Ontologies: Instructional Materials



Ontologies for Business Analysis Udemy

#artificialintelligence

The practice of Business Analysis revolves around the formation, transformation and finalisation of requirements to recommend suitable solutions to support enterprise change programmes. Practitioners working in the field of business analysis apply a wide range of modelling tools to capture the various perspectives of the enterprise, for example, business process perspective, data flow perspective, functional perspective, static structure perspective, and more. These tools aid in decision support and are especially useful in the effort towards the transformation of a business into the "intelligent enterprise", in other words, one which is to some extent "self-describing" and able to adapt to organisational change. However, a fundamental piece remains missing from the puzzle. Achieving this capability requires us to think beyond the idea of simply using the current mainstream modelling tools.


Publishing Math Lecture Notes as Linked Data

arXiv.org Artificial Intelligence

We mark up a corpus of LaTeX lecture notes semantically and expose them as Linked Data in XHTML+MathML+RDFa. Our application makes the resulting documents interactively browsable for students. Our ontology helps to answer queries from students and lecturers, and paves the path towards an integration of our corpus with external sites.


Toward a Category Theory Design of Ontological Knowledge Bases

arXiv.org Artificial Intelligence

I discuss (ontologies_and_ontological_knowledge_bases / formal_methods_and_theories) duality and its category theory extensions as a step toward a solution to Knowledge-Based Systems Theory. In particular I focus on the example of the design of elements of ontologies and ontological knowledge bases of next three electronic courses: Foundations of Research Activities, Virtual Modeling of Complex Systems and Introduction to String Theory.


Alignment of Heterogeneous Ontologies: A Practical Approach to Testing for Similarities and Discrepancies

AAAI Conferences

Ontology alignment is regarded as one of the core tasks in many Web services. It is concerned with finding the correspondences between separate ontologies by identifying concepts with the same or similar semantics in order to resolve semantic heterogeneity between them. Existing ontology alignment techniques are tailored towards today's ontology languages, which are not capable of representing and reasoning with uncertain or incomplete information. It is expected, however, that future Semantic Web services will rely on the development and use of proper domain ontologies. Alignment of such ontologies goes beyond standard concept matching, and requires nonstandard logic processing. In this paper, we present an alignment technique utilizing an alternative, rule-based representation, which provides a uniform framework for representing and mapping heterogeneous ontologies. To justify and illustrate our research, we describe an example application scenario.


Augmenting AI Coursework Through Undergraduate Research

AAAI Conferences

All courses in Artificial Intelligence are not equal. The topics covered by a course entitled Artificial Intelligence vary widely. The Computing Curricula 2001: Computer Science offers a good deal of flexibility for degree programs to meet the prescribed standard of knowledge units for the field of Intelligent Systems. Most, but not all, Historically Black Colleges and Universities can achieve more than the minimum recommended core hours through a one-semester, intermediate-level course in the Junior/Senior year. A few have the ability to offer at least one advanced course as a Senior Elective. At our institution, we found undergraduate research projects to be an excellent means of preparing students for the one-semester, intermediate-level Artificial Intelligence course, or extending what can be covered in that course. This paper presents a review of the suggested coursework for a one-semester, intermediate-level course in Artificial Intelligence and what is possible at a non-Research Type I institution. It then gives two areas where undergraduate research projects have been used to create interest in or expand knowledge of Artificial Intelligence topics, thus covering more than what is possible in one course.


Ontology Extraction for Educational Knowledge Bases

AAAI Conferences

A student who wishes to learn about some particular topic does not have many options. An often used tool is the search engine, which gives a tiny and difficult to control window into the vast amounts of information that is available on the internet. A student who wants to learn some concept should be able to interact with the available information in a coherent and personalized way. The classroom is the ideal of this goal, and our system would not replace, but augment it. It is within the reach of modern tutoring systems to use both knowledge of the student and of the subject's structure in order to present a subject in a manner that is more coherent and pedagogically sound than currently existing technology.