Ontology Aligning is an answer to the problem of handling heterogenous information on different domains. After application of some measures, one reaches a set of similarity values. The final goal is to extract map-pings. Our contribution is to introduce a new genetic algorithm (GA) based extraction method. The GA, employs a structured based weighting model,… (More)
Ontology Matching is a process for selection of a good alignment across entities of two (or more) Ontologies. This can be viewed as a two phase process of: 1) applying a similarity measure to find the correspondence of each pair of entities from two ontologies, and 2) Extraction of an optimal or near optimal mapping. This paper is focused on the second… (More)
Ontology Matching (OM) which targets finding a set of alignments across two ontologies, is a key enabler for the success of Semantic Web. In this paper, we introduce a new perspective on this problem. By interpreting ontologies as Typed Graphs embedded in a Metric Space, coincidence of the structures of the two ontologies is formulated. Having such a… (More)
Motivation What's available? We provide reusable components. ⇒ Component-Based Mechanisation You order components and assemble them desirably – if you do mechanisation.
We present a work in progress report on a new programming model that supports declarative, functional style aggregation operations over devices at the edge. This programming model bridges the gap between the two competing approaches for large-scale aggregations, streaming all data back to a central coordinator versus designing an optimized, distributed… (More)
Interactions between internet users are mediated by their devices and the common support infrastructure in data centres. Keeping track of causality amongst actions that take place in this distributed system is key to provide a seamless interaction where effects follow causes. Tracking causality in large scale interactions is difficult due to the cost of… (More)
This paper presents a big-step operational semantics for distributed lazy evaluation. Our semantics is an extension to the famous heap-based semantics of Launchbury for lazy evaluation. The high level of abstraction in our semantics helps us to easily prove different properties that are of interest to task distribution. Most importantly, we give criteria… (More)