Query Understanding via Entity Attribute Identification
This work addresses query understanding for semantic search systems, but it is incremental as it builds on existing knowledge base integration approaches.
The paper tackled the problem of understanding search queries by identifying related entity attributes from a knowledge base, proposing two methods that showed significant improvements over baseline methods.
Understanding searchers' queries is an essential component of semantic search systems. In many cases, search queries involve specific attributes of an entity in a knowledge base (KB), which can be further used to find query answers. In this study, we aim to move forward the understanding of queries by identifying their related entity attributes from a knowledge base. To this end, we introduce the task of entity attribute identification and propose two methods to address it: (i) a model based on Markov Random Field, and (ii) a learning to rank model. We develop a human annotated test collection and show that our proposed methods can bring significant improvements over the baseline methods.