Place names are often used to describe and to enquire about geographical information. It is common for users to employ vernacular names that have vague spatial extent and which do not correspond to the official and administrative place name terminology recorded within typical gazetteers. There is a need therefore to enrich gazetteers with knowledge of such vague places and hence improve the quality of place name-based information retrieval. Here we describe a method for modelling vague places using knowledge harvested from Web pages. It is found that vague place names are frequently accompanied in text by the names of more precise co-located places that lie within the extent of the target vague place. Density surface modelling of the frequency of co-occurrence of such names provides an effective method of representing the inherent uncertainty of the extent of the vague place while also enabling approximate crisp boundaries to be derived from contours if required. The method is evaluated using both precise and vague places. The use of the resulting approximate boundaries is demonstrated using an experimental geographical search engine.

Abstract. Many web documents refer to specific geographic localities and many people include geographic context in queries to web search engines. Standard web search engines treat the geographical terms in the same way as other terms. This can result in failure to find relevant documents that refer to the place of interest using alternative related names, such as those of included or nearby places. This can be overcome by associating text indexing with spatial indexing methods that exploit geo-tagging procedures to categorise documents with respect to geographic space. We describe three methods for spatio-textual indexing based on multiple spatially indexed text indexes, attaching spatial indexes to the document occurrences of a text index, and merging text index access results with results of access to a spatial index of documents. These schemes are compared experimentally with a conventional text index search engine, using a collection of geo-tagged web documents, and are shown to be able to compete in speed and storage performance with pure text indexing.

Much of the information stored on the web contains geographical context, but current search engines treat such context in the same way as all other content. In this paper the design, implementation and evaluation of a spatially-aware search engine are described which is capable of handling queries in the form of the triplet of . The process of identifying geographic references in documents and assigning appropriate footprints to documents, to be stored together with document terms in an appropriate indexing structure allowing real-time search is described. Methods allowing users to query and explore results which have been relevance ranked in terms of both thematic and spatial relevance have been implanted and a usability study indicates that users are happy with the range of spatial relationships available and intuitively understand how to use such a search engine. Normalised precision for 38 queries, containing four types of spatial relationships is significantly higher (p < 0.001) for search exploiting spatial information than pure text search.

We conducted a laboratory-based observational study where pairs of people performed search tasks communicating verbally. Examination of the discourse allowed commonly used interactions to be identi ed for Spoken Conversational Search (SCS). We compared the interactions to existing models of search behaviour. We nd that SCS is more complex and interactive than traditional search. is work enhances our understanding of di erent search behaviours and proposes research opportunities for an audio-only search system. Future work will focus on creating models of search behaviour for SCS and evaluating these against actual SCS systems.