Generating Semantic Aspects for Queries

Abstract

Ambiguous information needs expressed in a limited number of keywords often result in long-winded query sessions and many query reformulations. In this work, we tackle ambiguous queries by providing automatically generated semantic aspects that can guide users to satisfying results regarding their information needs. To generate semantic aspects, we use semantic annotations available in the documents and leverage models representing the semantic relationships between annotations of the same type. The aspects in turn provide us a foundation for representing text in a completely structured manner, thereby allowing for a semantically-motivated organization of search results. We evaluate our approach on a testbed of over 5,000 aspects on Web scale document collections amounting to more than 450 million documents, with temporal, geographic, and named entity annotations as example dimensions. Our experimental results show that our general approach is Web-scale ready and finds relevant aspects for highly ambiguous queries.

Extraction of spatio‐temporal data about historical events from text documents

“…They also consider uncertainty in temporal expressions and geographic expressions. The same approach has also been used to mine historical documents (Gupta, Strötgen, & Berberich, ).…”