Geographical information retrieval with ontologies of place

Geographical context is required of many information retrieval tasks in which the target of the search may be documents, images or records which are referenced to geographical space only by means of place names. Often there may be an imprecise match between the query name and the names associated with candidate sources of information. There is a need therefore for geographical information retrieval facilities that can rank the relevance of candidate information with respect to geographical closeness of place as well as semantic closeness with respect to the information of interest. Here we present an ontology of place that combines limited coordinate data with semantic and qualitative spatial relationships between places. This parsimonious model of geographical place supports maintenance of knowledge of place names that relate to extensive regions of the Earth at multiple levels of granularity. The ontology has been implemented with a semantic modelling system linking non-spatial conceptual hierarchies with the place ontology. An hierarchical spatial distance measure is combined with Euclidean distance between place centroids to create a hybrid spatial distance measure. This is integrated with thematic distance, based on classification semantics, to create an integrated semantic closeness measure that can be used for a relevance ranking of retrieved objects

Trust beyond reputation: A computational trust model based on stereotypes

Models of computational trust support users in taking decisions. They are commonly used to guide users' judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger's actions in absence of the knowledge of such behavioral history, we often use our "instinct"- essentially stereotypes developed from our past interactions with other "similar" persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger's profile. Since stereotypes are formed locally, recommendations stem from the trustor's own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information

Network formation with closeness incentives

We study network formation in a strategic setting where every agent strives for short paths to the other agents. The main parameter of our model is the marginal rate of substitution between network benefits and linking costs. We provide boundaries of stable networks for increasing and decreasing marginal returns. The formulated model stands in strong relation to the famous connections model (Jackson and Wolinsky ‘96): we show that for certain parameter values both models induce the same network structures.

Measuring and Explaining Localisation: Evidence from two British Sectors

The degree of localisation of manufacturing, defined as the excess geographic concentration remaining after correcting for both sectorial concentration and the agglomeration of overall economic activity, has recently gained new techniques of measurement. These techniques are illustrated and theoretically discussed. The paper then investigates the sectorial scale of localisation, using evidence from two British sectors, SIC 244 (pharmaceutical) and 334 (optical and photographic), and respective sub-sectors. Applying the measures, it is evidenced that the individual sub-sectors are very differently localized both in extent and in location, even within the same sector. In addition to this, with survey data the paper shows that localisation is due to different economic explanations in different sub-sectors. This is a proof that the economic factors behind localisation are in this case at 5-digit level, making economically not meaningful the measurement of localisation at a different scale. The study implies that identifying localisation remains a delicate process, since the right sectorial scale has to be detected case by case, the use of more than one technique usually gives additional insights and, finally, the survey confirms that, in field studies, a mix of diferent theoretical models is generally needed to explain the observed patterns.