Ontologies are becoming increasingly important because they provide the critical semantic foundation for many rapidly expanding technologies such as software agents, e-commerce and knowledge management (McGuinness, 2002). The Unified Modelling Language (UML)1 has been widely adopted by the software engineering community and its scope is broadening to include more diverse modelling tasks. This paper discusses the recent convergence of UML and ontologies and suggests some possible future directions.

There is rapidly growing momentum for web enabled agents that reason about and dynamically integrate the appropriate knowledge and services at runtime. The dynamic integration of knowledge and services depends on the existence of explicit declarative semantic models (ontologies). We have been building tools for ontology development based on the Unified Modeling Language (UML). This allows the many mature UML tools, models and expertise to be applied to knowledge representation systems, not only for visualizing complex ontologies but also for managing the ontology development process. UML has many features, such as profiles, global modularity and extension mechanisms that are not generally available in most ontology languages. However, ontology languages have some features that UML does not support. Our paper identifies the similarities and differences (with examples) between UML and the ontology languages RDF and DAML+OIL. To reconcile these differences, we propose a modification to the UML metamodel to address some of the most problematic differences. One of these is the ontological concept variously called a property, relation or predicate. This notion corresponds to the UML concepts of association and attribute. In ontology languages properties are first-class modeling elements, but UML associations and attributes are not firstclass. Our proposal is backward-compatible with existing UML models while enhancing its viability for ontology modeling. While we have focused on RDF and DAML+OIL in our research and development activities, the same issues apply to many of the knowledge representation languages. This is especially the case for semantic network and concept graph approaches to knowledge representations.

Abstract. There is rapidly growing momentum for web enabled agents that reason about and dynamically integrate the appropriate knowledge and services at run-time. The World Wide Web Consortium and the DARPA Agent Markup Language (DAML) program have been actively involved in furthering this trend. The dynamic integration of knowledge and services depends on the existence of explicit declarative semantic models (ontologies). DAML is an emerging language for specifying machine-readable ontologies on the web. DAML was designed to support tractable reasoning.We have been developing tools for developing ontologies in the Unified Modeling Language (UML) and generating DAML. This allows the many mature UML tools, models and expertise to be applied to knowledge representation systems, not only for visualizing complex ontologies but also for managing the ontology development process. Furthermore, UML has many features, such as profiles, global modularity and extension mechanisms that have yet to be considered in DAML.Our paper identifies the similarities and differences (with examples) between UML and DAML. To reconcile these differences, we propose a modest extension to the UML infrastructure for one of the most problematic differences. This is the DAML concept of property which is a first-class modeling element in DAML, while UML associations are not. For example, a DAML property can have more than one domain class. Our proposal is backward-compatible with existing UML models while enhancing its viability for ontology modeling.While we have focused on DAML in our research and development activities, the same issues apply to many of the knowledge representation languages. This is especially the case for semantic network and concept graph approaches to knowledge representations.

Ensuring that ontologies are consistent is an important part of ontology development and testing. This is especially important when autonomous software agents are to use ontologies in their reasoning. Reasoning with inconsistent ontologies may lead to erroneous conclusions. In this paper we introduce the ConsVISor tool for consistency checking of ontologies. This tool is a consistency checker for formal ontologies, including both traditional data modeling languages and the more recent ontology languages. ConsVISor checks consistency by verifying axioms. ConsVISor is part of the UBOT toolkit that uses a variety of techniques such as theorem proving and logic programming. Some examples of the use of these tools are given.

We propose an image-classification method to predict the perceivedrelevance of text documents from eye-movements. An eye-tracking study was conducted where participants read short news articles, and rated them as relevant or irrelevant for answering a trigger question. We encode participants' eye-movement scanpaths as images, and then train a convolutional neural network classifier using these scanpath images. The trained classifier is used to predict participants' perceived-relevance of news articles from the corresponding scanpath images. This method is content-independent, as the classifier does not require knowledge of the screen-content, or the user's information-task. Even with little data, the image classifier can predict perceived-relevance with up to 80% accuracy. When compared to similar eye-tracking studies from the literature, this scanpath image classification method outperforms previously reported metrics by appreciable margins. We also attempt to interpret how the image classifier differentiates between scanpaths on relevant and irrelevant documents.