Mianwei Zhou

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This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources , social(More)
To reveal information hiding in link space of bibliographical networks, link analysis has been studied from different perspectives in recent years. In this paper, we address a novel problem namely citation prediction, that is: given information about authors, topics, target publication venues as well as time of certain research paper, finding and predicting(More)
This paper studies the entity-centric document filtering task -- given an entity represented by its identification page (e.g., an Wikpedia page), how to correctly identify its relevant documents. In particular, we are interested in learning an entity-centric document filter based on a small number of training entities, and the filter can predict document(More)
As the Web provides rich data embedded in the immense contents inside pages, we witness many ad-hoc efforts for exploiting fine granularity information across Web text, such as Web information extraction, typed-entity search, and question answering. To unify and generalize these efforts, this paper proposes a general search system--Data-oriented Content(More)
Witnessing the richness of data in document content and many ad-hoc efforts for finding such data, we propose a Data-oriented Content Query System(<i>DoCQS</i>), which is oriented towards fine granularity data of all types by searching directly into document content. DoCQS uses the relational model as the underlying data model, and offers a powerful and(More)
For document scoring, although learning to rank and domain adaptation are treated as two different problems in previous works, we discover that they actually share the same challenge of adapting keyword contribution across different queries or domains. In this paper, we propose to study the cross-task document scoring problem, where a task refers to a query(More)
In this paper, we study the task of relational entity search which aims at automatically learning an entity ranking function for a desired relation. To rank entities, we exploit the redundancy abound in their snippets; however, such redundancy is noisy as not all the snippets represent information relevant to the desired relation. To explore useful(More)
Search engines play a crucial role in our daily lives. Relevance is the core problem of a commercial search engine. It has attracted thousands of researchers from both academia and industry and has been studied for decades. Relevance in a modern search engine has gone far beyond text matching, and now involves tremendous challenges. The semantic gap between(More)
Real-world knowledge is growing rapidly nowadays. New entities arise with time, resulting in large volumes of relations that do not exist in current knowledge graphs (KGs). These relations containing at least one new entity are called emerging relations. They often appear in news, and hence the latest information about new entities and relations can be(More)
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