Geographical Information Retrieval (GIR) is concerned with the retrieval of documents involving the interpretation of geographical knowledge by means of topological, directional, and proximity information. Documents typically contain descriptions of events or static situations that are temporally and/or spatially restricted. For example, consider the phrases “the industrial development after World War II” and “the social security system outside of Scandinavia”. Furthermore, many documents contain ambiguous geographic references. There are, for example, more than 30 cities named “Zell” in Germany, and any occurrence of this name in a document can have a different meaning and should be disambiguated from context. In addition, a toponym (a name for a geographic entity) can be referred to with names in different languages or local dialects, with historical names, etc., which will require normalization or translation to enable successful document retrieval. The latter problems are similar to the problems of polysemy and synonymy in traditional information retrieval (IR).

GeoCLEF is a task of the Cross Language Evaluation Forum offering scientific challenges in the interpretation of geographical information retrieval queries. The queries are targeted at existing CLEF document collections of news stories that include a variety of topics and geographical regions; for German, these are the news articles of “Der Spiegel”, “Frankfurter Rundschau”, and “Schweizer Depeschenagentur” from 1994 and 1995. The goal of the GeoCLEF task is to find all and only documents that are relevant to a given topic.

GeoCLEF topics include a short description (title and description), a longer narrative, and location elements consisting of a combination of thematic concepts (DE-concept ), spatial relations (DE-spatialrelation), and place names (DE-location ). For example, the short description of the first topic includes the natural language title “Haifischangriffe vor Australien und Kalifornien”/‘Shark attacks off Australia and California’.

A traditional approach to GIR involves at least the following processing steps to identify geographical entities (Jones et al., 2002) : • named entity recognition (NER), including the detection of geographic names (tagging with named entities, including toponyms); • collecting and integrating information from the contexts of named entities; • disambiguation of named entities; and • grounding the entities (i.e. connecting them to the model) and interpreting coordinates. After identifying toponyms in queries and documents, coordinates can be assigned to them. In GIR, assigning a relevance score to a document for a given query typically involves calculating the distance between geographical entities in the query and the document and mapping it to a score.

One of the major problems for GIR is the disambiguation of toponyms from semantic context and identifying spatial ambiguity (e.g. ‘California’ in ‘Mexico’ and/or ‘California’ in the ‘United States of America’). Table 1 shows a comparison of ambiguity in GermaNet1 (Kunze and Wagner, 2001) and the GEOnet Names Server data (GNS, described in Section 2.2). As shown in the table, synonymy seems to be a lesser problem for German geographic names (1.08 synonyms per synset vs. 1.45 synonyms per synset in a lexical-semantic net for German), while the role of polysemy (word senses and disambiguation) becomes more important for GIR (1.28 vs. 1.16).

Problems that are less often identified and less investigated in research for GIR are: • Toponyms in different languages. The translation of toponyms plays an important role even for monolingual retrieval when different and external information resources are integrated. In gazetteers, mostly English naming conventions are used. • Name variants. The same geographic object can be referenced by endonymic names, exonymic names, and historical names. An endonym is a local name for a geographic entity, for example, “Wien”, “Ko¨ln”, and “Milano”. An exonym is a place name in a certain language for a geographic object that lies outside the region where this language has an official status; for example, “Vienna” is the English exonym for “Wien”, “Cologne” is the English exonym for “Ko¨ln”, and “Mailand” is the German exonym for “Milano”. Examples of historical names or traditional names are “New Amsterdam” for “New York” and “Colonia Claudia Ara Agrippinensium” or “Co¨llen” for “Ko¨ln”.

For GIR, name variants should be conflated. • Composite names. Composite names or complex named entities consist of two or more words.

Frequently, appositions are considered to be a part of a name. For example, there is no need for the translation of the word “mount” in “Mount Cook” (the German translation is “Mount Cook”), but “Insel” is typically translated in the expression “Insel Sylt”/“island of Sylt”. For named entity recognition, certain rules have to be established how composite names are normalized. In some composite names, two or more toponyms (geographic names) are employed in reference to a single entity, for example, “Haren (Ems)”, “Frankfurt/Oder”, or “Freiburg im Breisgau”. While additional toponyms in a context allow for a better disambiguation, such composite names require a normalization, too. • Semantic relations between toponyms and related concepts. In named entity recognition and GIR, semantic relations between toponyms and related concepts are often ignored. Concepts related to a toponym such as the language, inhabitants of a place, properties (adjectives), or phrases (“former Yugoslavia”) are not considered in geographic tagging. For example, the toponym “Scotland” can be inferred for occurrences of “Scottish”, “Scotsman”, or “Scottish districts”. 1http://www.sfs.nphil.uni-tuebingen.de/lsd/Intro.html

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3. adjusting parameters for network variation scores and limits for generating query network variants. These three areas are explained in more detail in the following paragraphs.

The base system, InSicht, matches semantic networks derived from a query parse (topic title or topic description7) to document sentence networks one by one (whereas sentence boundaries are ignored in traditional IR). In GIR (as in IR), this approach yields high precision, but low recall because often the information contained in a query is distributed across several sentences in a document. To adjust the 6http://www.indexdata.dk/zebra 7GIR-InSicht combines the results for a query from the title field and for a query from the description field. All other topic fields are ignored. The information from the attributes DE-concept , DE-spatialrelation , and DE-location were equally well derived from the parse result of the title or description attribute. matching approach to such situations, the query network is split if certain graph topologies are encountered. The resulting query network parts are viewed as conjunctively connected. The query network can be split at the following semantic relations: CIRC, CTXT, LOC, TEMP. For example, the LOC edge in Figure 1 can be deleted leading to two separate semantic networks. One corresponds to “Bericht von Amnesty International u¨ber Menschenrechte” (‘Amnesty International reports on human rights’) and the other to “Lateinamerika” (‘Latin America’). The greatest positive impact for GeoCLEF comes from splitting at LOC edges.

The geographical knowledge base described in Section 3 is scanned by GIR-InSicht; relations that contain names that do not occur in the document parse results (i.e. the semantic networks of document sentences) are ignored. For simplicity, meronymy edges (PARS) are treated like hyponymy edges so that GIR-InSicht can use all part-whole relations for concept variations (see Hartrumpf (2005) ) in query networks. Without the geographical knowledge base, recall is much lower.

Some InSicht parameters had to be adjusted in order to yield more results in GIR-InSicht and/or to keep answer time and RAM consumption acceptable even when working with large background knowledge bases like the one mentioned in Section 2.3. The final steps of InSicht that come after a semantic network match has been found (answer generation and answer selection) can be skipped. Some minor adjustments further reduce run time without losing relevant documents. For example, an apposition for a named entity (like “Hauptstadt” (‘capital’) for “Sarajevo”/“Sarajewo”) leads to a new query network variant only if this combination occurs at least 2 times in the document collection.

We evaluated about ten different setups of GIR-InSicht on the GeoCLEF 2005 topics. The setups used different extension combinations from the extensions described above and other extensions, like coreference resolution for documents. The performance differences were often marginal. In some cases, this indicates that a specific extension is irrelevant for GIR; in other cases, the number of topics (23 topics have relevant documents) and the number of relevant documents might be too small to draw any conclusions. However, one can see considerable performance improvements for some extensions, e.g. splitting query networks at LOC edges. Larger evaluations are needed to gain more insights about which development directions are most promising for the semantic network matching approach to GIR. 5