05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Information extraction for the geospatial domain(2014) Blessing, André; Schütze, Hinrich (Prof. Dr.)Geospatial knowledge is increasingly becoming an essential part of software applications. This is primarily due to the importance of mobile devices and of location-based queries on the World Wide Web. Context models are one way to disseminate geospatial data in a digital and machine-readable representation. One key challenge involves acquiring and updating such data, since physical sensors cannot be used to collect such data on a large scale. Doing the required manual work is very time-consuming and expensive. Alternatively, a lot of geospatial data already exists in a textual representation, and this can instead be used. The question is how to extract such information from texts in order to integrate it into context models. In this thesis we tackle this issue and provide new approaches which were implemented as prototypes and evaluated. The first challenge in analyzing geospatial data in texts is identifying geospatial entities, which are also called toponyms. Such an approach can be divided into several steps. The first step marks possible candidates in the text, which is called spotting. Gazetteers are the key component for that task but they have to be augmented by linguistically motivated methods to enable the spotting of inflected names. A second step is needed, since the spotting process cannot resolve ambiguous entities. For instance, London can be a city or a surname; we call this a geo/non-geo ambiguity. There are also geo/geo ambiguities, e.g. Fulda (city) vs. Fulda (river). For our experiments, we prepared a new dataset that contains mentions of street names. Each mention was manually annotated and one part of the data was used to develop methods for toponym recognition and the remaining part was used to evaluate performance. The results showed that machine learning based classifiers perform well for resolving the geo/non-geo ambiguity. To tackle the geo/geo ambiguity we have to ground toponyms by finding the corresponding real world objects. In this work we present such approaches in a formal description and in a (partial) prototypical implementation, e.g., the recognition of vernacular named regions (like old town or financial district). The lack of annotated data in the geospatial domain is a major obstacle for the development of supervised extraction approaches. The second part of this thesis thus focuses on approaches that enable the automatic annotation of textual data, which we call unstructured data, by using machine-readable data from a knowledge base, which we call structured data. This approach is an instance of distant supervision (DS). It is well established for the English language. We apply this approach to German data which is more challenging than English, since German provides a richer morphology and its word order is more variable than that of English. Our approach takes these requirements into account. We evaluated our approach in several scenarios, which involve of the extraction of relations between geospatial entities (e.g., between cities and their suburbs or between towns and their corresponding rivers). For our evaluation, we developed two different relation extraction systems. One is a DS-based system, which uses the automatically annotated training set and the other one is a standard system, which uses the manually annotated training set. The comparison of the systems showed that both reach the same quality, which is evidence that DS can replace manual annotations. One drawback of current DS approaches is that both structured data and unstructured data must be represented in the same language. However, most knowledge bases are represented in the English language, which prevents the development of DS for other languages. We developed an approach called Crosslingual Distant Supervision (CDS) that eliminates this restriction. Our experiments showed that structured data from a German knowledge base can successfully be transferred by CDS into other languages (English, French, and Chinese).