9 research outputs found

    Institutionalising Ontology-Based Semantic Integration

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    We address what is still a scarcity of general mathematical foundations for ontology-based semantic integration underlying current knowledge engineering methodologies in decentralised and distributed environments. After recalling the first-order ontology-based approach to semantic integration and a formalisation of ontological commitment, we propose a general theory that uses a syntax-and interpretation-independent formulation of language, ontology, and ontological commitment in terms of institutions. We claim that our formalisation generalises the intuitive notion of ontology-based semantic integration while retaining its basic insight, and we apply it for eliciting and hence comparing various increasingly complex notions of semantic integration and ontological commitment based on differing understandings of semantics

    Achieving Business Process Model Interoperability Using Metamodels and Ontologies

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    A messaging system to handle semantic dissonance

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    Enterprises have been compelled to share their data internally and externally, but creating a consistent view of enterprise data has been challenging. Within a typical enterprise, each division uses its own domain specific data model and schema, and different enterprises obviously use their own data models and schema. Integrating these diverse data models and schemas, which have both syntactic and semantic differences, tends to be complex, slow, and inaccurate. Syntactic differences, i.e., differences in names or layout, have received substantial attention in research. Semantic dissonance simply means that the structure may be similar (or even the same) but the meaning associated with the attributes that define each structure are different, has received less attention in the world of practical software development. A practical messaging system for handling semantic dissonance has been developed. The system utilizes the Resource Description Framework (RDF) and SOAP XML Messaging Specification. It is implemented using Jena, a Java API for RDF, and the Apache SOAP, an Open Source SOAP server and client. This report describes the messaging system, its implementation, its strengths and limitations in handling semantic dissonance

    Medical Informatics

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    Information technology has been revolutionizing the everyday life of the common man, while medical science has been making rapid strides in understanding disease mechanisms, developing diagnostic techniques and effecting successful treatment regimen, even for those cases which would have been classified as a poor prognosis a decade earlier. The confluence of information technology and biomedicine has brought into its ambit additional dimensions of computerized databases for patient conditions, revolutionizing the way health care and patient information is recorded, processed, interpreted and utilized for improving the quality of life. This book consists of seven chapters dealing with the three primary issues of medical information acquisition from a patient's and health care professional's perspective, translational approaches from a researcher's point of view, and finally the application potential as required by the clinicians/physician. The book covers modern issues in Information Technology, Bioinformatics Methods and Clinical Applications. The chapters describe the basic process of acquisition of information in a health system, recent technological developments in biomedicine and the realistic evaluation of medical informatics

    Attribute lattice: a graph-based conceptual modeling grammar for heterogeneous data

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    One key characteristic of big data is variety. With massive and growing amounts of data existing in independent and heterogeneous (structured and unstructured) sources, assigning consistent and interoperable data semantics, which is essential for meaningful use of data, is an increasingly important challenge. I argue, conceptual models, in contrast to their traditional roles in the Information System development, can be used to represent data semantics as perceived by the user of data. In this thesis, I use principles from philosophical ontology, human cognition (i.e., classification theory), and graph theory to offer a theory-based conceptual modeling grammar for this purpose. This grammar reflects data from users of data perspective and independent from data source schema. I formally define the concept of attribute lattice as a graph-based, schema-free conceptual modeling grammar that represents attributes of instances in the domain of interest and precedence relations among them. Each node in an attribute lattice represents an attribute - a true statement (predicate) about some instances in the domain. Each directed arc represents a precedence relation indicating that possessing one attribute implies possessing another attribute. In this thesis, based on the premise that inherent classification is a barrier that hinders semantic interoperation of heterogeneous data sources, a human cognition based conceptual modeling grammar is introduced as an effective way to resolve semantic heterogeneity. This grammar represents the precedence relationship among attributes as perceived by human user and provides a mechanism to infer classes based on the pattern of precedences. Hence, a key contribution of attribute lattice is semantic relativism – that is, the classification in this grammar relies on the pattern of precedence relationship among attributes rather than fixed classes. This modeling grammar uses the immediate and semantic neighbourhoods of an attribute to designate an attribute as either a category, a class or a property and to specify the expansion of an attribute – attributes which are semantically equal to the given attribute. The introduced conceptual modeling grammar is implemented as an artifact to store and manage attribute lattices, to graphically represent them, and integrate lattices from various heterogeneous sources. With the ever-increasing amount of unstructured data (mostly text data) from various data sources such as social media, integrating text data with other data sources has gained considerable attention. This massive amount of data, however, makes finding the data relevant to a topic of interest a new challenge. I argue that the attribute lattice provides a robust semantic foundation to address this information retrieval challenge from unstructured data sources. Hence, a topic modeling approach based on the attribute lattice is proposed for Twitter. This topic model conceptualizes topic structure of tweets related to the domain of interest and enhances information retrieval by improving the semantic interpretability of hashtags

    An evaluation of the challenges of Multilingualism in Data Warehouse development

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    In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen

    Introduction to the special issue on semantic integration

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