IT Service Management deals with managing a broad range of items related to complex system environments. As there is both, a close connection to business interests and IT infrastructure, the application of semantic expressions which are seamlessly integrated within applications for managing ITSM environments, can help to improve transparency and profitability. This paper focuses on the challenges regarding the integration of semantics and ontologies within ITSM environments. It will describe the paradigm of relationships and inheritance within complex service trees and will present an approach of ontologically expressing them. Furthermore, the application of SBVR-based rules as executable SQL triggers will be discussed. Finally, the broad range of topics for further research, derived from the findings, will be presented.

Informledge System (ILS) is a knowledge network with autonomous nodes and intelligent links that integrate and structure the pieces of knowledge. In this paper, we put forward the strategies for knowledge embedding and retrieval in an ILS. ILS is a powerful knowledge network system dealing with logical storage and connectivity of information units to form knowledge using autonomous nodes and multi-lateral links. In ILS, the autonomous nodes known as Knowledge Network Nodes (KNN)s play vital roles which are not only used in storage, parsing and in forming the multi-lateral linkages between knowledge points but also in helping the realization of intelligent retrieval of linked information units in the form of knowledge. Knowledge built in to the ILS forms the shape of sphere. The intelligence incorporated into the links of a KNN helps in retrieving various knowledge threads from a specific set of KNNs. A developed entity of information realized through KNN forms in to the shape of a knowledge cone

Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.

Simple conceptual graphs are considered as the kernel of most knowledge representation formalisms built upon Sowa's model. Reasoning in this model can be expressed by a graph homomorphism called projection, whose semantics is usually given in terms of positive, conjunctive, existential FOL. We present here a family of extensions of this model, based on rules and constraints, keeping graph homomorphism as the basic operation. We focus on the formal definitions of the different models obtained, including their operational semantics and relationships with FOL, and we analyze the decidability and complexity of the associated problems (consistency and deduction). As soon as rules are involved in reasonings, these problems are not decidable, but we exhibit a condition under which they fall in the polynomial hierarchy. These results extend and complete the ones already published by the authors. Moreover we systematically study the complexity of some particular cases obtained by restricting the form of constraints and/or rules.

This paper presents our work on development of OWL-driven systems for formal representation and reasoning about terminological knowledge and facts in petrology. The long-term aim of our project is to provide solid foundations for a large-scale integration of various kinds of knowledge, including basic terms, rock classification algorithms, findings and reports. We describe three steps we have taken towards that goal here. First, we develop a semi-automated procedure for transforming a database of igneous rock samples to texts in a controlled natural language (CNL), and then a collection of OWL ontologies. Second, we create an OWL ontology of important petrology terms currently described in natural language thesauri. We describe a prototype of a tool for collecting definitions from domain experts. Third, we present an approach to formalization of current industrial standards for classification of rock samples, which requires linear equations in OWL 2. In conclusion, we discuss a range of opportunities arising from the use of semantic technologies in petrology and outline the future work in this area.

In this paper we propose an ontological framework for tools facilitating creative writing and story reading. It is based on an ontology implemented as a topic map and employs linguistic analysis methods for discovering conceptual entities in the text.

Various strategies and techniques have been proposed for the generation of questions/answers tests in Intelligent Tutoring Systems by using OWL (Web Ontology Language) ontolo- gies. Currently there have been no known methods to utilize SWRL rules for this task. This paper presents a system and a set of strategies that can be used in order to automatically generate multiple choice questions from SWRL rules. The aim of the proposed framework is to support further research in the area and to be a testbed for the development of more advanced assessment techniques.

Ontologies have been criticized because they are not sufficiently flexible, and thus cannot capture the dynamism and complexity of reality. However, they have increasingly come into focus because of the need for knowledge management in both general and specialized knowledge domains. EcoLexicon is a frame-based visual thesaurus on the environment that is gradually evolving towards the status of a formal ontology. For this purpose, the information in its relational database is in the process of being linked to the ontological system of FunGramKB, a multipurpose knowledge base that has been specifically designed for natural language understanding with modules for lexical, grammatical, and conceptual knowledge. This enables the explicitation of specialized knowledge as an extension of general knowledge through its representation in the domain-specific satellite ontology of a main general ontology.

Intelligent tutoring systems (ITS) is a multidisciplinary field of study that draws upon artificial intelligence, computer science, and cognitive science to create computerized tutoring systems that offer immediate feedback and individualized instruction. Broadly construed, most ITSs can be characterized as having two loops: an outer loop and an inner loop. The outer loop intelligently selects the next relevant task for the student to complete. The inner loop iterates over individual problem-solving steps and provides contextually relevant feedback and instructional guidance. The ultimate goal of an ITS is to promote deep learning that persists over time, transfers to new domains, and accelerates future learning.

This paper describes preliminary research in the use of ontological knowledge for the task of automatically classifying classical Chinese poetry (CCCP) according to authorship. Based on a collection of poems written by Liu Yong (987–1053 AD) and Su Shi (1037– 1101 AD), which have been analyzed according to a taxonomy of ontological entities at the lexical level, the research looks into the issue of whether characteristic features can be automatically extracted as important stylistic differences between the two poets. This paper examines the efficiency of different ontological concepts as features in CCCP using Support Vector Machine (SVMs). The experiment shows that an integration of ontological knowledge and bags-of-words (BoW) produces a higher precision for CCCP than BoW only with an overall increase of 2.1% and 2.2% in terms of precision and F-score.