Ontology mapping by concept similarity

This paper presents an approach to the problem of mapping ontologies. The motivation for the research stems from the Diogene Project which is developing a web training environment for ICT professionals. The system includes high quality training material from registered content providers, and free web material will also be made available through the project's "Web Discovery" component. This involves using web search engines to locate relevant material, and mapping the ontology at the core of the Diogene system to other ontologies that exist on the Semantic Web. The project's approach to ontology mapping is presented, and an evaluation of this method is described

Mapping Australia’s economy: cities as engines of prosperity

This report maps the Australian economy by the location of economic activity, defined as the dollar value of goods and services produced by workers within a particular area. Overview Eighty per cent of the value of all goods and services produced in Australia is generated on just 0.2 per cent of the nation’s land mass – mostly in cities. Today, cities are the engines of economic prosperity. But the concentration of highly productive activity in city centres presents challenges for policymakers. Too many workers live too far away to fulfil our cities’ economic potential. This report maps the Australian economy by the location of economic activity, defined as the dollar value of goods and services produced by workers within a particular area. It finds that economic activity is concentrated most heavily in the central business districts (CBDs) and inner areas of large cities. The CBDs of Sydney and Melbourne – just 7.1 square kilometres in total – generated $118 billion in 2011-12, almost 10 per cent of all economic activity in Australia, and triple the contribution of the entire agriculture sector. The intense economic contribution of CBDs occurs partly because of the concentration of jobs in these areas. But CBD businesses are also much more productive on average than those in other areas. Inner city areas and secondary commercial hubs, such as those around large cities’ airports, also tend to be more productive than other locations. For example, in 2011-12 the Sydney CBD produced$64.1 billion worth of goods and services: about $100 for every hour worked there. Employing only 13 per cent of Sydney’s workforce, this small area generates almost a quarter of the value of the Greater Sydney economy. Parramatta, often said to be Sydney’s second CBD, generated only$68 for each hour worked, and its total of $6.8 billion was about a tenth of the value generated in the CBD. There is a reason intense economic activity is concentrating in CBDs and inner suburbs. Many businesses in these areas provide highly knowledge-intensive and specialised services such as funds management, insurance, design, engineering and international education. These businesses depend on highly skilled workers, and locating in the heart of large cities gives them access to the largest possible pools of them. Proximity to suppliers, customers and partners also helps businesses to work efficiently, to generate opportunities and to come up with new ideas and ways of working. Knowledge-intensive activity is present in all sectors, including manufacturing and mining. Perth’s CBD is home to more than a third of Western Australian mining jobs, including accountants, administrators, geologists and specialist engineers. In the early 20th century one in three workers were employed in primary industry and almost half of the population lived on rural properties or in towns of less than 3,000 people. By 1960 manufacturing had grown to make up almost 30 per cent of GDP and employ one in four Australians, with a big presence in suburban areas. But today the small areas that generate most value are often a very long commute from the fast-growing outer suburbs in which many Australians live. If the prosperity that comes from knowledge-intensive activity is to be widely shared, governments need to enable more people to live closer to these areas, and to improve road and public transport networks so that they better connect employers and workers

FunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation

Data has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions – specified with function-based mapping languages like FunUL and RML+FnO – can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RML-compliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation

FunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation

Data has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions -- specified with function-based mapping languages like FunUL and RML+FnO -- can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RML-compliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation

Challenging Ubiquitous Inverted Files

Stand-alone ranking systems based on highly optimized inverted file structures are generally considered ‘the’ solution for building search engines. Observing various developments in software and hardware, we argue however that IR research faces a complex engineering problem in the quest for more flexible yet efficient retrieval systems. We propose to base the development of retrieval systems on ‘the database approach’: mapping high-level declarative specifications of the retrieval process into efficient query plans. We present the Mirror DBMS as a prototype implementation of a retrieval system based on this approach

A real time neural net estimator of fatigue life

A neural net architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the Intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopump blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines. This neural net estimator of fatigue life is seen as the enabling technology to achieve component life prognosis, and therefore would be an important part of life extending control for reusable rocket engines