Technology and intellectual property markets have witnessed great developments in the last few decades. Due to intellectual property rights gaining more importance and technology companies opening up their innovation processes, a wide range of intellectual property rights related services have emerged in the last two decades. The goal of this research is to develop a comprehensive classification system of intellectual property rights related services (IPSC). The classification is created by applying an ontology engineering process. The IPSC consists of 72 various IPR services divided into six main categories (100 Legal Service; 200 IP Consulting; 300 Matchmaking and Trading; 400 IP Portfolio Processing; 500 IPR-related Financial Service; 600 IPR-related Communication Service). The implications of the thesis are directed to policy makers, technology transfer managers, C-level executives and innovation researchers. The IPSC enables practitioners and researchers to organize industry data that can be thereafter analyzed for better strategy and policy making. In addition, this contributes towards organizing a more transparent and single intellectual property market.:Acknowledgements I Abstract II Contents IV List of Figures VI List of Tables VII 1. Introduction 1 1.1. Introduction to Technology Markets 1 1.2. Explanation of Key Concepts 5 1.3. Research Questions and Goals 9 1.4. Readers Guide 13 2. Literature Review 15 2.1. Intellectual Property Markets State of the Art Review 15 2.2. Ontology Engineering State of the Art Review 22 3. Methodology 26 3.1. Methontology 26 3.2. Planning the IPSC 29 3.3. Specification 30 3.4. Conceptualization 31 3.5. Formalization 32 3.6. Integration 32 3.7. Evaluation 33 3.8. Documentation 33 3.9. Realization and Maintenance 33 4. Data description and collection framework 34 5. Applying Methontology 46 5.1. Knowledge Acquisition and Planning the IPSC 46 5.2. Specification 46 5.3. Conceptualization 47 5.4. Formalization 54 100 Legal Service 56 200 IP Consulting 60 300 Matchmaking and Trading 65 400 IP Portfolio Processing 72 500 IPR-related Financial Service 76 600 IPR-related Communication Service 81 5.5. Integration 86 5.6. Evaluation 95 5.7. Documentation 104 5.8. Realization and Maintenance of the IPSC 106 6. Interview Results and Further Discussions 108 6.1. Implications for Industry 108 6.2. Contributions of the IPSC 110 6.3. Limitations of the IPSC and Future Work 112 7. Conclusions 116 References 120 List of experts interviewed and the date of interview 129 Appendices 130

In the context of the Semantic Web, ontologies based on Description Logics are gaining more and more importance for knowledge representation on a large scale. While the need arises for high quality ontologies with large background knowledge to enable powerful machine reasoning, the acquisition of such knowledge is only advancing slowly, because of the lack of appropriate tools. Concept learning algorithms have made a great leap forward and can help to speed up knowledge acquisition in the form of induced concept descriptions. This work investigated whether concept learning algorithms have reached a level on which they can produce results that can be used in an ontology engineering process. Two learning algorithms (YinYang and DL-Learner) are investigated in detail and tested with benchmarks. A method that enables concept learning on large knowledge bases on a SPARQL endpoint is presented and the quality of learned concepts is evaluated in a real use case. A proposal is made to increase the complexity of learned concept descriptions by circumventing the Open World Assumption of Description Logics.

This thesis contributes to the research field information logistics. Information logistics aims at improving information flow and at reducing information overload by providing the right information, in the right context, at the right time, at the right place through the right channel. Ontologies are expected to contribute to reduced information overload and solving information supply problems. An ontology is created to form some kind of shared understanding for the involved stakeholders in the domain at hand. By using this semantic structure you can further build applications that use the ontology and support the employee by providing only the most important information for this person. During the last years, there has been an increasing number of successful cases in which industrial applications successfully use ontologies. Most of these cases however, stem from large enterprises or IT-intensive small or medium-sized enterprises (SME). The current ontology development methodologies are not tailored for SME and their specific demands and preferences, such as that SME prefer mature technologies, and show a clear preference for to a large extent standardised solutions. The author proposes a new ontology development methodology, taking the specific characteristics of SME into consideration. This methodology was tested in an application case, which resulted in a number of concrete improvement ideas, but also the conclusion that further specialisation of the methodology was needed, for example for a specific usage area or domain. In order to find out in which direction to specify the methodology a survey was performed among SME in the region of Jönköping. The main conclusion from the survey is that ontologies can be expected to be useful for SME mainly in the area of product configuration and variability modelling. Another area of interest is document management for supporting project work. The area of information search and retrieval can also be seen as a possible application field, as many of the respondents of the survey spend much time finding and saving information.

With the popularity of the World Wide Web, a large amount of data is generated and made available through the Internet everyday. To integrate and query this huge amount of heterogeneous data, the vision of Semantic Web has been recognized as a possible solution. One key technology for the Semantic Web is ontologies. Many ontologies have been developed in recent years. Meanwhile, due to the demand of applications using multiple ontologies,  mappings between entities of these ontologies are generated as well, which leads to the generation of ontology networks consisting of ontologies and mappings between these ontologies. However, neither developing ontologies nor finding mappings between ontologies is an easy task. It may happen that the ontologies are not consistent or complete, or the mappings between these ontologies are not correct or complete, or the resulting ontology network is not consistent. This may lead to problems when they are used in semantically-enabled applications. In this thesis, we address two issues relevant to the quality of the mappings and the structure in the ontology network. The first issue deals with the missing mappings between networked ontologies. Assuming existing mappings between ontologies are correct, we investigate whether and how to use these existing mappings, to find more mappings between ontologies. We propose and test several strategies of using the given correct mappings to align ontologies. The second issue deals with the missing structure, in particular missing is-a relations, in networked ontologies. Based on the assumption that missing is-a relations are a kind of modeling defects, we propose an ontology debugging approach to tackle this issue. We develop an algorithm for detecting missing is-a relations in ontologies, as well as algorithms which assist the user in repairing by generating and recommending possible ways of repairing and executing the repairing. Based on this approach, we develop a system and test its use and performance.

The World Wide Web contains large amounts of data and in most cases this data is without any explicit structure. The lack of structure makes it difficult for automated agents to understand and use such data. A step towards a more structured World Wide Web is the idea of the Semantic Web which aims at introducing semantics to data on the World Wide Web. One of the key technologies in this endeavour are ontologies which provide means for modeling a domain of interest. Developing and maintaining ontologies is not an easy task and it is often the case that defects are introduced into ontologies. This can be a problem for semantically-enabled applications such as ontology-based querying. Defects in ontologies directly influence the quality of the results of such applications as correct results can be missed and wrong results can be returned. This thesis considers one type of defects in ontologies, namely the problem of completing the is-a structure in ontologies represented in description logics. We focus on two variants of description logics, the EL family and ALC, which are often used in practice. The contributions of this thesis are as follows. First, we formalize the problem of completing the is-a structure as a generalized TBox abduction problem (GTAP) which is a new type of abduction problem in description logics. Next, we provide algorithms for solving GTAP in the EL family and ALC description logics. Finally, we describe two implemented systems based on the introduced algorithms. The systems were evaluated in two experiments which have shown the usefulness of our approach. For example, in one experiment using ontologies from the Ontology Alignment Evaluation Initiative 58 and 94 detected missing is-a relations were repaired by adding 54 and 101 is-a relations, respectively, introducing new knowledge to the ontologies.

No description available.

Software intensive systems are developed to provide solutions in some problem domain and software engineering principles are employed to develop and implement that system. Software engineering principles should enhance the development and production of software artifacts and yet the artifacts often lack in quality. Crucial in the development process are requirements engineering activities and methods for software documentation. This research focused on requirements engineering activities, software requirements documentation and employed a new approach in these activities that incorporated ontology engineering principles. Ontology engineering refers to the set of activities concerned with the ontology development process, the ontology life cycle, the methods for building ontologies, and the tool suites and languages that support them. Ontologies facilitate domain knowledge reuse and sharing and provides a common vocabulary to system developers. The motivation of this research came from Ambr´osio and Kaiya, advocating the definition of the Software Requirements Knowledge Area of the Software Engineering Body of Knowledge (SWEBOK ) within an ontology system. The resulting system utilized the benefits of intelligent reasoning to elicit, automatically verify, extract and document software requirements. The requirements engineering process was modeled in an ontology. An ontology is a machine-readable data structure that distinctly defines concepts and describes relationships among those concepts. The requirements engineering process and ontology were the focal points in this research. A baseline ontology for software requirements engineering was created. The following are contributions of this research. A methodology was designed to enhance the software documentation production process. An initial ontology model of SWEBOK recommended data items was created. A method was provided to verify software requirements as they were elicited, entered and maintained in an ontology. A method was created that electronically provided provenance of software requirements. Software was created to automatically extract the software requirements from within an ontology.

Due to the explosion of the amount of biomedical data, knowledge and tools that are often publicly available over the Web, a number of difficulties are experienced by biomedical researchers. For instance, it is difficult to find, retrieve and integrate information that is relevant to their research tasks. Ontologies and the vision of a Semantic Web for life sciences alleviate these difficulties. In recent years many biomedical ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies they have to be aligned or merged. A number of systems have been developed for aligning and merging ontologies and various alignment strategies are used in these systems. However, there are no general methods to support building such tools, and there exist very few evaluations of these strategies. In this thesis we give an overview of the existing systems. We propose a general framework for aligning and merging ontologies. Most existing systems can be seen as instantiations of this framework. Further, we develop SAMBO (System for Aligning and Merging Biomedical Ontologies) according to this framework. We implement different alignment strategies and their combinations, and evaluate them in terms of quality and processing time within SAMBO. We also compare SAMBO with two other systems. The work in this thesis is a first step towards a general framework that can be used for comparative evaluations of alignment strategies and their combinations. / <p>Report code: LiU-Tek-Lic-2006:6.</p>

Yes / Yes / The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing a colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion.

It turns out that for most users of spatial data presents a harmonization of data (as an elimination of heterogeneity) as the greatest problem - a transformation their own data sets to the data specifications produced by the INSPIRE Directive so as not to limit current processes processing, management, sharing and presentation of data. Therefore, they need the widest possible description of spatial data, because it will only eliminate the problems of heterogeneity. The author of this Ph.D. thesis dealt with the opportunities which ontologies bring to the process of data modelling. The aim was to describe using ontologies the selected part (classification systems) of data models of spatial data themes "Land Cover" and "Land Use" which are defined in Annexes II and III of the INSPIRE directive. Developed ontologies serve as a source of information on classification systems. Their evaluation (so-called reasoning) will provide new information (relationships between taxonomy elements) that play an important role in the transformation process of classification systems as part of the harmonization of spatial data. Creating of ontologies consisted of three main steps - selection of appropriate methodology, development of a basic ontology structure and iterative process of populating and specification of...