PR OWL

There has been much recent progress in building ontologies and publishing scientific data based on these ontologies. This paper overviews issues and progress in the other half of semantic science: having machine accessible scientific theories that can make predictions on this data and can be used for new cases. This paper presents the grand vision, issues that have arisen in building such systems for the geological domain (minerals exploration and geo-hazards), and sketches the formal foundations that underlie this vision.

In the past decade, Statistical Relational Learning (SRL) has emerged as a new branch of machine learning for representing and learning a joint probability distribution over relational data. Relational representations have the necessary expressive power for important real-world problems, but until recently have not supported uncertainty. Statistical relational models fill this gap. Among the languages recently developed for statistical relational representations is Multi-Entity Bayesian Networks (MEBN). MEBN is the logical basis for Probabilistic OWL (PR-OWL), a language for uncertainty reasoning in the Semantic Web. However, until now there has been no implementation of MEBN learning. This paper describes the first implementation of MEBN learning. The algorithm learns a MEBN theory for a domain from data stored in a relational Database. Several issues are addressed such as aggregating influences, optimization problem, and so on. In this paper, as our contributions, we will provide a MEBN-RM (Relational Model) Model which is a bridge between MEBN and RM, and suggest a basic structure learning algorithm for MEBN. And the method was applied to a test case of a maritime domain in order to prove our basic method.

The goal of this research is to create a reference data model for educational and research institutes of Iranian Ministry of Sciences, Research, and Technology. After investigating existing technologies and considering the problem context, ontology was chosen as the data model format. In order to create the target ontology, an ontology construction methodology was designed and implemented. This methodology is created using design science research method and contains an architecture, a detailed workflow process, a guideline for performing 1each step, and related softwares in an integrated web-based system. The designed system is implemented in PHP and is available as open source. The system is used as the main tool to construct the target ontology. The proposed methodology leverages the three main knowledge sources including textual documents, existing ontologies in the higher education domain, and reverse engineering of a relational database of an integrated university system. The resulted product of this methodology was evaluated based on the data requirements of the Ministry of Sciences, Research, and Technology, and its shortcomings were resolved. The novelty of this work is both on the generated product, that is, a localized reference data model, and an ontology construction methodology.

Achieving agile Command and Control in Maritime Operations requires composeability-the ability to construct responses "on the fly" to queries about a given situation, by discovering and drawing upon the appropriate resources from among the vast collection of resources existing on a distributed network. Although net-centric architectures such as FORCEnet provide the necessary connectivity and computational power needed to achieve fast and adaptive decision cycles, the sheer volume of data creates informational and cognitive bottlenecks that hinder agility. To address these limitations, new approaches bridging the gap from data interchange to knowledge interchange are needed, enabling C2 systems to produce a dynamic, comprehensive, and accurate battlespace picture. This is the main focus of PROGNOS, a system for Predictive Naval Situation Awareness currently being developed at George Mason University's C4I Center. PROGNOS will integrate four state-of-the-art enabling technologies into a distributed system architecture that represents domain knowledge as a modular collection of probabilistic ontologies, combine these "knowledge nuggets" dynamically into complex situation models, and apply theoretically sound, computationally efficient hypothesis management and inference to combine evidence and background knowledge to reason about the current situation. PROGNOS will also interoperate with other FORCEnet systems by interacting via semantically enabled services.

Achieving agile Command and Control in Maritime Operations requires composeability-the ability to construct responses "on the fly" to queries about a given situation, by discovering and drawing upon the appropriate resources from among the vast collection of resources existing on a distributed network. Although net-centric architectures such as FORCEnet provide the necessary connectivity and computational power needed to achieve fast and adaptive decision cycles, the sheer volume of data creates informational and cognitive bottlenecks that hinder agility. To address these limitations, new approaches bridging the gap from data interchange to knowledge interchange are needed, enabling C2 systems to produce a dynamic, comprehensive, and accurate battlespace picture. This is the main focus of PROGNOS, a system for Predictive Naval Situation Awareness currently being developed at George Mason University's C4I Center. PROGNOS will integrate four state-of-the-art enabling technologies into a distributed system architecture that represents domain knowledge as a modular collection of probabilistic ontologies, combine these "knowledge nuggets" dynamically into complex situation models, and apply theoretically sound, computationally efficient hypothesis management and inference to combine evidence and background knowledge to reason about the current situation. PROGNOS will also interoperate with other FORCEnet systems by interacting via semantically enabled services.

Situational awareness and prediction are essential elements of information fusion. Both involve various types of uncertainty and require a sound automated inferential process. Probabilistic ontologies support uncertainty management in semantically aware systems, and facilitate modular, interoperable systems. This paper describes the process of developing a probabilistic ontology for a Maritime Domain Awareness application. The ontology was created to support identification of ships behaving suspiciously enough to be declared ships of interest. The original model was expanded in two ways: to provide reasons for declaring a ship as being of interest, and to include individual crew member associations. The latter is achieved by supporting inferences about a person's close relations, group associations, communications, and background influences to assess his likelihood of having terrorist links.

Ontology partitioning is a good solution to overcome challenges of large ontologies such as reusability, scalability, complexity and maintenance. The languages of ontology such as OWL do not allow partial reuse and one must use the whole ontology. The problem is that processing large ontologies consumes more time and space than processing only parts of ontologies. To produce high quality modules, one must consider both semantic and structure of an ontology. In this paper, we partition a given ontology according to its semantic and structure to accomplish reusability. Semantic is considered according to weights given to different relationships, and random walk distance is used to maintain a balance between semantic and structure. In our work, more relationships are taken into account than other approaches that only consider hierarchical relations. We propose an agglomerative and a heuristic algorithm to partition ontologies. Our methods produce meaningful modules and reduce required time and space to process an ontology.

There has been much recent progress in building ontologies and publishing scientific data based on these ontologies. This paper overviews issues and progress in the other half of semantic science: having machine accessible scientific theories that can make predictions on this data and can be used for new cases. This paper presents the grand vision, issues that have arisen in building such systems for the geological domain (minerals exploration and geo-hazards), and sketches the formal foundations that underlie this vision.

The Semantic Web and ontologies have received increased attention in recent years. The delivery of welldesigned ontologies enhances the effect of Semantic Web services, but building ontologies from scratch requires considerable time and effort. Modularizing ontologies and integrating ontology modules to a given context help users effectively develop ontologies and revitalize ontology dissemination. Therefore, various tools for modularizing ontologies have been developed. However, selecting an appropriate tool to fit a given context is difficult because the assumptions for the approaches greatly vary. Therefore, a suitable framework is required to compare and help screen the most suitable modularization tool. In this research, we propose a new evaluation framework for selecting an appropriate ontology modularization tool. We present three aspects of tool evaluation as the main dimensions for the assessment of modularization tools: tool performance, data performance, and usability. This study provides an implicit evaluation and an empirical analysis of three modularization tools. It also provides an evaluation method for ontology modularization, enabling ontology engineers to compare different modularization tools and easily choose an appropriate one for the production of qualifying ontology modules. The experimental results indicate that the proposed evaluation criteria for ontology modularization tools are valid and effective. This research provides a useful method for assessing and selecting ontology modularization tools. Modularization performance, data performance, and usability are the three modularization aspects designed and applied to the context of ontology. We provide a new focus on the comprehensive framework to evaluate the performance and usability of ontology modularization tools. The proposed framework should be of value to both ontology engineers, who are interested in ontology modularization, and to practitioners, who need information on how to evaluate and select a specific type of ontology tool in accordance with the requirements of the individual environment.

The past few years have witnessed an increasingly mature body of research on the Semantic Web, with new standards being developed and more complex use cases being proposed and explored. As complexity increases in SW applications, so does the need for principled means to cope with uncertainty inherent to real world SW applications. Not surprisingly, several approaches addressing uncertainty representation and reasoning on the Semantic Web have emerged [3, 4, 6, 7, 10, 11, 13, 14]. For example, PROWL [3] provides OWL constructs for representing Multi-Entity Bayesian Network (MEBN) [8] theories. This paper reviews some shortcomings of PROWL 1 [2] and describes how they will be addressed in PROWL 2. A method is presented for mapping back and forth from triples into random variables (RV). The method applies to triples representing both predicates and functions. A complex example is given for mapping an n-ary relation using the proposed schematic.

Information in the battlefield comes from reports from diverse sources, in distinct syntax, and with different meanings. There are many kinds of uncertainty involved in this process, e.g., noise in sensors, incorrect, incomplete, or deceptive human intelligence, and others, which makes it essential to have a coherent, consistent, and principled means to represent such phenomena among the systems performing Predictive Situation Awareness (PSAW). PROGNOS is a PSAW system being developed to work within the operational context such as U.S. Navy's FORCENet. It employs probabilistic ontologies in a distributed system architecture as a means to provide semantic interoperability within an intrinsically complex and uncertain environment. This paper explores our current status in developing the system while addressing the major research challenges for making an effective PSAW system to support maritime operations.

Ontology partitioning is a good solution to overcome challenges of large ontologies such as reusability, scalability, complexity and maintenance. The languages of ontology such as OWL do not allow partial reuse and one must use the whole ontology. The problem is that processing large ontologies consumes more time and space than processing only parts of ontologies. To produce high quality modules, one must consider both semantic and structure of an ontology. In this paper, we partition a given ontology according to its semantic and structure to accomplish reusability. Semantic is considered according to weights given to different relationships, and random walk distance is used to maintain a balance between semantic and structure. In our work, more relationships are taken into account than other approaches that only consider hierarchical relations. We propose an agglomerative and a heuristic algorithm to partition ontologies. Our methods produce meaningful modules and reduce required time and space to process an ontology.

In this article, we develop a framework for comparing ontologies and place a number of the more prominent ontologies into it. We have selected 10 specific projects for this study, including general ontologies, domain-specific ones, and one knowledge representation system. The comparison framework includes general characteristics, such as the purpose of an ontology, its coverage (general or domain specific), its size, and the formalism used. It also includes the design process used in creating an ontology and the methods used to evaluate it. Characteristics that describe the content of an ontology include taxonomic organization, types of concept covered, top-level divisions, internal structure of concepts, representation of part-whole relations, and the presence and nature of additional axioms. Finally, we consider what experiments or applications have used the ontologies. Knowledge sharing and reuse will require a common framework to support interoperability of independently created...

This paper aims to fill the gap in major Islamic topics by developing an ontology for the Book of Purification in Islam. Many trusted books start with the Book of Purification as it is the key to prayer (Second Pillar after Shahadah, the profession of faith) and required in Islamic duties like performing Umrah and Hajj. The strategy for developing the ontology included six steps: (1) domain identification, (2) knowledge acquisition, (3) conceptualization, (4) classification, (5) integration and implementation, and (6) ontology generation. Examples of the built tables and classifications are included in this paper. Focus in this paper is given to the design and analysis phases where the technical implementing of the proposed ontology is not within this paper's objectives. Though, we presented an initial implementation to illustrate the steps of our strategy. We make sure that this ontology or knowledge representation on the Book of Purification in Islam satisfy reusability, where the main attributes, concepts, and their relationships are defined and encoded. This formal encoding will be available for sharing and reusing.

Semantic Webs increasingly allow the processing of information over the Internet according to their content and purpose. The widespread use of metadata standards has, however, caused interoperability problems between different systems. Enabling users to search for different languages requires translation resources to cross the language barrier can be a serious challenge. The presented tendency to use semantic webs is an opportunity for legislation, facilitating the process of developing and providing legal regula-tions and managing the progress of legislative work and implementation of procedures. The aim of this article is to review semantic web theory in rela-tion to the pitfalls of defining and applying ontology to the machine-based understanding of content stored in the network. The scope of this article is a preliminary analysis of aspects of interdisciplinary cooperation in the metadata creation phase - indexed according to the conceptual analysis (ontology) of a given field. ...

Probabilistic ontologies incorporate uncertain and incomplete information into domain ontologies, allowing uncertainty in attributes of and relationships among domain entities to be represented in a consistent and coherent manner. The probabilistic ontology language PROWL provides OWL constructs for representing multi-entity Bayesian network (MEBN) theories. Although compatibility with OWL was a major design goal of PROWL , the initial version fell short in several important respects. These shortcomings are addressed by the latest version, PROWL 2. This paper provides an overview of the new features of PROWL 2 and presents a case study of a probabilistic ontology in the maritime domain. The case study describes the process of constructing a PROWL 2 ontology using an existing OWL ontology as a starting point.

Annotating RDF triples within a context (such as provenance) is becoming more and more important in the recent years. A number of approaches exist to represent this annotations, but how to distinguish the information of each context and be able to reason with it is still a challenge. In this work, we present formalizations of the necessary transformations in the RDF graph to encode the contextual information as contextualization function in RDF, and describe the existing approaches according to this formalization. We then present NdProperties, an OWL-DL ontology inspired by NdFluents and singleton properties, and present alternative contextualization functions for it.

Abstract—Probabilistic ontologies incorporate uncertain and incomplete information into domain ontologies, allowing uncertainty in attributes of and relationships among domain entities to be represented in a consistent and coherent manner. The probabilistic ontology language PR-OWL provides OWL constructs for representing multi-entity Bayesian network (MEBN) theories. Although compatibility with OWL was a major design goal of PR-OWL, the initial version fell short in several important respects. These shortcomings are addressed by the latest version, PR-OWL 2. This paper provides an overview of the new features of PR-OWL 2 and presents a case study of a probabilistic ontology in the maritime domain. The case study describes the process of constructing a PR-OWL 2 ontology using an existing OWL ontology as a starting point.

Abstract. As stated in [5], a major design goal for PR-OWL was to attain compatibility with OWL. However, this goal has been only partially achieved as yet, primarily due to several key issues not fully addressed in the original work. This paper describes several important issues of compatibility between PR-OWL and OWL, and suggests approaches to deal with them. To illustrate the issues and how they can be addressed, we use procurement fraud as an example application domain [2]. First, we describe the lack of mapping between PR-OWL random variables (RVs) and the concepts defined in OWL, and then show how this mapping can be done. Second, we describe PR-OWL’s lack of compatibility with existing types already present in OWL, and then show how every type defined in PR-OWL can be directly mapped to concepts already present in OWL.

Probabilistic ontologies incorporate uncertain and incomplete information into domain ontologies, allowing uncertainty in attributes of and relationships among domain entities to be represented in a consistent and coherent manner. The probabilistic ontology language PR-OWL provides OWL constructs for representing multi-entity Bayesian network (MEBN) theories. Although compatibility with OWL was a major design goal of PR-OWL, the initial version fell short in several important respects. These shortcomings are addressed by the latest version, PR-OWL 2. This paper provides an overview of the new features of PR-OWL 2 and presents a case study of a probabilistic ontology in the maritime domain. The case study describes the process of constructing a PR-OWL 2 ontology using an existing OWL ontology as a starting point.

Situational awareness and prediction are essential elements of information fusion. Both involve various types of uncertainty and require a sound automated inferential process. Probabilistic ontologies support uncertainty management in semantically aware systems, and facilitate modular, interoperable systems. This paper describes the process of developing a probabilistic ontology for a Maritime Domain Awareness application. The ontology was created to support identification of ships behaving suspiciously enough to be declared ships of interest. The original model was expanded in two ways: to provide reasons for declaring a ship as being of interest, and to include individual crew member associations. The latter is achieved by supporting inferences about a person's close relations, group associations, communications, and background influences to assess his likelihood of having terrorist links.

In this paper, we present a novel structure-based partitioning algorithm opportunely designed to break a large ontology into different modules related to specific topics for the domain of interest. The main idea behind our work is to exploit topological properties of the ontology graph and several techniques derived from Network Analysis to produce an effective partitioning without considering any information about semantics of ontology relationships. Several preliminary experiments conducted to validate the effectiveness of our approach are also reported.

With the growth rate of information repositories, most of the current research effort are focusing on improving the accuracy in searching and managing information (especially text data), because of lacking of adaptive knowledge representation to the information content of these systems. Besides, domain knowledge is evolving and consequently, ontologies should be automatically built and extended. Thus, introducing modularity paradigm in ontology engineering is now important to tackle scalability problems. In this paper, we address the problem of representing modular ontologies at an abstract level that can improve the traditional information system with higher efficiency, in the context of previous work aiming at integrating ontology learning in traditional Information Retrieval systems on the web. The contribution consists in organizing ontology elements into semantic three-layered ontology warehouse (topic classification, domain knowledge representation, and module representation). The proposed model has been applied for textual content semantic search and relevance improvement has been observed.

With the growth rate of information repositories, most of the current research effort are focusing on improving the accuracy in searching and managing information (especially text data), because of lacking of adaptive knowledge representation to the information content of these systems. Besides, domain knowledge is evolving and consequently, ontologies should be automatically built and extended. Thus, introducing modularity paradigm in ontology engineering is now important to tackle scalability problems. In this paper, we ...

Many real-world ontologies contain thousands of terms and are developed by multiple participants. The use of monolithic ontologies can cause problems that affect various stages of the ontology life cycle. Thus, there is an urgent need for tools and methodologies that facilitate modular ontology design. The benefits of a modular approach include division of labor, scalability, partial reuse, and broadened participation. This article presents a methodology for modular ontology development. The main idea is to facilitate an interoperable hierarchical network of ontology modules. Modules are designed as a combination of more abstract modules in higher levels of the hierarchy. This methodology differs from previous methodologies in the way that it defines concrete development steps, to facilitate use by both naive and expert ontology developers. This methodology is also supported by ontology design patterns and a prototypical ontology development tool.

Two of the most important standardization efforts for e-learning technologies are related to the definition of metadata describing educational resources and metadata describing the learner's profile. The internal details of systems that utilize these metadata is still an open issue since these efforts are primarily dealing with "what" and not "how". Under the light of these emerging efforts, we present CG-PerLS, a knowledge based approach for organizing and accessing educational resources. CG-PerLS is a model of a WWW portal for learning objects that encodes the learning technologies metadata in the Conceptual Graph knowledge representation formalism, and uses related inference techniques to provide advanced, personalized functionality. CG-PerLS allows learning resource creators to manifest their material, client-side learners to access these resources in a way tailored to their individual profile and educational needs, and dynamic course generation based on fine or coarse grained educational resources.

In this paper, we propose a pragmatic approach where time consumption of RDFS reasoning remains fixed with increasing sizes of instance data. The approach infers facts about schema, but prevents producing facts about individuals. At the time of query answering the queries are rewritten. The query rewriting process does not result in complex or disjunctive queries due to the syntactic transformation that we made on the ontology and the rules. The most prominent contribution of this work is reducing reasoning to schema level without increasing query complexity. Thus, query execution performance improves in a considerable manner.

A common approach for reasoning is to compute the deductive closure of an ontology using the rules specified and to work on the closure at query time. This approach reduces the run time complexity but increases the space requirements. The main reason of this increase is the type and subclass statements in the ontology. Type statements show a significant percentage in most ontologies. Since subclass is a transitive property, derivation of other statements, in particular type statements relying on it, gives rise to cyclic repetition and an excess of inferred type statements. In brief, a major part of closure computation is deriving the type statements relying on subclass statements. In this paper, we propose a syntactic transformation that is based on novel individual grouping constructs. This transformation reduces the number of inferred type statements relying on subclass relations. Thus, the space requirement of reasoning is reduced without affecting the soundness and the completeness.

This paper introduces 'just enough' principles and 'systems engineering' approach to the practice of ontology development to provide a minimal yet complete, lightweight, agile and integrated development process, supportive of stakeholder management and implementation independence.

Ontology partitioning is a good solution to overcome challenges of large ontologies such as reusability, scalability, complexity and maintenance. The languages of ontology such as OWL do not allow partial reuse and one must use the whole ontology. The problem is that processing large ontologies consumes more time and space than processing only parts of ontologies. To produce high quality modules, one must consider both semantic and structure of an ontology. In this paper, we partition a given ontology according to its semantic and structure to accomplish reusability. Semantic is considered according to weights given to different relationships, and random walk distance is used to maintain a balance between semantic and structure. In our work, more relationships are taken into account than other approaches that only consider hierarchical relations. We propose an agglomerative and a heuristic algorithm to partition ontologies. Our methods produce meaningful modules and reduce required time and space to process an ontology.

Abstract. We describe the design and use of the Ontology Pre-Processor Language (OPPL) as a means of embedding the use of Knowledge Patterns in OWL ontologies. We illustrate the specification of patterns in OPPL and discuss the advantages of its adoption by Ontology Engineers with respect to ontology generation, transformation, and maintainability. The consequence of the declarative specification of patterns will be their unambiguous description inside an ontology in OWL. Thus, OPPL enables an ontology engineer to work ...

W artykule zaprezentowane zostały główne koncepcje sieci semantycznych i ich wykorzystanie w procesie kojarzenia faktów pozwalających na identyfikacje potencjalnych sytuacji kryzysowych. Opisywana metoda ma umożliwić zbudowanie narzędzi identyfikacji działań terrorystycznych w oparciu o modele ontologiczne. Zaproponowane rozwiązania obejmują szerokie spektrum algorytmów operujących na strukturach grafowych oraz mechanizmy wnioskowania regułowego. W ramach pracy dokonany został przegląd dostępnych języków i narzędzi opisu semantycznego oraz analiza ich przydatności w metodzie. Reprezentacja wiedzy konstruowanej w postaci sieci powiązań dostarcza wielu problemów związanych z efektywnością algorytmów ich przetwarzania. Na potrzeby budowanego rozwiązania zaproponowano dwa odrębne narzędzia redukcji rozmiaru sieci semantycznej polegającej na analizie ilościowej parametrów sieci semantycznej i eliminacji wierzchołków, związków nieistotnych z punktu widzenia analizowanej domeny oraz ontolo...

Ontologies are parts of an Artificial Intelligent advance research area where it is a very familiar topic in computer science. There are many domains of interests that can utilize ontolologies to use them as the knowledge repository. Economy is one of the widely interesting topics that anyone can relate to. This research simplifies ontologies concepts using economy domain to guide people in the field of economics to understand how ontology works as well as to guide the computer software developers to understand the basic concept of economy. The economy ontology is illustrated in ontology chart and hierarchical chart.

The focus of ontology modularization to date has largely been on the creation of techniques to carry out ontology modularization. This creates a problem in evaluating the results of the different techniques. Ontology modularization techniques cannot solely be evaluated by examining their logical properties. Certain applications of ontology modularization, such as ontology reuse, require a new objective way to evaluate the results. This paper motivates the use of an entropy inspired measure to evaluate ontology modules by arguing that current objective measures of evaluation do not reconcile with the subjective measures employed by Ontology Engineers. Experiments are conducted to show that an entropy based evaluation of ontology modules is beneficial to an Ontology Engineer evaluating the results of ontology module extraction techniques.

Abstract. We describe the design and use of the Ontology Pre-Processor Language (OPPL) as a means of embedding the use of Knowledge Patterns in OWL ontologies. We illustrate the specification of patterns in OPPL and discuss the advantages of its adoption by Ontology Engineers with respect to ontology generation, transformation, and maintainability. The consequence of the declarative specification of patterns will be their unambiguous description inside an ontology in OWL. Thus, OPPL enables an ontology engineer to work ...

Two of the most important standardization efforts for e-learning technologies are related to the definition of metadata describing educational resources and metadata describing the learner's profile. The internal details of systems that utilize these metadata is still an open issue since these efforts are primarily dealing with "what" and not "how". Under the light of these emerging efforts, we present CG-PerLS, a knowledge based approach for organizing and accessing educational resources. CG-PerLS is a model of a WWW portal for learning objects that encodes the learning technologies metadata in the Conceptual Graph knowledge representation formalism, and uses related inference techniques to provide advanced, personalized functionality. CG-PerLS allows learning resource creators to manifest their material, client-side learners to access these resources in a way tailored to their individual profile and educational needs, and dynamic course generation based on fine or coarse grained educational resources.

Two of the most important standardization efforts for e-learning technologies are related to the definition of metadata describing educational resources and metadata describing the learner's profile. The internal details of systems that utilize these metadata is still an open issue since these efforts are primarily dealing with "what" and not "how". Under the light of these emerging efforts, we present CG-PerLS, a knowledge based approach for organizing and accessing educational resources. CG-PerLS is a model of a WWW portal for learning objects that encodes the learning technologies metadata in the Conceptual Graph knowledge representation formalism, and uses related inference techniques to provide advanced, personalized functionality. CG-PerLS allows learning resource creators to manifest their material, client-side learners to access these resources in a way tailored to their individual profile and educational needs, and dynamic course generation based on fine or coarse grained educational resources.

Some of the most outstanding problems in Computer Science (e.g. access to heterogeneous information sources, use of different e-commerce standards, ontology translation, etc.) are often approached through the identification of ontology mappings. A manual mapping generation slows down, or even makes unfeasible, the solution of particular cases of the aforementioned problems via ontology mappings. Some algorithms and formal models for partial tasks of automatic generation of mappings have been proposed. However, an integrated framework to solve this problem is still missing. In this paper, we present a framework for automatic ontology mapping generation, and a partial implementation of it. Our proposal is that this integrated vision can guide, not only our future work, but also the future work of other researchers. In the implementation carried out, we have built a mapping ontology with knowledge on ontology mappings.

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Ontologies are the core component in semantic Web applications. The employment of an ontology building method affects the quality of ontology and the applicability of ontology language. A weighted evaluation approach for ontology building guidelines is presented in this paper. The evaluation criteria are based on an existing classification scheme of a semiotic framework for evaluating the quality of conceptual models. Directions for further refinement of ontology building methods are discussed.

Our paper tries to open a discussion about a new research topic: Topic Maps, which we think it can be interesting to incorporate into software engineering projects as a technical terminology. Not only it is a new profound paradigms shift but also an unknown vision of how to represent knowledge in a smart way. We can approach to the paradigm thanks to the Project EA-2003-52, suportted by the Spanish Ministry of Education, about subject gateways. We explain the software used to develop the first version, and while we were developing it, the reflection that make we think: Can concept maps be topic maps, when they begin to display lots of information in a relational way?

Prawa Autorskie: Materiał był opublikowany przez PWNT w książce lub elektronicznym wydaniu książki pod tytułem "Zarządzanie technologiami informatycznymi : stan i perspektywy rozwoju". Możliwe jest zrobienie jednej kopii bądź wydruku tylko i wyłącznie do użytku własnego. Wielokrotne kopiowanie w celach komercyjnych, bądź modyfikowanie treści materiału jest zabronione. Strona WWW wydawnictwa: http://www.konsulting.gda.pl/pwnt.html W rozdziale tym zostały przedstawione podstawowe zagadnienia związane z tworzeniem ontologii rozumianych jako modele wiedzy o wybranej dziedzinie. Prezentowane są języki służące do opisu ontologii, aplikacje wspomagające tworzenie i implementację ontologii, a także przykładowe projekty, których celem jest rozwijanie ontologii dotyczących wiedzy ogólnej lub ograniczonych do określonych domen.

Trudno jest znaleźć bardziej uderzający kontrast niż ten, który zachodzi pomiędzy postawą poznawczą inżyniera a postawą filozofa. Nawet jeżeli zajmujemy się etyką środowiska a nasz kolega inżynier bada teoretyczne podstawy kształtowania krajobrazu, to pomiędzy naszymi a jego metodami i celami badawczymi rozpościera się przepaść, wyznaczona przez opozycję: teoria vs praktyka. Dlatego termin „inżynieria ontologiczna” wydaje się zawierać contradictio in adiecto.

Problems with large monolithical ontologies in terms of reusability, scalability and maintenance have led to an increasing interest in modularization techniques for ontologies. Currently, existing work suffers from the fact that the notion of modularization is not as well understood in the context of ontologies as it is in software engineering. In this paper, we experiment on applying state-of-the-art tools for ontology modularization in the context of a concrete application: the automatic selection of knowledge components to be used for Web page annotation and semantic browsing. We conclude that, in a broader context, an evaluation framework is required to guide the choice of a modularization tool, in accordance with the requirements of the considered application.

While many authors have argued for the benefits of applying principles of modularization to ontologies, there is not yet a common understanding of how modules are defined and what properties they should have. In the previous section, this question was addressed from a purely logical point of view. In this chapter, we take a broader view on possible criteria that can be used to determine the quality of a modules. Such criteria include logic-based, but also structural and application-dependent criteria, sometimes borrowing from related fields such as software engineering. We give an overview of possible criteria and identify a lack of application-dependent quality measures. We further report some modularization experiments and discuss the role of quality criteria and evaluation in the context of these experiments.

Abstract Problems resulting from the management of shared, distributed knowledge has led to ontologies being employed as a solution, in order to effectively integrate information across applications. This is dependent on having ways to share and reuse existing ontologies; with the increased availability of ontologies on the web, some of which include thousands of concepts, novel and more efficient methods for reuse are being devised.

In this paper we propose facilitating ontology development by constant evaluation of steps in the process of ontology development. Existing methodologies for ontology development are complex and they require technical knowledge that business users and developers don’t poses. By introducing ontology completeness indicator developer is guided throughout the development process and constantly aided by recommendations to progress to next step and improve the quality of ontology. In evaluating the ontology, several aspects are considered; from description, partition, consistency, redundancy and to anomaly. The applicability of the approach was demonstrated on Financial Instruments and Trading Strategies (FITS) ontology with comparison to other approaches.

As the sheer volume of new knowledge increases, there is a need to find effective ways to convey and correlate emerging knowledge in machine-readable form. The success of the Semantic Web hinges on the ability to formalize distributed knowledge in terms of a varied set of ontologies. We present Pan-Onto-Eval, a comprehensive approach to evaluating an ontology by considering its structure, semantics, and domain. We provide formal definitions of the individual metrics that constitute Pan-Onto-Eval, and synthesize them into an integrated metric. We illustrate its effectiveness by presenting an example based on multiple ontologies for a University.