Computing Geo-Spatial Motives from Linked Data for Search-driven Applications

The Web of Data puts a vast and ever-increasing amount of information at the disposal of its users. In the era of big data, interpreting and exploiting these information is both a highly active research area and a key issue for users in industry trying to gain a competitive edge. One current problem in industry with many potential application areas is finding a common theme for varying features by generating higher level summaries. We introduce the notion of motives to describe these common themes. Motives can be identified for all sorts of entities such as geo-spatial regions (e.g., \cultural regions") or holidays (e.g., \winter holidays", \activity holidays"). These motives are closer to common language and human conversations than ordinary keywords. Since users prefer formulating their information needs using everyday language, which expresses their understanding of the world, the potential for a strong industrial impact for search applications can be derived. However, capturing the users' often vaguely formulated intentions and matching them to appropriate retrieval operations on the available knowledge bases is a challenging issue. Yet, it is an important step on the way of providing the best possible search experience to users. This paper presents our work in progress on computing motives for geospatial regions. Following a long term agenda, we are evaluating the requirements for identifying such motives in large data sets. At this point, we can show that out-of-the-box machine learning methods can be used on Linked Data to train a model for computation of geo-spatial motives with good accuracy