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.

S. Tenier, A. Napoli, X. Polanco and Y.Toussaint (2006) With the proliferation of news articles from thousands of developed an automatic WebPages semantic annotation different sources now available on the Web, summarization system. The objective is to classify pages concerning teams of such information is becoming increasingly important. of research, in order to be able to determine for example Considering the large number of news source (for who works where, on what and with whom (use of examples, BBC, Reuters, CNN…), every day, thousands of ontology of the domain). It consists, first, of the articles are produced in the entire world concerning a given identification of the syntactic structure characterizing the event.

In this paper we introduce a system that is designed to automatically populate a knowledge base from both structured and unstructured text given an ontology. Our system is designed as a modular end-to-end system that takes structured or unstructured data as input, extracts information, maps relevant information to an ontology, and finally disambiguates entities in the knowledge base. The novelty of our approach is that it is domain independent and can easily be adapted to new ontologies and domains. Unlike most knowledge base population systems, ours includes entity detection. This feature allows one to employ very complex ontologies that include events and the entities that are involved in the events.

Linguistic and semantic annotations are important features for text-based applications. However, achieving and maintaining a good quality of a set of annotations is known to be a complex task. Many ad hoc approaches have been developed to produce various types of annotations, while comparing those annotations to improve their quality is still rare. In this paper, we propose a framework in which both linguistic and domain information can cooperate to reason with annotations. The underlying knowledge representation issues are carefully analyzed and solved by studying a higher order logic, which accounts for the cooperation of different sorts of knowledge. Our prototype implements this logic based on a reduction to classical description logics by preserving the semantics, allowing us to benefit from cutting-edge Semantic Web reasoners. An application scenario shows interesting merits of this framework on reasoning with annotations of texts.