University of Hagen at GeoCLEF 2005: Using Semantic Networks for Interpreting Geographical Queries

The IICS group at the University of Hagen employs multilayered extended semantic networks for the representation of background knowledge, queries, and documents for geographic information retrieval (GIR). This paper describes our work for the participation at the GeoCLEF task of the CLEF 2005 evaluation campaign (Cross Language Evaluation Forum). In our approach, geographical concepts from the query network are expanded with concepts which are semantically connected via topological, directional, and proximity relations. We started with an existing geographical knowledge base represented as a large semantic network and expanded it with concepts automatically extracted from the GEOnet Names Server (GNS). Furthermore, we created concept hypotheses by adding a prefix with regular semantics, for example “Süd”/‘South’ and “Zentral”/‘Central’, and integrated the corresponding semantic relations into our geographical knowledge base. Several experiments for GIR on German documents have been performed: a baseline corresponding to a traditional information retrieval approach; a variant expanding thematic, temporal, and geographic descriptors from the semantic network representation of the query; and an adaptation of a question answering (QA) algorithm based on semantic networks. The second experiment is based on a representation of the natural language description of a topic as a semantic network, which is achieved by a deep linguistic analysis. The semantic network is transformed into an intermediate representation of a database query explicitly representing thematic, temporal, and local restrictions. This experiment showed the best performance with respect to mean average precision (MAP): 10.53 percent using the topic title and description or 10.22 percent using title, description, and additional location information. The third experiment, adapting a QA algorithm, uses a modified version of the QA system InSicht. The system matches deep semantic representations (semantic networks) of queries or their equivalent or similar variants to semantic networks for document sentences. Since this approach was too much oriented towards precision, partitioning a query network was allowed when certain graph topologies exist. For example, local specifications can be split off, so that they can be matched in other sentences of the document under investigation. The geographical knowledge base developed for the other experiments improved the results of this approach, too. To keep answer time low and main memory consumption acceptable, some parameters of the InSicht system had to be adjusted. In conclusion, we provide a basic architecture for further experiments in geographic information retrieval based on semantic networks. Future research aims at improving the named entity recognition for toponyms, connecting semantic networks and databases, expanding our geographical knowledge base, and investigating the role of semantic relations in geographic queries.