David Ratcliffe

XML Schema Representation and Reasoning: A Description Logic Method

XML plays a key role in service-oriented computing (SOC). We envisage an environment where knowledge workers are developing semantic correspondences from XML schemas to a target ontology. We would like to use DL reasoning services within this environment to assist the knowledge worker in the following three tasks: establishing the correctness of structural correspondences; the implications of correspondences; and finally, search and selection of schemas with semantic and structural constraints. A knowledge representation method for XML schemas using a description logic language is presented. With this representation method, it is now possible to use the underlying DL reasoning services to achieve our three tasks: correspondence correctness checking through DL model validation; correspondence implication through DL model completion; and finally, semantic and structural search and selection through DL model query.

A semantic approach to data translation

We propose an ontology-mediated approach for environmental data translation.We outline the principles underlying the design of a mediating ontology, and show the development of such an ontology.We propose a declarative formalism for representing spreadsheet-to-ontology mappings, and give an algorithm for the mapping evaluation.We propose a declarative formalism for representing ontology-to-XML mappings, and give an algorithm for the mapping evaluation.We have developed an ontology-mediated spreadsheet-to-XML translation tool, and showed its effectiveness with real environmental observations data. To facilitate the exchange of environmental observations, efforts have been made to develop standardised markup languages for describing and transmitting data from multiple sources. Along with this is often a need to translate data from different formats or vocabularies to these languages. In this paper, we focus on the problem of translating data encoded in spreadsheets to an XML-based standardised exchange language. We describe the issues with data that have to be resolved. We present a solution that relies on an ontology capturing semantic gaps between data and the target language. We show how to develop such an ontology and use it to mediate translation through a real scenario where water resources data have to be translated to a standard data transfer format. In particular, we provide declarative mapping formalisms for representing relationships between spreadsheets, ontologies, and XML schemas, and give algorithms for processing mappings. We have implemented our approach in AdHoc, an ontology-mediated spreadsheet-to-XML translation tool, and showed its effectiveness with real environmental observations data.

Closed-World Concept Induction for Learning in OWL Knowledge Bases

We present a general-purpose method for inducing OWL class descriptions over data and knowledge captured with RDF and OWL in a closed-world way. We combine our approach with a top-down refinement-based search with Description Logic (DL) expressions which incorporates OWL background knowledge. Our methods are designed for speed and scalability to support analysis tasks like data mining over large knowledge-rich data sets. We compare our methods to a state-of-the-art DL learning tool with respect to a large benchmark problem to demonstrate the speed and effectiveness of our approach.

Refinement-Based OWL Class Induction with Convex Measures

Beam-search may be used to iteratively explore and evaluate refinements of candidate hypotheses expressed in logical formalisms such as description logic. In this paper, we analyse heuristics for beam search methods over OWL classes and present a novel search algorithm, OWL-Miner which leverages the properties of convex measure functions to deliver an improved memory-bounded beam search. We present performance results on the mutagenesis benchmark problem and demonstrate superior performance relative to another state-of-the-art implementation, and present 10-fold cross-validated accuracy results which are comparable with those from a variety of other methods. Our improvements to the space and time-based efficiency of refinement-based learning algorithms are significant for expanding the size of learning problems that can be feasibly addressed by refinement learning and the quality of solutions that can be found with limited resources.

The Emergency Response Intelligence Capability Tool

The Emergency Response Intelligence Capability (ERIC) tool, http://eric.csiro.au, automatically gathers data about emergency events from authoritative web sources, harmonises the information content and presents it on an interactive map. All data is recorded in a database which allows the changing status of emergency events to be identified and provides an archive for historical review.

Semantic water data translation: a knowledge-driven approach

In order for the Bureau of Meteorology (BOM), Australia, to build and maintain an integrated national water information system, over 240 organisations are required to provide their data to BOM. These organisations use a wide range of systems and data formats. To ensure robust and reliable data delivery, BOM has established Water Data Transfer Format (WDTF) as a standard format for data transfer. Meanwhile, the Water Regulations 2008 were enacted to specify the water information required from organisations. This paper analyses semantic gaps between data from organisations, WDTF, and the Regulations requirements, and proposes a knowledge-driven approach in which these gaps are captured in a way that facilitates data translation and validation. Throughout the paper, real data examples are used to illustrate the details of the approach and its feasibility.

Declarative Data Exchange: Spreadsheets to RDF/OWL

Integrating and accessing data from the many existing formats is a key challenge for the Semantic Web. In particular, representing and understanding non-RDF/OWL data in terms of ontologies is important for reasoning about this data and its relationships to other data, and for extracting or accessing the data itself. This paper presents a declarative method for mapping and translating spreadsheet data to RDF/OWL. First, a declarative mapping language is presented. Then, for this syntax, the paper presents a declarative semantics in terms of matrix operations as abstractions over spreadsheets and the relations therein. The language supports arbitrary search and indexing functions over spreadsheet data and allows the generation of complex associations and aggregations in the RDF/OWL output. Imperative and functional implementations of the mapping and extraction technology are presented.