Prediction of links -both new as well as recurring -in a social network representing interactions between individuals is an important problem. In the recent years, there is significant interest in methods that use only the graph structure to make predictions. However, most of them consider a single snapshot of the network as the input, neglecting an important aspect of these social networks viz., their evolution over time.In this work, we investigate the value of incorporating the history information available on the interactions (or links) of the current social network state. Our results unequivocally show that timestamps of past interactions significantly improve the prediction accuracy of new and recurrent links over rather sophisticated methods proposed recently. Furthermore, we introduce a novel testing method which reflects the application of link prediction better than previous approaches.
Attribute synonyms are important ingredients for keywordbased search systems. For instance, web search engines, recognize queries that seek the value of an entity on a specific attribute (referred to as e+a queries) and provide direct answers for them using a combination of knowledge bases, web tables and documents. However, users often refer to an attribute in their e+a query differently from how it is referred in the web table or text passage. In such cases, search engines may fail to return relevant answers. To address that problem, we propose to automatically discover all the alternate ways of referring to the attributes of a given class of entities (referred to as attribute synonyms) in order to improve search quality. The state-of-the-art approach that relies on attribute name co-occurrence in web tables suffers from low precision. Our main insight is to combine positive evidence of attribute synonymity from query click logs, with negative evidence from web table attribute name co-occurrences. We formalize the problem as an optimization problem on a graph, with the attribute names being the vertices and the positive and negative evidences from query logs and web table schemas as weighted edges. We develop a linear programming based algorithm to solve the problem that has bi-criteria approximation guarantees. Our experiments on real-life datasets show that our approach has significantly higher precision and recall compared with the state-of-theart. * Work done during employment at Microsoft Research † Work done during employment at Microsoft Research Copyright is held by the International World Wide Web Conference Committee (IW3C2). IW3C2 reserves the right to provide a hyperlink to the author's site if the Material is used in electronic media.
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The Web and, in particular, knowledge-sharing communities such as Wikipedia contain a huge amount of information encompassing disparate and diverse fields. Knowledge bases such as DBpedia or Yago represent the data in a concise and more structured way bearing the potential of bringing database tools to Web Search. The wealth of data, however, poses the challenge of how to retrieve important and valuable information, which is often intertwined with trivial and less important details. This calls for an efficient and automatic summarization method.In this demonstration proposal, we consider the novel problem of summarizing the information related to a given entity, like a person or an organization. To this end, we utilize the rich type graph that knowledge bases provide for each entity, and define the problem of selecting the best cost-restricted subset of types as summary with good coverage of salient properties.We propose a demonstration of our system which allows the user to specify the entity to summarize, an upper bound on the cost of the resulting summary, as well as to browse the knowledge base in a more simple and intuitive manner.
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