Pervasive computing is by its nature open and extensible, and must integrate the information from a diverse range of sources. This leads to a problem of information exchange, so sub-systems must agree on shared representations. Ontologies potentially provide a well-founded mechanism for the representation and exchange of such structured information. A number of ontologies have been developed specifically for use in pervasive computing, none of which appears to cover adequately the space of concerns applicable to application designers. We compare and contrast the most popular ontologies, evaluating them against the system challenges generally recognized within the pervasive computing community. We identify a number of deficiencies that must be addressed in order to apply the ontological techniques successfully to next-generation pervasive systems.

In the longstanding debate in political economy about the feasibility of socialism, the Austrian School of Economists have argued that markets are an indispensable means of evaluating goods, hence a prerequisite for productive efficiency. Socialist models for non-market economic calculation have been strongly influenced by the equilibrium model of neoclassical economics. The Austrians contend that these models overlook the essence of the calculation problem by assuming the availability of knowledge that can be acquired only through the market process itself. But the debate in political economy has not yet considered the recent emergence of agent-based systems and their applications to resource allocation problems. Agent-based simulations of market exchange offer a promising approach to fulfilling the dynamic functions of knowledge encapsulation and discovery that the Austrians show to be performed by markets. Further research is needed in order to develop an agent-based approach to the calculation problem, as it is formulated by the Austrians. Given that the macro-level objectives of agent-based systems can be easily engineered, they could even become a desirable alternative to the real markets that the Austrians favour.

The goal of semantic search is to improve on traditional search methods by exploiting the semantic metadata. In this paper, we argue that supporting iterative and exploratory search modes is important to the usability of all search systems. We also identify the types of semantic queries the users need to make, the issues concerning the search environment and the problems that are intrinsic to semantic search in particular. We then review the four modes of user interaction in existing semantic search systems, namely keyword-based, form-based, view-based and natural language-based systems. Future development should focus on multimodal search systems, which exploit the advantages of more than one mode of interaction, and on developing the search systems that can search heterogeneous semantic metadata on the open semantic Web.

The paper describes the methodology used to develop a construction domain ontology, taking into account the wealth of existing semantic resources in the sector ranging from dictionaries to thesauri. Given the characteristics and settings of the construction industry, a modular, architecture-centric approach was adopted to structure and develop the ontology. The paper argues that taxonomies provide an ideal backbone for any ontology project. Therefore, a construction industry standard taxonomy was used to provide the seeds of the ontology, enriched and expanded with additional concepts extracted from large discipline-oriented document bases using information retrieval (IR) techniques.