This paper tackles the problem of mining subgoals of a given search goal from data. For example, when a searcher wants to travel to London, she may need to accomplish several subtasks such as “book flights, ” “book a hotel, ” “find good restaurants ” and “decide which sightseeing spots to visit. ” As another example, if a searcher wants to lose weight, there may exist several alternative solutions such as “do physical exercise, ” “take diet pills, ” and “control calo-rie intake. ” In this paper, we refer to such subtasks or solutions as subgoals, and propose to utilize sponsored search data for finding subgoals of a given query by means of query clustering. Adver-tisements (ads) reflect advertisers ’ tremendous efforts in trying to match a given query with implicit user needs. Moreover, ads are usually associated with a particular action or transaction. We there-fore hypothesized that they are useful for subgoal mining. To our knowledge, our work is the first to use sponsored search data for this purpose. Our experimental results show that sponsored search data is a good resource for obtaining related queries and for identi-fying subgoals via query clustering. In particular, our method that combines ad impressions from sponsored search data and query co-occurrences from session data outperforms a state-of-the-art query clustering method that relies on document clicks rather than ad im-pressions in terms of purity, NMI, Rand Index, F1-measure and subgoal recall.

A significant portion of web search queries are name entity queries. The major search engines have been exploring various ways to provide better user experiences for name entity queries, such as showing “search tasks ” (Bing search) and showing direct answers (Yahoo!, Kosmix). In order to provide the search tasks or direct answers that can satisfy most popular user intents, we need to capture these intents, together with relationships between them. In this paper we propose an approach for building a hierarchical taxonomy of the generic search intents for a class of name entities (e.g., musicians or cities). The proposed approach can find phrases representing generic intents from user queries, and organize these phrases into a tree, so that phrases indicating equivalent or similar meanings are on the same node, and the parent-child relationships of tree nodes represent the relationships between search intents and their sub-intents. Three different methods are proposed for tree building, which are based on directed maximum spanning tree, hierarchical agglomerative clustering, and pachinko allocation model. Our approaches are purely based on search logs, and do not utilize any existing taxonomies such as Wikipedia. With the evaluation by human judges (via Mechanical Turk), it is shown that our approaches can build trees of phrases that capture the relationships between important search intents.

In NTCIR-10, we participated in the subtask of Subtopic Mining. We classify test topics into two types: role-explicit topic and role-implicit topic. According to the topic type, we devise different approaches to perform subtopic mining. Specifically, for role-explicit topics, we propose an approach of modifier graph based subtopic mining. The key idea is that: The modifier graph corresponding to a role-explicit topic is decomposable into clusters with strong intra-cluster interaction and relatively weak inter-cluster interaction. Each modifier cluster intuitively reveals a possible subtopic. For role-implicit topics that generally express single information needs, we directly generate the ranked list through semantic similarities leveraging on lexical ontologies. The evaluation results show that our best Chinese subtopic mining run gets the first position among all the runs in terms of #D nDCG . However, our English subtopic mining runs show a poor performance, which is planned to be further improved in our future work.

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