3 research outputs found
Adaptive Emotional Personality Model based on Fuzzy Logic Interpretation of Five Factor Theory
In recent years, emotional personality has found an important application in the field of human machine
interaction. Interesting examples of this domain are computer games, interface agents, human-robot
interaction, etc. However, few systems in this area include a model of personality, although it plays an
important role in differentiating and determining the way they experience emotions and the way they
behave. Personality simulation has always been a complex issue due to the complexity of the human
personality itself, and the difficulty to model human psychology on electronic basis. Current efforts for
emotion simulation are rather based on predefined set or inputs and its responses or on classical models
which are simple approximate and have proven flaws. In this paper an emotional simulation system was
presented. It utilizes the latest psychological theories to design a complex dynamic system that reacts to
any environment, without being pre-programmed on sets of input. The design was relying on fuzzy logic
to simulate human emotional reaction, thus increasing the accuracy by further emulating human brain and
removing the pre-defined set of input and its matched output
Towards interoperability of i* models using iStarML
Goal-oriented and agent-oriented modelling provides an effective approach to the understanding of distributed information
systems that need to operate in open, heterogeneous and evolving environments. Frameworks, firstly introduced more than ten
years ago, have been extended along language variants, analysis methods and CASE tools, posing language semantics and tool interoperability issues. Among them, the i* framework is one the most widespread. We focus on i*-based modelling languages and tools and on the problem of supporting model exchange between them. In this paper, we introduce the i* interoperability problem and derive an XML interchange format, called iStarML, as a practical solution to this problem. We first discuss the main requirements for its definition, then we characterise the core concepts of i* and we detail the tags and options of the interchange format. We complete the presentation of iStarML showing some possible applications. Finally, a survey on the i* community perception about iStarML is included for assessment purposes.Preprin
Exploiting general-purpose background knowledge for automated schema matching
The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process.
In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources.
A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems.
One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented.
In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications