Automatic maintenance of category hierarchy

Category hierarchy is an abstraction mechanism for efficiently managing large-scale resources. In an open environment, a category hierarchy will inevitably become inappropriate for managing resources that constantly change with unpredictable pattern. An inappropriate category hierarchy will mislead the management of resources. The increasing dynamicity and scale of online resources increase the requirement of automatically maintaining category hierarchy. Previous studies about category hierarchy mainly focus on either the generation of category hierarchy or the classification of resources under a pre-defined category hierarchy. The automatic maintenance of category hierarchy has been neglected. Making abstraction among categories and measuring the similarity between categories are two basic behaviours to generate a category hierarchy. Humans are good at making abstraction but limited in ability to calculate the similarities between large-scale resources. Computing models are good at calculating the similarities between large-scale resources but limited in ability to make abstraction. To take both advantages of human view and computing ability, this paper proposes a two-phase approach to automatically maintaining category hierarchy within two scales by detecting the internal pattern change of categories. The global phase clusters resources to generate a reference category hierarchy and gets similarity between categories to detect inappropriate categories in the initial category hierarchy. The accuracy of the clustering approaches in generating category hierarchy determines the rationality of the global maintenance. The local phase detects topical changes and then adjusts inappropriate categories with three local operations. The global phase can quickly target inappropriate categories top-down and carry out cross-branch adjustment, which can also accelerate the local-phase adjustments. The local phase detects and adjusts the local-range inappropriate categories that are not adjusted in the global phase. By incorporating the two complementary phase adjustments, the approach can significantly improve the topical cohesion and accuracy of category hierarchy. A new measure is proposed for evaluating category hierarchy considering not only the balance of the hierarchical structure but also the accuracy of classification. Experiments show that the proposed approach is feasible and effective to adjust inappropriate category hierarchy. The proposed approach can be used to maintain the category hierarchy for managing various resources in dynamic application environment. It also provides an approach to specialize the current online category hierarchy to organize resources with more specific categories

Grid-based semantic integration of heterogeneous data resources: Implementation on a HealthGrid

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.The semantic integration of geographically distributed and heterogeneous data resources still remains a key challenge in Grid infrastructures. Today's mainstream Grid technologies hold the promise to meet this challenge in a systematic manner, making data applications more scalable and manageable. The thesis conducts a thorough investigation of the problem, the state of the art, and the related technologies, and proposes an Architecture for Semantic Integration of Data Sources (ASIDS) addressing the semantic heterogeneity issue. It defines a simple mechanism for the interoperability of heterogeneous data sources in order to extract or discover information regardless of their different semantics. The constituent technologies of this architecture include Globus Toolkit (GT4) and OGSA-DAI (Open Grid Service Architecture Data Integration and Access) alongside other web services technologies such as XML (Extensive Markup Language). To show this, the ASIDS architecture was implemented and tested in a realistic setting by building an exemplar application prototype on a HealthGrid (pilot implementation). The study followed an empirical research methodology and was informed by extensive literature surveys and a critical analysis of the relevant technologies and their synergies. The two literature reviews, together with the analysis of the technology background, have provided a good overview of the current Grid and HealthGrid landscape, produced some valuable taxonomies, explored new paths by integrating technologies, and more importantly illuminated the problem and guided the research process towards a promising solution. Yet the primary contribution of this research is an approach that uses contemporary Grid technologies for integrating heterogeneous data resources that have semantically different. data fields (attributes). It has been practically demonstrated (using a prototype HealthGrid) that discovery in semantically integrated distributed data sources can be feasible by using mainstream Grid technologies, which have been shown to have some Significant advantages over non-Grid based approaches

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

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

Exploring a Modelling Method with Semantic Link Network and Resource Space Model

To model the complex reality, it is necessary to develop a powerful semantic model. A rational approach is to integrate a relational view and a multi-dimensional view of reality. The Semantic Link Network (SLN) is a semantic model based on a relational view and the Resource Space Model (RSM) is a multi-dimensional view for managing, sharing and specifying versatile resources with a universal resource observation. The motivation of this research consists of four aspects: (1) verify the roles of Semantic Link Network and the Resource Space Model in effectively managing various types of resources, (2) demonstrate the advantages of the Resource Space Model and Semantic Link Network, (3) uncover the rules through applications, and (4) generalize a methodology for modelling complex reality and managing various resources. The main contribution of this work consists of the following aspects: 1. A new text summarization method is proposed by segmenting a document into clauses based on semantic discourse relations and ranking and extracting the informative clauses according to their relations and roles. The Resource Space Model benefits from using semantic link network, ranking techniques and language characteristics. Compared with other summarization approaches, the proposed approach based on semantic relations achieves a higher recall score. Three implications are obtained from this research. 2. An SLN-based model for recommending research collaboration is proposed by extracting a semantic link network of different types of semantic nodes and different types of semantic links from scientific publications. Experiments on three data sets of scientific publications show that the model achieves a good performance in predicting future collaborators. This research further unveils that different semantic links play different roles in representing texts. 3. A multi-dimensional method for managing software engineering processes is developed. Software engineering processes are mapped into multiple dimensions for supporting analysis, development and maintenance of software systems. It can be used to uniformly classify and manage software methods and models through multiple dimensions so that software systems can be developed with appropriate methods. Interfaces for visualizing Resource Space Model are developed to support the proposed method by keeping the consistency among interface, the structure of model and faceted navigation

Grid-based semantic integration of heterogeneous data resources : implementation on a HealthGrid

The semantic integration of geographically distributed and heterogeneous data resources still remains a key challenge in Grid infrastructures. Today's mainstream Grid technologies hold the promise to meet this challenge in a systematic manner, making data applications more scalable and manageable. The thesis conducts a thorough investigation of the problem, the state of the art, and the related technologies, and proposes an Architecture for Semantic Integration of Data Sources (ASIDS) addressing the semantic heterogeneity issue. It defines a simple mechanism for the interoperability of heterogeneous data sources in order to extract or discover information regardless of their different semantics. The constituent technologies of this architecture include Globus Toolkit (GT4) and OGSA-DAI (Open Grid Service Architecture Data Integration and Access) alongside other web services technologies such as XML (Extensive Markup Language). To show this, the ASIDS architecture was implemented and tested in a realistic setting by building an exemplar application prototype on a HealthGrid (pilot implementation). The study followed an empirical research methodology and was informed by extensive literature surveys and a critical analysis of the relevant technologies and their synergies. The two literature reviews, together with the analysis of the technology background, have provided a good overview of the current Grid and HealthGrid landscape, produced some valuable taxonomies, explored new paths by integrating technologies, and more importantly illuminated the problem and guided the research process towards a promising solution. Yet the primary contribution of this research is an approach that uses contemporary Grid technologies for integrating heterogeneous data resources that have semantically different. data fields (attributes). It has been practically demonstrated (using a prototype HealthGrid) that discovery in semantically integrated distributed data sources can be feasible by using mainstream Grid technologies, which have been shown to have some Significant advantages over non-Grid based approaches.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Research on the automatic construction of the resource space model for scientific literature

The resource space model is a semantic data model to organize Web resources based on a classification of resources. The scientific resource space is an application of the resource space model on massive scientific literature resources. The construction of a scientific resource space needs to build a category (or concept) hierarchy and classify resources. Manual design suffers from heavy workload and low efficiency. In this thesis, we propose novel methods to solve the following two problems in the construction of a scientific resource space: 1. Automatic maintenance of a category hierarchy. A category hierarchy needs to evolve dynamically with new resources continually arriving so as to satisfy the dynamic re-quirements of the organization and management of resources. We propose an automatic maintenance approach to modifying the category hierarchy according to the hierarchical clustering of resources and show the effectiveness of this method by a series of comparison experiments on multiple datasets. 2. Automatic construction of a concept hierarchy. We propose a joint extraction model based on a deep neural network to extract entities and relations from scientific articles and build a concept hierarchy. Experimental results show the effectiveness of the joint model on the Semeval 2017 Task 10 dataset. We also implement a prototype system of the scientific resource space. The prototype system enables the comparative summarization on scientific articles. A set of novel comparative summarization methods based on the differential topic models (dTM) are proposed in this thesis. The effectiveness of the dTM-based methods is shown by a series of experimental results