Next generation assisting clinical applications by using semantic-aware electronic health records

The health care sector is no longer imaginable without electronic health records. However; since the original idea of electronic health records was focused on data storage and not on data processing, a lot of current implementations do not take full advantage of the opportunities provided by computerization. This paper introduces the Patient Summary Ontology for the representation of electronic health records and demonstrates the possibility to create next generation assisting clinical applications based on these semantic-aware electronic health records. Also, an architecture to interoperate with electronic health records formatted using other standards is presented

Open world reasoning in semantics-aware access control: A preliminary study

We address the relationships between theoretical foundations of Description Logics and practical applications of security-oriented Semantic Web techniques. We first describe the advantages of semantics-aware Access Control and review the state of the art; we also introduce the basics of Description Logics and the novel semantics they share. Then we translate the principle underlying the Little House Problem of DL into a real-world use case: by applying Open World Reasoning to the Knowledge Base modelling a Virtual Organization, we derive information not achievable with traditional Access Control methodologies. With this example, we also show that a general problem such as ontology mapping can take advantage of the enhanced semantics underlying OWL Lite and OWL DL to handle under-specified concepts

ICT INDUSTRY INTEGRATED CURRICULA: TOWARDS AN ONTOLOGY BASED COMPETENCY MODEL

As technology advances rapidly, the ever changing industry needs for skills and competencies keeps changing in efforts to seize the nearest competitive advantage. This creates a great burden on higher education institutions to accurately be able to supply what the industry currently demands. Understanding and analyzing the gap between the supplied and demanded competencies has been always a topic of debate and research between both domains of knowledge. In this thesis, we have proposed developing an ontology that would help in identifying the gap between the employee and occupation competencies. The objective is to be able to generate the gap analysis utilizing the ontology and provide users with information that would help them in gaining more knowledge about the domain and taking informative decisions based on facts. Two separate ontologies representing classes and object properties of the Education and the Industry domain were successfully modeled. The validation shows that the ontology correctly classifies the employees as Fit or Un-fit to the set of occupations they applied for according to the competency gap analysis. Future work will involve experts validating the results of the ontology from the domain of knowledge point of view.QNRF project ProSkima NPRP 7-1883-5-28

An Integrated Approach for Ontology-Driven Configuration Management and Run-Time Execution of Manufacturing Systems

The contemporary manufacturing systems must respond instantly to rapidly changing customer and market requirements in order to survive the intensive competitive environment. The factories should have the agility to adapt to mass customization and introduction of new product, equipment and technology to manufacturing. This is possible by having re-configurable loosely coupled system which offers run-time decision making capability on all levels of factory from shop floor to ERP systems. This thesis proposes a knowledge-based approach for achieving shop-floor device configuration management, run-time execution support for orchestration engines and re-configurable visualization for monitoring systems. The thesis work was carried out as a part of the European project eScop by Arthemis Joint Undertaking, where knowledge bases as the information source for manufacturing execution system is the core concept. This thesis work involves semantic modeling of the manufacturing knowledge in a Manufacturing System Ontology (MSO) and exposing the knowledge to other components of the system using web services. It employs Service Oriented Architecture (SOA) on device level to facilitate knowledge extraction. A methodology is put forward in the thesis to design ontology with broader capabilities and queries for reasoning the ontology. Ontologies are extendable and easy to update offering flexibility to address system changes. The reusability of knowledge simplifies the addition of new product or equipment and thereby offering re-configurability to the system. The proposed approach has been tested by implementing on a real manufacturing system and the research objectives were achieved

Semantic Model Alignment for Business Process Integration

Business process models describe an enterprise’s way of conducting business and in this form the basis for shaping the organization and engineering the appropriate supporting or even enabling IT. Thereby, a major task in working with models is their analysis and comparison for the purpose of aligning them. As models can differ semantically not only concerning the modeling languages used, but even more so in the way in which the natural language for labeling the model elements has been applied, the correct identification of the intended meaning of a legacy model is a non-trivial task that thus far has only been solved by humans. In particular at the time of reorganizations, the set-up of B2B-collaborations or mergers and acquisitions the semantic analysis of models of different origin that need to be consolidated is a manual effort that is not only tedious and error-prone but also time consuming and costly and often even repetitive. For facilitating automation of this task by means of IT, in this thesis the new method of Semantic Model Alignment is presented. Its application enables to extract and formalize the semantics of models for relating them based on the modeling language used and determining similarities based on the natural language used in model element labels. The resulting alignment supports model-based semantic business process integration. The research conducted is based on a design-science oriented approach and the method developed has been created together with all its enabling artifacts. These results have been published as the research progressed and are presented here in this thesis based on a selection of peer reviewed publications comprehensively describing the various aspects

Investigating the role of knowledge management in driving the development of an effective business process architecture

Business Process Architecture (BPA) modelling methods are not dynamic and flexible enough to effectively respond to changes. This may create a barrier that contributes to a lack of knowledge and learning capabilities which can affect the BPA regarding its support for a sustainable competitive advantage in an organisation. New business challenges are driving business enterprises to adopt Knowledge Management (KM) as one means of making a positive difference to their performance and competitiveness. However, shortcomings still remain in utilising knowledge management in business processes where efforts were mostly directed towards the integration of knowledge management with business process management but not including BPAs. The idea of applying KM as a memory to be timely retrieved and updated as needed is no longer sufficient. The resource-based view suggests a number of key factors to be investigated and taken into consideration during the development of knowledge management systems. These key factors are known as Knowledge Management Enablers (KMEs). KMEs are crucial for representing KM and understanding how knowledge is created, shared and disseminated. They are also essential to identify available assets and resources, and to clarify how organisational capabilities are created and utilised.This research is aimed at investigating the role of the knowledge management enablers in the development of an effective process architecture. An effective process architecture needs to be dynamic and supports a sustainable competitive advantage in an organisation. Identifying the KMEs, selecting an appropriate BPA method, aligning these KMEs with this method as well as undertaking a critical evaluation of this alignment are the main objectives set for this research. In order to accomplish the research aim and objectives, a resource-based and semantic-enriched framework, namely the KMEOntoBPA has been designed using KMEs to drive the process of BPA development. Organisational structure, culture, information technology, leadership, knowledge context and business repository have been selected as representatives of the KMEs. The object-based BPA modelling, specifically the semantically enriched Riva BPA (srBPA) method, has been adopted in order to embrace the knowledge resources generated by KMEs and utilise them in the derivation and re-configuration of its constitutional elements. These knowledge resources are employed as business objects. They are considered as Candidate Essential Business Entities (CEBEs) in the Riva method, that characterise or represent a form of business of an organisation. The Design Science Research Methodology (DSRM) is used to guide the research phases with an emphasis on the design and development, demonstration and evaluation of the research framework. The KMEOntoBPA has been demonstrated using sufficient and representative core banking case studies of the Treasury, Deposits and Financing. These case studies have been applied to the DSRM iterations beginning with the Treasury as the 1st case study, followed by the Deposits and the Financing case studies.The results have revealed that KMEs utilisation provides an agile generation of representative CEBEs and their corresponding Riva BPA elements, which reflect the real business in each of the core banking business studies. This research also demonstrated the semantic Riva BPA method as an appropriate object-based method that is well aligned with KMEs in exploiting knowledge resources for the development of a dynamic BPA with reference to robustness and learning capabilities. In addition to these results, the research framework, i.e, the KMEOntoBPA has shown an understanding of the flow of knowledge in the bank and has provided several possible advantages such as the accuracy of service delivery and the improvement of the financial control. It also supports the sources of sustainable competitive advantage (SCA): technical capabilities, core competences and social capital.Finally, a number of significant contributions and artefacts have been attained. For example, there is the aKMEOnt which is the abstract ontology that utilises six KMEs in this research to investigate the effectiveness of using such KMEs in driving the development of the BPA. These contributions along with the research results provide a guide to future research directions such as using the aKMEOnt in the development of different business process modelling and deriving the Enterprise Information Architecture (EIA) and Service Oriented Architecture (SOA)

Ontology transformation into taxonomic structure algorithm for evidential reasoning

Modeliranje podataka predstavlja proces stvaranja modela podataka kojim se simboliˇcki opisuju hijerarhijski odnosi izmed¯u stvari i dogad¯aja u nekom sustavu ili procesu. Ontologije kao napredniji naˇcin modeliranja podataka na raspolaganju imaju ve´ci broj jeziˇcnih konstrukata u odnosu na uobiˇcajene naˇcine modeliranja. Uz navedeno, ontologije na raspolaganju imaju i zaklju ˇcivaˇc koji na temelju znaˇcenja daje logiˇcke zakljuˇcke koji utjeˇcu na hijerarhijsku strukturu i raspored objekata (instanci) klasa. S druge strane, evidencijsko zakljuˇcivanje predstavlja najnoviju metodu višekriterijskog odluˇcivanja temeljenu na Dempster-Shafer teoriji koja se odnosi na donošenje odred¯enih odluka s prisutnim, a cˇesto i konfliktnim, kriterijima. Kako bi se evidencijsko zakljucˇivanje moglo primijeniti nad OWL ontologijama potrebno je napraviti odred¯ene prilagodbe s obzirom da se OWL ontologija strukturno razlikuje od zahtjeva za primjenu evidencijskog zakljuˇcivanja. U ovoj disertaciji predstavljen je model prilagodbe najˇceš´ce korištene OWL ontologije u taksonomsku strukturu temeljen na HermiT zakljuˇcivaˇcu. Predloženi model dijeli se na tri glavna dijela. Prvi dio odnosi se na primjenu HermiT zakljuˇcivaˇca nad ulaznom ontologijom, a kao rezultat dobija se hijerarhijska struktura koja predstavlja ontologiju pri ˇcemu se u obzir uzimaju svi korišteni aksiomi OWL ontologije. Drugi dio odnosi se na primjenu algoritma za prilagodbu ontologije u taksonomsku strukturu gdje dobivena taksonomija zadovoljava pravila op´ceg stabla, odnosno, nad takvom taksonomijom mogu´ce je primjeniti evidencijsko zakljuˇcivanje. Tre´ci dio odnosi se na pripremu za proces primjene evidencijskog zakljuˇcivanja. Najbitniji dio modela prilagodbe je predloženi algoritam prilagodbe koji na temelju skupa pravila i podpravila rješava specifiˇcne situacije koje se mogu na´ci u ontološkoj strukturi u svrhu dobivanja taksonomske strukture. Kako bi se provjerila ispravnost prilagod¯ene strukture, predložena je metoda evaluacije koja se temelji na provjeri definirana tri svojstva, a to su svojstvo klasa, svojstvo veza i svojstvo povezanosti. Primjena predloženog modela prilagodbe, algoritma i metode evaluacije prikazana je na skupu ulaznih ontologija iz dostupnih repozitorija. Dobiveni rezultati prikazuju vremena izvršavanja zasebno za HermiT zakljuˇcivaˇc i predloženi algoritam prilagodbe, te vrijednosti definirana tri svojstva evaluacije prilagodbe. Iz rezultata je vidljivo kako bi se takav naˇcin prilagodbe mogao iskoristiti na ve´c postoje´cim modelima prikazanih u OWL ontologiji, a koji opisuju neki objekt ocjenjivanja u svrhu primjene evidencijskog zakljuˇcivanjaData modelling represents the process of creating data model which symbolically describes hierarchical relationships between things and events within a system or process. Ontologies as more advanced data modelling approach have greater number of available language constructs compared to usual data modelling methods. Also, ontologies use reasoner which generates logical conclusions based on meaning that affect hierarchical structure and instance membership. On the other hand, evidential reasoning represents the latest multiple criteria decision method based on Dempster-Shafer theory related to the decision making process with present, but often conflicted, criteria. In order to apply evidential reasoning over OWL ontologies it is necessary to apply some adjustments considering that OWL ontology is structurally different from demands that impose evidential reasoning. This dissertation presents adjustment model for commonly used OWL ontology into taxonomic structure based on HermiT reasoner. The proposed model is divided into three main parts. The first part is related to application of HermiT reasoner over input ontology resulting in hierarchical structure that represents original ontology regarding all used OWL ontology axioms. The second part is related to the application of ontology adjustment algorithm into taxonomic structure where resulting taxonomy satisfies general tree rules, i.e. evidential reasoning can be applied over such taxonomy. The third part is related to preparation for evidential reasoning application. The most significant part of the adjustment model is proposed adjustment algorithm that addresses specific situations existing in ontology structure based on set of rules and sub rules in order to get taxonomic structure. The evaluation method based on satisfying of three predefined properties (class property, relation property and connectivity property) in order to verify the adjusted structure correctness is proposed. The application of proposed adjustment model, adjustment algorithm and evaluation method is performed over the input set of ontologies from available online repositories. The results show executing times separately for HermiT reasoner and proposed adjustment algorithm and also the values of three predefined evaluation properties. The results show that proposed adjustment method can be used over existing data models represented in OWL ontology that describe some object of evaluation in order to apply evidential reasoning

Ontology transformation into taxonomic structure algorithm for evidential reasoning

Modeliranje podataka predstavlja proces stvaranja modela podataka kojim se simboliˇcki opisuju hijerarhijski odnosi izmed¯u stvari i dogad¯aja u nekom sustavu ili procesu. Ontologije kao napredniji naˇcin modeliranja podataka na raspolaganju imaju ve´ci broj jeziˇcnih konstrukata u odnosu na uobiˇcajene naˇcine modeliranja. Uz navedeno, ontologije na raspolaganju imaju i zaklju ˇcivaˇc koji na temelju znaˇcenja daje logiˇcke zakljuˇcke koji utjeˇcu na hijerarhijsku strukturu i raspored objekata (instanci) klasa. S druge strane, evidencijsko zakljuˇcivanje predstavlja najnoviju metodu višekriterijskog odluˇcivanja temeljenu na Dempster-Shafer teoriji koja se odnosi na donošenje odred¯enih odluka s prisutnim, a cˇesto i konfliktnim, kriterijima. Kako bi se evidencijsko zakljucˇivanje moglo primijeniti nad OWL ontologijama potrebno je napraviti odred¯ene prilagodbe s obzirom da se OWL ontologija strukturno razlikuje od zahtjeva za primjenu evidencijskog zakljuˇcivanja. U ovoj disertaciji predstavljen je model prilagodbe najˇceš´ce korištene OWL ontologije u taksonomsku strukturu temeljen na HermiT zakljuˇcivaˇcu. Predloženi model dijeli se na tri glavna dijela. Prvi dio odnosi se na primjenu HermiT zakljuˇcivaˇca nad ulaznom ontologijom, a kao rezultat dobija se hijerarhijska struktura koja predstavlja ontologiju pri ˇcemu se u obzir uzimaju svi korišteni aksiomi OWL ontologije. Drugi dio odnosi se na primjenu algoritma za prilagodbu ontologije u taksonomsku strukturu gdje dobivena taksonomija zadovoljava pravila op´ceg stabla, odnosno, nad takvom taksonomijom mogu´ce je primjeniti evidencijsko zakljuˇcivanje. Tre´ci dio odnosi se na pripremu za proces primjene evidencijskog zakljuˇcivanja. Najbitniji dio modela prilagodbe je predloženi algoritam prilagodbe koji na temelju skupa pravila i podpravila rješava specifiˇcne situacije koje se mogu na´ci u ontološkoj strukturi u svrhu dobivanja taksonomske strukture. Kako bi se provjerila ispravnost prilagod¯ene strukture, predložena je metoda evaluacije koja se temelji na provjeri definirana tri svojstva, a to su svojstvo klasa, svojstvo veza i svojstvo povezanosti. Primjena predloženog modela prilagodbe, algoritma i metode evaluacije prikazana je na skupu ulaznih ontologija iz dostupnih repozitorija. Dobiveni rezultati prikazuju vremena izvršavanja zasebno za HermiT zakljuˇcivaˇc i predloženi algoritam prilagodbe, te vrijednosti definirana tri svojstva evaluacije prilagodbe. Iz rezultata je vidljivo kako bi se takav naˇcin prilagodbe mogao iskoristiti na ve´c postoje´cim modelima prikazanih u OWL ontologiji, a koji opisuju neki objekt ocjenjivanja u svrhu primjene evidencijskog zakljuˇcivanjaData modelling represents the process of creating data model which symbolically describes hierarchical relationships between things and events within a system or process. Ontologies as more advanced data modelling approach have greater number of available language constructs compared to usual data modelling methods. Also, ontologies use reasoner which generates logical conclusions based on meaning that affect hierarchical structure and instance membership. On the other hand, evidential reasoning represents the latest multiple criteria decision method based on Dempster-Shafer theory related to the decision making process with present, but often conflicted, criteria. In order to apply evidential reasoning over OWL ontologies it is necessary to apply some adjustments considering that OWL ontology is structurally different from demands that impose evidential reasoning. This dissertation presents adjustment model for commonly used OWL ontology into taxonomic structure based on HermiT reasoner. The proposed model is divided into three main parts. The first part is related to application of HermiT reasoner over input ontology resulting in hierarchical structure that represents original ontology regarding all used OWL ontology axioms. The second part is related to the application of ontology adjustment algorithm into taxonomic structure where resulting taxonomy satisfies general tree rules, i.e. evidential reasoning can be applied over such taxonomy. The third part is related to preparation for evidential reasoning application. The most significant part of the adjustment model is proposed adjustment algorithm that addresses specific situations existing in ontology structure based on set of rules and sub rules in order to get taxonomic structure. The evaluation method based on satisfying of three predefined properties (class property, relation property and connectivity property) in order to verify the adjusted structure correctness is proposed. The application of proposed adjustment model, adjustment algorithm and evaluation method is performed over the input set of ontologies from available online repositories. The results show executing times separately for HermiT reasoner and proposed adjustment algorithm and also the values of three predefined evaluation properties. The results show that proposed adjustment method can be used over existing data models represented in OWL ontology that describe some object of evaluation in order to apply evidential reasoning