt) ontology that was developed in this research and represents a semantic meta-model of EIA elements of a generic enterprise. The resultant EIA provides a highly coherent semantic information model that is in-line with the theory of EIA design, semantically enriched, and fully utilises the semantic knowledge of business processes.Benefits of this research to industry include the semantic EIA derivation process and a resultant information model that utilises the semantic information of business processes in the enterprise. Therefore, this enables the enterprise strategic management to plan for a single, secure and accessible information resource that is business process driven, and enabled in an agile environment. The semantic enrichment of the EIA is a starting point for a simplistic design of a domain-independent semantic enterprise architecture for the development of systems of systems in loosely coupled enterprises

    Comparative analysis of knowledge representation and reasoning requirements across a range of life sciences textbooks.

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    BackgroundUsing knowledge representation for biomedical projects is now commonplace. In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student interest and improve learning. A natural question that arises from this success, and this paper's primary focus, is whether a similar approach is applicable across a range of life science textbooks. To answer that question, we considered four different textbooks, ranging from a below-introductory college biology text to an advanced, graduate-level neuroscience textbook. For these textbooks, we investigated the following questions: (1) To what extent is knowledge shared between the different textbooks? (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks? (3) To what extent can the questions of interest for a range of textbooks be answered by using the same reasoning mechanisms?ResultsOur existing modeling and reasoning methods apply especially well both to a textbook that is comparable in level to the text studied in our previous work (i.e., an introductory-level text) and to a textbook at a lower level, suggesting potential for a high degree of portability. Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook. We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important.ConclusionsWith some additional work, our representation methodology would be applicable to a range of textbooks. The requirements for knowledge representation are common across textbooks, suggesting that a shared semantic infrastructure for the life sciences is feasible. Because our representation overlaps heavily with those already being used for biomedical ontologies, this work suggests a natural pathway to include such representations as part of the life sciences curriculum at different grade levels

    Conceptual Modeling Applied to Data Semantics

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    In software system design, one of the purposes of diagrammatic modeling is to explain something (e.g., data tables) to others. Very often, syntax of diagrams is specified while the intended meaning of diagrammatic constructs remains intuitive and approximate. Conceptual modeling has been developed to capture concepts and their interactions with each other in the intended domain and to represent structural and behavioral features of the modeled system. This paper is a venture into diagrammatic approaches to the semantics of modeling notations, with a focus on data and graph semantics. The first decade of the new millennium has seen several new world-changing businesses spring to life (e.g., Google and Twitter), that have put connected data at the center of their trade. Harnessing such data requires significant effort and expertise, and it quickly becomes prohibitively expensive. One solution involves building graph-based data models, which is a challenging problem. In many applications, the utilized software is managing not just objects as well as isolated and discrete data items but also the connections between them. Data semantics is a key ingredient to construct a model that explicitly describes the relationships between data objects. In this paper, we claim that current ad hoc graphs that attempt to provide semantics to data structures (e.g., relational tables and tabular SQL) are problematic. These graphs mix static abstract concepts with dynamic specification of objects (particulars). Such a claim is supported by analysis that applies the thinging machine (TM) model to provide diagrammatic representations of data (e.g., Neo4J graphs). The study s results show that to take advantage of graph algorithms and simultaneously achieve appropriate data semantics, the data graphs should be developed as simplified forms of TM.Comment: 12 pages, 27 figure

    Ontological approach for database integration

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    Database integration is one of the research areas that have gained a lot of attention from researcher. It has the goal of representing the data from different database sources in one unified form. To reach database integration we have to face two obstacles. The first one is the distribution of data, and the second is the heterogeneity. The Web ensures addressing the distribution problem, and for the case of heterogeneity there are many approaches that can be used to solve the database integration problem, such as data warehouse and federated databases. The problem in these two approaches is the lack of semantics. Therefore, our approach exploits the Semantic Web methodology. The hybrid ontology method can be facilitated in solving the database integration problem. In this method two elements are available; the source (database) and the domain ontology, however, the local ontology is missing. In fact, to ensure the success of this method the local ontologies should be produced. Our approach obtains the semantics from the logical model of database to generate local ontology. Then, the validation and the enhancement can be acquired from the semantics obtained from the conceptual model of the database. Now, our approach can be applied in the generation phase and the validation-enrichment phase. In the generation phase in our approach, we utilise the reverse engineering techniques in order to catch the semantics hidden in the SQL language. Then, the approach reproduces the logical model of the database. Finally, our transformation system will be applied to generate an ontology. In our transformation system, all the concepts of classes, relationships and axioms will be generated. Firstly, the process of class creation contains many rules participating together to produce classes. Our unique rules succeeded in solving problems such as fragmentation and hierarchy. Also, our rules eliminate the superfluous classes of multi-valued attribute relation as well as taking care of neglected cases such as: relationships with additional attributes. The final class creation rule is for generic relation cases. The rules of the relationship between concepts are generated with eliminating the relationships between integrated concepts. Finally, there are many rules that consider the relationship and the attributes constraints which should be transformed to axioms in the ontological model. The formal rules of our approach are domain independent; also, it produces a generic ontology that is not restricted to a specific ontology language. The rules consider the gap between the database model and the ontological model. Therefore, some database constructs would not have an equivalent in the ontological model. The second phase consists of the validation and the enrichment processes. The best way to validate the transformation result is to facilitate the semantics obtained from the conceptual model of the database. In the validation phase, the domain expert captures the missing or the superfluous concepts (classes or relationships). In the enrichment phase, the generalisation method can be applied to classes that share common attributes. Also, the concepts of complex or composite attributes can be represented as classes. We implement the transformation system by a tool called SQL2OWL in order to show the correctness and the functionally of our approach. The evaluation of our system showed the success of our proposed approach. The evaluation goes through many techniques. Firstly, a comparative study is held between the results produced by our approach and the similar approaches. The second evaluation technique is the weighting score system which specify the criteria that affect the transformation system. The final evaluation technique is the score scheme. We consider the quality of the transformation system by applying the compliance measure in order to show the strength of our approach compared to the existing approaches. Finally the measures of success that our approach considered are the system scalability and the completeness

    Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research

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    The era of big data provides many opportunities for conducting impactful research from both data-driven and theory-driven perspectives. However, data-driven and theory-driven research have progressed somewhat independently. In this paper, we develop a framework that articulates important differences between these two perspectives and propose a role for information systems research at their intersection. The framework presents a set of pathways that combine the data-driven and theory-driven perspectives. From these pathways, we derive a set of challenges, and show how they can be addressed by research in information systems. By doing so, we identify an important role that information systems research can play in advancing both data-driven and theory-driven research in the era of big data
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