Learn More
In large social networks, nodes (users, entities) are influenced by others for various reasons. For example, the colleagues have strong influence on one's work, while the friends have strong influence on one's daily life. How to differentiate the social influences from different angles(topics)? How to quantify the strength of those social influences? How to(More)
With the Web content having been changed from homogeneity to heterogeneity, the recommendation becomes a more challenging issue. In this paper, we have investigated the recommendation problem on a general heterogeneous Web social network. We categorize the recommendation needs on it into two main scenarios: recommendation when a person is doing a search and(More)
In this paper, we present a topic level expertise search framework for heterogeneous networks. Different from the traditional Web search engines that perform retrieval and ranking at document level (or at object level), we investigate the problem of expertise search at topic level over heterogeneous networks. In particular, we study this problem in an(More)
Expert finding, aiming to answer the question: “Who are experts on topic X?”, is becoming one of the biggest challenges for information management. Much work has been conducted for expert finding. Methods based on language model, topic model, and random walk have been proposed. However, little work has studied why people want to find experts. In this work,(More)
Traditional ranking mainly focuses on one type of data source, and effective modeling still relies on a sufficiently large number of labeled or supervised examples. However, in many real-world applications, in particular with the rapid growth of the Web 2.0, ranking over multiple interrelated (heterogeneous) domains becomes a common situation, where in some(More)
Software frameworks which support integration and scaling of text analysis algorithms make it possible to build complex, high performance information systems for information extraction, information retrieval, and question answering; IBM's Watson is a prominent example. As the complexity and scaling of information systems become ever greater, it is much more(More)
In this paper, we study the problem of topic-level random walk, which concerns the random walk at the topic level. Previously, several related works such as topic sensitive page rank have been conducted. However, topics in these methods were predefined, which makes the methods inapplicable to different domains. In this paper, we propose a four-step approach(More)
This paper describes the CMU OAQA system evaluated in the BioASQ 3B Question Answering track. We first present a three-layered architecture , and then describe the components integrated for exact answer generation and retrieval. Using over 400 factoid and list questions from past BioASQ 1B and 2B tasks as background knowledge, we focus on how to learn to(More)