卓越研究

呂榮聰教授,金國慶教授和鄧洪波博士在2012年度JCDL中獲得Vannevar Bush最佳論文獎 (6/2012)

祝賀呂榮聰教授,金國慶教授,他們指導的博士生鄧洪波,以及他們的合作者韓家煒教授,他們的論文“Modeling and Exploiting Heterogeneous Bibliographic Networks for Expertise Ranking”被授予Vannevar Bush最佳論文獎。他們在2012年6月10日至14日在美國華盛頓舉行的第12屆ACM/IEEE-CS資料圖書館聯合會議 (JCDL)上就論文作了報告。JCDL是世界上關於數字形檔方面最具影響力的國際會議。今年共有202篇論文參加了角逐,這篇文章獲得了唯一的Vannevar Bush最佳論文獎。

 

論文以其在專家排名方面的貢獻而獲獎。近來,在學術領域以及工業領域越來越多大人對專家和專業知識檢索產生興趣。為一個給定查詢找到能提供專業意見的專家(尤其是從一個大型的Web 2.0系統中找到)是一項很重要的任務。文章通過開拓和構建異構信息網路模型,提出一個共同的正規化的框架以提高專家檢索的性能。為了有效地探索這些異構信息網路模型,文章研究並系統闡釋了三種假設,藉以刻劃不同圖結構,包括有向圖和無向圖的圖的獨特特徵。接著,文章還通過在基於文檔的模型上,發展規範化約束,給那些假設建立數學模型。實驗結果表明文章中所提出的方法可以比現有的最新水準的模型取得更好的結果。

 

參考連結: http://jcdl.org/awards.php

 

論文摘要:

Recently expertise retrieval has received increasing interests in both academia and industry. Finding experts with demonstrated expertise for a given query is a nontrivial task especially from a large-scale Web 2.0 systems, such as question answering and bibliography data, where users are actively publishing useful content online, interacting with each other, and forming social networks in various ways, leading to heterogeneous networks in addition to the large amounts of textual content information. Many approaches have been proposed and shown to be useful for expertise ranking. However, most of these methods only consider the textual documents while ignoring heterogeneous network structures or can merely integrate with one additional kind of information. None of them can fully exploit the characteristics of heterogeneous networks. In this paper, we propose a joint regularization framework to enhance expertise retrieval by modeling heterogeneous networks as regularization constraints on top of document-centric model. We argue that multi-typed linking edges reveal valuable information which should be treated differently. Motivated by this intuition, we formulate three hypotheses to capture unique characteristics for different graphs, and mathematically model those hypotheses jointly with the document and other information. To illustrate our methodology, we apply the framework to expert finding applications using a bibliography dataset with 1.1 million papers and 0.7 million authors. The experimental results show that our proposed approach can achieve significantly better results than the baseline and other enhanced models.