臺灣博碩士論文加值系統

本文探討一個即時性的知識演化的問題，針對一個知識庫中不斷變化的知識要如何擴展進行研究。在先前的研究中要做到知識的取得，來源必須從整個完整的上億個網頁進行搜索確認，然而，一般人平時在關注的知識卻只有在知識庫中的某一部份，例如一些新發生的熱門事件。因此本文利用使用者搜尋記錄得知使用者所關注的實體人事物，並將這些使用者搜尋實體並點選的網頁來輔助系統使用資料探勘方式計算實體間的關聯性，對於關聯性高的兩個實體，系統進入共連網頁抓出此二實體真正的關係，並日復一日的更新入知識庫。

In this paper, we explore a novel problem, called Knowledge Evolution, to identify timely new knowledge triples. In the literature, the need of knowledge enrichment has been recognized as the key to the success of knowledge-based search. However, previous work of automatic knowledge extraction, such as Google Knowledge Vault, aim at identifying the unannotated knowledge triples from the full web-scale content in the offline execution. However, in our study, we show that most people demand a specific knowledge, such as the marriage between Brad Pitt and Angelina Jolie, soon after the information is announced. Moreover, the number of queries of such knowledge dramatically declines after a few days, meaning that the most people cannot obtain the precise knowledge from the execution of the offline knowledge enrichment. To remedy this, we propose the SCKE framework to extract new knowledge triples which can be executed in the online scenario. We model the ’Query-Click Page’ bipartite graph to extract the query correlation and to identify cohesive pairwise entities, finally statistically identifying the confident relation between entities. Our experimental studies show that new triples can also be identified in the very beginning after the event happens, enabling the capability to provide the up-to-date knowledge summary for most user queries.

中文摘要 i
Abstract ii
Contents iii
List of Tables iv
List of Figures v
1 Introduction 1
2 Related Work 5
3 The SCKE Framework and Algorithms 8
3.1 Cohesive Pairwise-entities Generation 8
3.2 Relation Identification 16
3.3 Triples Classifier 25
4 Experimental Results 30
5 Conclusions 40
6 Bibliography 41

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