Solving problems of data heterogeneity, semantic heterogeneity and data inequality : an approach using ontologies

Knowledge is people’s personal map and people’s personal model of the world. Knowledge acquisition involves complex cognitive processes such as perception, communication, and reasoning. According to the knowledge differences, then it is possible for people have a different perception to attain awareness or understand the environment or reality. This paper provides a case study where there is a group of people in different communities managing data using different perceptions, different concepts, different terms (terminologies), and different semantics to represent the same reality. Perceptions are converted into data, and then saved into separate storage devices that are not connected to each other. Each user – belonging to different communities - use different terminologies in collecting data and as a consequence they also get different results of that exercise. It is not a problem if the different results are used for each community, the problem occur if people need to take data from another communities, sharing, collaborating and using it to get a bigger solution. In this paper we present an approach to generate a common set of terms based on the terms of several and different storage devices, used by different communities, in order to make data retrieval independent of the different perceptions and terminologies used by those communities. We use ontologies to represent the knowledge and discuss the use of mapping and integration techniques to find correspondences between the concepts used in those ontologies. We discuss too how to derive a common ontology to be used by all the communities. We can find in literature several documents about the theoretical concepts and techniques that can be used to solve the described problem. However, in this paper we are presenting a real implementation of a system using those concepts

An Extended Semantic Interoperability Model for Distributed Electronic Health Record Based on Fuzzy Ontology Semantics

Semantic interoperability of distributed electronic health record (EHR) systems is a crucial problem for querying EHR and machine learning projects. The main contribution of this paper is to propose and implement a fuzzy ontology-based semantic interoperability framework for distributed EHR systems. First, a separate standard ontology is created for each input source. Second, a unified ontology is created that merges the previously created ontologies. However, this crisp ontology is not able to answer vague or uncertain queries. We thirdly extend the integrated crisp ontology into a fuzzy ontology by using a standard methodology and fuzzy logic to handle this limitation. The used dataset includes identified data of 100 patients. The resulting fuzzy ontology includes 27 class, 58 properties, 43 fuzzy data types, 451 instances, 8376 axioms, 5232 logical axioms, 1216 declarative axioms, 113 annotation axioms, and 3204 data property assertions. The resulting ontology is tested using real data from the MIMIC-III intensive care unit dataset and real archetypes from openEHR. This fuzzy ontology-based system helps physicians accurately query any required data about patients from distributed locations using near-natural language queries. Domain specialists validated the accuracy and correctness of the obtained resultsThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2021R1A2B5B02002599)S

Conceptual design of sound, custom composition languages

Service composition, web mashups, and business process modeling are based on the composition and reuse of existing functionalities, user interfaces, or tasks. Composition tools typically come with their own, purposely built composition languages, based on composition techniques like data flow or control flow, and only with minor distinguishing features-besides the different syntax. Yet, all these composition languages are developed from scratch, without reference specifications (e.g., XML schemas), and by reasoning in terms of low-level language constructs. That is, there is neither reuse nor design support in the development of custom composition languages. We propose a conceptual design technique for the construction of custom composition languages that is based on a generic composition reference model and that fosters reuse. The approach is based on the abstraction of common composition techniques into high-level language features, a set of reference specifications for each feature, and the assembling of features into custom languages by guaranteeing their soundness. We specifically focus on mashup languages

Validation Framework for RDF-based Constraint Languages

In this thesis, a validation framework is introduced that enables to consistently execute RDF-based constraint languages on RDF data and to formulate constraints of any type. The framework reduces the representation of constraints to the absolute minimum, is based on formal logics, consists of a small lightweight vocabulary, and ensures consistency regarding validation results and enables constraint transformations for each constraint type across RDF-based constraint languages

Semantic Integration of Cervical Cancer Data Repositories to Facilitate Multicenter Association Studies: The ASSIST Approach

The current work addresses the unifi cation of Electronic Health Records related to cervical cancer into a single medical knowledge source, in the context of the EU-funded ASSIST research project. The project aims to facilitate the research for cervical precancer and cancer through a system that virtually unifi es multiple patient record repositories, physically located in different medical centers/hospitals, thus, increasing fl exibility by allowing the formation of study groups “on demand” and by recycling patient records in new studies. To this end, ASSIST uses semantic technologies to translate all medical entities (such as patient examination results, history, habits, genetic profi le) and represent them in a common form, encoded in the ASSIST Cervical Cancer Ontology. The current paper presents the knowledge elicitation approach followed, towards the defi nition and representation of the disease’s medical concepts and rules that constitute the basis for the ASSIST Cervical Cancer Ontology. The proposed approach constitutes a paradigm for semantic integration of heterogeneous clinical data that may be applicable to other biomedical application domains

An ontology-based approach to security pattern selection

Usually, the security requirements are addressed by abstracting the security problems arising in a specific context and providing a well proven solution to them. Security patterns incorporating proven security expertise solution to the recurring security problems have been widely accepted by the community of security engineering. The fundamental challenge for using security patterns to satisfy security requirements is the lack of defined syntax, which makes it impossible to ask meaningful questions and get semantically meaningful answers. Therefore, this paper presents an ontological approach to facilitating security knowledge mapping from security requirements to their corresponding solutions-security patterns. Ontologies have been developed usingWeb Ontology Language (OWL) and then incorporated into a security pattern search engine which enables sophisticated search and retrieval of security patterns using the proposed algorithm. Applying the introduced approach allows security novices to reuse security expertise to develop secure software system

Knowledge representation and ontologies for lipids and lipidomics

Master'sMASTER OF SCIENC