Extracting geographical information from various web sources is likely to be important for a variety of applications. One such use for this information is to enable the study of vernacular regions: informal places referred to on a day-to-day basis, but with no official entry in geographical resources, such as gazetteers. Past work in automatically extracting geographical information from the web to support the creation of vernacular regions has tended to focus on larger regions (e.g. The British Midlands and The South of France). In this paper we report the results of preliminary work to investigate the success of using a simple geotagging approach and resources of varying granularity from the Ordnance Survey to extract geographical information from web pages. We find that the data gathered for smaller regions (compared with larger ones) is more fine-grained which has an effect on the type of resource most useful for geo-tagging and its success.

In this paper, we describe a methodology to estimate the geographic coverage of the web without the need for secondary knowledge or complex geo-tagging. This is achieved by randomly selecting toponyms from the Ordnance Survey 50K gazetteer to create search queries and thus gather document counts from various web sources for Great Britain. The same gazetteer is then used to geo-code the results and enable mapping. To validate our approach, and demonstrate the effects of geo/non-geo and geo/geo ambiguity, we mapped the selected toponyms to Geograph, a community project that contains user generated geo-tagged photographs of the UK. Although success varies with resolution, the proposed approach is likely sufficient to be reliably used by applications exploring the geographic coverage of the web for cases where references to settlements are likely to be common. In our case, we applied the method to produce maps of web coverage for a range of sources at a resolution of 30km.

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People often communicate with reference to informally agreed places, such as 'the city centre'. However, views of the spatial extent of such areas may vary and result in imprecise regions. We compare perceptions of Sheffield's City Centre from a street survey (with 61 participants) to spatial extents derived from various web-based sources. Such automated approaches have advantages of speed, cost and repeatability. Our results show that footprints derived from web sources are often in concordance with models derived from more labourintensive methods. There were, however, differences between some of the data sources, with those advertising/selling residential property diverging the most from the street survey data. Agreement between sources was measured by aggregating the web sources to identify locations of consensus.

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Product Lifecycle Management (PLM) systems support industrial organizations in managing their product portfolios and related data across all phases of the product lifecycle. PLM seeks to enhance an organization's ability to manage its product development activities and facilitate collaboration across organizational functions and between organizations. Effective decision-making is vital for the successful management of products over their lifecycle. However, PLM decision-making is an underresearched area. We argue that decision-making theory and group decision support concepts can be brought to bear to enhance PLM decision-making processes. We present and justify a set of six principles to support decision-making in a PLM context. The paper highlights the need to consider and capture decisions as distinct units of PLM knowledge to support product lifecycle management. We derive a generic information flow and a group decision support structure for PLM decision-making that encapsulates the six principles. Three industrial cases are analyzed to illustrate the application and value of the principles in supporting decision-making. The principles enable PLM decisions to be codified, recorded, and reviewed. Decision-making processes can be reused where appropriate. The principles can support future innovations that may affect PLM, such as ontological and semantic reasoning and Artificial Intelligence.

An overview of PhD research into defining imprecise regions (e.g. "The Midlands" of Great Britain) using unstructured data sources found on the web. A literature review is undertaken and future experiments are suggested. Two preliminary experiments, one into geo-tagging, the other into geographic coverage on the web, are described.