Topic Maps : a bibliometric study

Topic Maps is an international standard (ISO/IEC 13250) to describe and encode knowledge structures and associating them with relevant information resources. This thesis seeks to investigate what has been written about Topic Maps from year 2000 to 2011, as well as finding out the research and publication trend in Topic Maps. This study was based on quantitative methodology, which was bibliometric analysis. The data was collected from Scopus and Web of Knowledge databases. Search keywords used are “topic map”, “topic maps” and “ISO/IEC 13250”. A total of 356 publications (265 conference papers, 91 journal articles) from 2001 to 2011 taken into data analysis. The findings revealed that Topic Maps researchers had a preference to present their findings in conference rather than in journal. The authorship pattern was more towards coauthorship. Most researchers were coauthored locally, as international collaboration was very low. Computer science and library and information science related journals were the favourite publishing venue. Majority of the conferences were computer science and education related. The focus of the topic maps was on data integration and interoperability (2001-2004), information theory (2005 – 2008), knowledge and intelligent based system (2009 – 2011). Also, there were five themes identified, namely content management, repository, ontology, information architecture, retrieval and navigation, and semantic web. The future research areas will possibly be collaborative e-learning system, knowledge visualization system, visualization construction, semantic metadata creation from a relational database, knowledge navigation and retrieval improvement, intelligent topic map, distributed knowledge management based on extended topic maps, knowledge service system, knowledge representation modeling, and multi granularity and multi-level knowledge.Joint Master Degree in Digital Library Learning (DILL

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