The discovery of knowledge in databases is currently a very active research area. Many discovery systems adapt traditional attribute{based learners for the extraction of patterns. They have been, however, restricted by their inability to incorporate background knowledge into the learning process. In this paper an attribute{based learning algorithm, called SIDEC, which can incorporate a restrict...

It is well-known that the input-output behaviour of a neural network can be recast in terms of a set of propositional rules, and under certain weak preconditions this is also always possible with positive (or definite) rules. Furthermore, in this case there is in fact a unique minimal (technically, reduced) set of such rules which perfectly captures the inputoutput mapping. In this paper, we in...

The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our Subdue substructure discovery system based on the minimum description length principle. The Subdue system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previo...

Keyphrase is an efficient representation of the main idea of documents. While background knowledge can provide valuable information about documents, they are rarely incorporated in keyphrase extraction methods. In this paper, we propose WikiRank, an unsupervised method for keyphrase extraction based on the background knowledge from Wikipedia. Firstly, we construct a semantic graph for the docum...

In this paper we propose an ontology matching paradigm based on the idea of harvesting the Semantic Web, i.e., automatically finding and exploring multiple and heterogeneous online knowledge sources to derive mappings. We adopt an experimental approach in the context of matching two real life, large-scale ontologies to investigate the potential of this paradigm, its limitations, and its relatio...

Accessing the wealth of structured data available on the Data Web is still a key challenge for lay users. Keyword search is the most convenient way for users to access information (e.g., from data repositories). In this paper we introduce a novel approach for determining the correct resources for user-supplied keyword queries based on a hidden Markov model. In our approach the user-supplied que...

Inductive Logic Programming (ILP) systems construct explanations for data in terms of domain-speciic background information. How does the quality of this information aaect the performance of an ILP system? Results from experiments concerned with learning simple programs for list processing suggest that performance is sensitive to the type and amount of background knowledge provided. In particul...

Using ontology as a background knowledge in ontology matching is being actively investigated. Recently the idea attracted attention because of the growing number of available ontologies, which in turn opens up new opportunities, and reduces the problem of finding candidate background knowledge. Particularly interesting is the approach of using multiple ontologies as background knowledge, which ...

We build the knowledge representation machinery for answering complex questions in poorly formalized and logically complex domains. Answers are annotated with deductively linked logical expressions (semantic skeletons), which are to be matched with formal representations for questions. Technique of semantic skeletons is a further development of our semantic headerbased approach to question answ...

Green home rating has emerged as an important agenda to practice the principles of sustainability. In Malaysia, the establishment of the ‘Green Building Index – Residential New Construction’ (GBI-RNC) has brought this agenda closer to the stakeholders of the local green building industry. GBI-RNC focuses on the evaluation of the environmental impacts posed by houses rather than assessing the Tr...