The conceptual modelling built from text is rarely an ontology. As a matter of fact, such a conceptualization is corpus-dependent and does not offer the main properties we expect from ontology. Furthermore, ontology extracted from text in general does not match ontology defined by expert using a formal language. It is not surprising since ontology is an extra-linguistic conceptualization whereas knowledge extracted from text is the concern of textual linguistics. Incompleteness of text and using rhetorical figures, like ellipsis, modify the perception of the conceptualization we may have. Ontological knowledge, which is necessary for text understanding, is not in general embedded into documents.

The construction of a reference ontology for a large domain still remains an hard human task. The process is sometimes assisted by software tools that facilitate the information extraction from a textual corpus. Despite of the great use of XML Schema files on the internet and especially in the B2B domain, tools that offer a complete semantic analysis of XML schemas are really rare. In this paper we introduce Janus, a tool for automatically building a reference knowledge base starting from XML Schema files. Janus also provides different useful views to simplify B2B application integration.

This paper presents a methodology to acquire new knowledge in TutorJ using external information sources. TutorJ is an ITS whose architecture is inspired to the HIPM cognitive model, while meta-cognition principles have been used to design the knowledge acquisition process. The system behavior is intended to increase its own knowledge as a consequence of the interaction with users. The implemented methodology uses external links and services to capture new knowledge from contents related to discussion topics and transforms these contents into structured knowledge that is stored inside an ontology. The purpose of the proposed methodology is to lower the effort of system scaffolding creation and to increase the level of interaction with users. The focus is on self-regulated learners while meta-cognitive strategies have to bee defined to adapt and to increase the effectiveness of tutoring actions.

Uncertainty in natural language dialogue is often treated through stochastic models. Some of the authors already presented TutorJ that is an Intelligent Tutoring System, whose interaction with the user is very intensive, and makes use of both dialogic and graphical modality. When managing the interaction, the system needs to cope with uncertainty due to the understanding of the user's needs and wishes. In this paper we present the extended version of TutorJ, focusing on the new features added to its chatbot module. These features allow to merge deterministic and probabilistic reasoning in dialogue management, and in writing the rules of the system's procedural memory.

This paper describes the DynaLearn project, which seeks to address contemporary problems in science education by integrating well established, but currently independent technological developments, and utilize the added value that emerges. Specifically, diagrammatic representations are used for learners to articulate, analyse and communicate ideas, and thereby construct their conceptual knowledge. Ontology mapping is used to find and match co-learners working on similar ideas to provide individualised and mutually benefiting learning opportunities. Virtual characters are used to make the interaction engaging and motivating. The development of the workbench is tuned to fit key topics from environmental science curricula, and evaluated and further improved in the context of existing curricula using case studies. Through this approach, the DynaLearn project will deliver an individualised and engaging cognitive tool for acquiring conceptual knowledge that fits the true nature of this expertise.

The report gives an overview about activities on the topic Semantic Web. It has been released as technical report for the project "KTweb -- Connecting Knowledge Technologies Communities" in 2003.

In this paper, we propose a first-order ontology for generalized stratified order structure. We then classify the models of the theory using model-theoretic techniques. An ontology mapping from this ontology to the core theory of Process Specification Language is also discussed.

In this paper, we present a prototype application — the Technology Trend Tracker — to measure the reality of technology trends of interest using information on the Web to inform decisions such as when to develop training, when to invest in expertise, and more. This prototype performs this task by integrating several artificial intelligence technologies in an innovative way. These technologies include rich semantic representations, a natural language understanding module, and a flexible semantic matcher. We use our system to augment Accenture's annual technology vision survey and show how our system performs well on measuring the reality of technology trends from this survey. We also show why our system performs well through an ablation study.

As the volume and interconnectedness of corporate data grows, data quality is becoming a business competency essential to success. Existing methods for managing data quality do not scale up to large volumes of data in a way that is directly manageable by the owner of the data. For the past two years a new breed of data quality products, built on applied AI techniques, are empowering non-technical users. Over 150 businesses are benefiting from these products including NASDAQ, Visa, Experian, Oracle, Fidelity, Bank of America, Volvo, Dell, Sabic, and Dassault Systems. The applied AI techniques described include lightweight ontologies to efficiently find inexact textual matches in large data sets.

This paper introduces the challenge of digital preservation in the   area of engineering design and manufacturing and presents a   methodology to apply knowledge representation and semantic   techniques to develop Digital Engineering Archives.  This work   is part of an ongoing, multi-university, effort to create   Cyber-Infrastructure-Based Engineering Repositories for   Undergraduates (CIBER-U) to support engineering design education.   The technical approach is to use knowledge representation techniques   to create formal models of engineering data elements, workflows and   processes.  With these formal engineering knowledge and processes   can be captured and preserved with some guarantee of long-term   interpretability.  The paper presents examples of how the techniques   can be used to encode specific engineering information     packages and workflows.  These techniques are being integrated   into a semantic Wiki that supports the CIBER-U engineering education   activities across nine universities and involving over 3,500   students since 2006.