In this paper, we provide an overview of the NTCIR IMine task, which is a core task of NTCIR-11 and also a succeeding work of

The problem of learning user search intents has attracted intensive attention from both industry and academia. How-ever, state-of-the-art intent learning algorithms suffer from different drawbacks when only using a single type of da-ta source. For example, query text has difficulty in distin-guishing ambiguous queries; search log is bias to the order of search results and users ’ noisy click behaviors. In this work, we for the first time leverage three types of objects, namely queries, web pages and Wikipedia concepts collaboratively for learning generic search intents and construct a hetero-geneous graph to represent multiple types of relationships between them. A novel unsupervised method called hetero-geneous graph-based soft-clustering is developed to derive an intent indicator for each object based on the constructed het-erogeneous graph. With the proposed co-clustering method, one can enhance the quality of intent understanding by tak-ing advantage of different types of data, which complement each other, and make the implicit intents easier to interpret with explicit knowledge from Wikipedia concepts. Experi-ments on two real-world datasets demonstrate the power of the proposed method where it achieves a 9.25 % improve-ment in terms of NDCG on search ranking task and a 4.67% enhancement in terms of Rand index on object co-clustering task compared to the best state-of-the-art method.

Recently, users who search on the web are targeting to more complex tasks due to the explosive growth of web usage. To accom-plish a complex task, users may need to ob-tain information of various entities. For ex-ample, a user who wants to travel to Beijing, should book a flight, reserve a hotel room, and survey a Beijing map. A complex task thus needs to submit several queries in order to seeking each of entities. Understanding complex tasks can allow a search engine to suggest related entities and help users explic-itly assign their ongoing tasks. 1

Knowledge graphs are recently used for enriching query representations in an entity-aware way for the rich facts or-ganized around entities in it. How-ever, few of the methods pay attention to non-entity words and clicked websites in queries, which also help conveying user intent. In this paper, we tackle the prob-lem of intent understanding with innova-tively representing entity words, refiners and clicked urls as intent topics in a uni-fied knowledge graph based framework, in a way to exploit and expand knowl-edge graph which we call ‘tailor’. We collaboratively exploit global knowledge in knowledge graphs and local contexts in query log to initialize intent representa-tion, then propagate the enriched features in a graph consisting of intent topics us-ing an unsupervised algorithm. The ex-periments prove intent topics with knowl-edge graph enriched features significantly enhance intent understanding. 1