The Semantics of Query Modification

We present a method that exploits `linked data' to determine semantic relations between consecutive user queries. Our method maps queries onto concepts in linked data and searches the linked data graph for direct or indirect relations between the concepts. By comparing relations between large numbers of user queries, we identify semantic modification patterns. The application of this method to the logs of an image search engine revealed interesting usage patterns, such as that users often search for two entities sharing a property (e.g., two players from the same team). These patterns can be used to generate query suggestions. Results of preliminary experiments show that the patterns enable us to generate suggestions for more queries than a method purely based on search-log statistics.