Using semantic networks for geographic information retrieval

  • Authors:

  • Johannes Leveling;Sven Hartrumpf;Dirk Veiel

  • Affiliations:

  • Intelligent Information and Communication Systems (IICS), University of Hagen (FernUniversität in Hagen), Hagen, Germany;Intelligent Information and Communication Systems (IICS), University of Hagen (FernUniversität in Hagen), Hagen, Germany;Intelligent Information and Communication Systems (IICS), University of Hagen (FernUniversität in Hagen), Hagen, Germany

  • Venue:
  • CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
  • Year:
  • 2005

Quantified Score

Hi-index 0.01

Visualization

Abstract

This paper describes our work for the participation at the GeoCLEF task of CLEF 2005. We employ multilayered extended semantic networks for the representation of background knowledge, queries, and documents for geographic information retrieval (GIR). In our approach, geographic concepts from the query network are expanded with concepts which are semantically connected via topological, directional, and proximity relations. We started with an existing geographic knowledge base represented as a semantic network and expanded it with concepts automatically extracted from the GEOnet Names Server. 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: 10.53% using the topic title and description. The third experiment, adapting a QA algorithm, uses a modified version of the QA system InSicht. The system matches deep semantic representations of queries or their equivalent or similar variants to semantic networks for document sentences.