著者

宮西 大樹 関 和広 上原 邦昭

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.27, no.3, pp.223-234, 2012 (Released:2012-03-28)

参考文献数

32

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This paper proposes a framework to predict future significance or importance of nodes of a network through link prediction. The network can be of any kind, such as a co-authorship network where nodes are authors and co-authors are linked by edges. In this example, predicting significant nodes means to discover influential authors in the future. There are existing approaches to predicting such significant nodes in a future network and they typically rely on existing relationships between nodes. However, since such relationships are dynamic and would naturally change over time (e.g., new co-authorship continues to emerge), approaches based only on the current status of the network would have limited potentiality to predict the future. In contrast, our proposed approach first predicts future links between nodes by multiple supervised classifiers and applies the RankBoost algorithm for combining the predictions such that the links would lead to more precise predictions of a centrality (significance) measure of our choice. To demonstrate the effectiveness of our proposed approach, a series of experiments are carried out on the arXiv (HEP-Th) citation data set.

著者

宮西 大樹 関 和広 上原 邦昭

出版者

一般社団法人情報処理学会

雑誌

情報処理学会論文誌データベース(TOD) (ISSN:18827799)

巻号頁・発行日

vol.7, no.2, pp.1-10, 2014-06-30

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マイクロブログ検索には,単語を用いた疑似適合フィードバックによるクエリ拡張が有効である.しかし,単語は意味的・時間的な曖昧性を持つため,単語を用いたクエリ拡張は有効に機能しない場合がある.そこで,本稿では,単語や2語以上の単語の組合せであるコンセプトを用いた疑似適合フィードバックによるクエリ拡張手法を提案する.さらに,提案手法は検索クエリと同時期に出現するコンセプトの頻度の時間遷移に関する情報を疑似適合フィードバックに組み入れることで,マイクロブログサービスのリアルタイム性を考慮する.代表的なマイクロブログデータであるTweets2011コーパスを用いた実験から,提案するコンセプトを用いたクエリ拡張によって,検索クエリに適合し,かつ情報量の豊富な文書を効果的に検索できることを示す.Incorporating the temporal property of words into query expansion methods based on relevance feedback has been shown to have a significant positive effect on microblog searching. In this paper, we propose a concept-based query expansion method based on a temporal relevance model that uses the temporal variation of concepts (e.g., terms or phrases) on microblogs. Our model naturally extends an extremely effective existing concept-based relevance model by tracking the concept frequency over time. Moreover, the proposed model produces important concepts that are frequently used within a particular time period associated with a given topic, which have more power to discriminate between relevant and non-relevant microblog documents than words. Our experiments using a corpus of microblog data (the Tweets2011 corpus) show that the proposed concept-based query expansion method improves search performance significantly, especially when retrieving highly relevant documents.