So-called ‘reified temporal logics’ were introduced by researchers in Artificial Intelligence (AI) in the early 1980s, and gave rise to a long-running series of debates concerning the proper way to represent states, events, causation, action, and other notions identified as crucial to the knowledge representation needs of AI. These debates never resulted in a definitive resolution of the issues under discussion, and indeed continue to produce aftershocks to the present day; none the less, we are now sufficiently far removed in (...) time from their heyday for it to be a worthwhile exercise to stand back and review them as a connected piece of history. (shrink)

We present a preliminary high-level formal theory, grounded on knowledge representation techniques and foundational ontologies, for the uniform and integrated representation of the different kinds of (quali- tative and quantitative) knowledge involved in the designing process. We discuss the conceptual nature of engineering design by individuating and analyzing the involved notions. These notions are then formally charac- terized by extending the DOLCE foundational ontology. Our ultimate purpose is twofold: (i) to contribute to foundational issues of design; and (ii) to support (...) the development of advanced modelling systems for (qualitative and quantitative) representation of design knowledge. (shrink)

Recently, there has been growing interest in the use of ontology as a fundamental tool for representing domain-specific conceptual models to improve the semantics, accuracy, and relevance of domain users’ query results. Although the amount of data has grown steadily over the past decade, much data shares similar characteristics that can be captured by a foundational ontology. In this paper, we show how queries based on a foundational ontology can be evaluated and their performance measured. We also present a Foundational (...) Patterns benchmark to help select the most efficient triple memory and its layout. We evaluate the foundational benchmark with both generated and real datasets for state-of-the-art triple stores. (shrink)