Slashdot 上看到 Google 發表了計算網頁真實性的演算法，Knowledge-Based Trust (KBT)：「Google Wants To Rank Websites Based On Facts Not Links」，原始的論文 PDF 檔案可以在「Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources」這邊取得。
論文本身的原理不難懂 (其實方法相當有趣)，主要是給出了三個貢獻。
首先是能夠區分是取出資訊的方法有問題 (extract 的演算法不夠好)，或是網站本身就給出錯誤的資訊：
Our main contribution is a more sophisticated probabilistic model, which can distinguish between two main sources of error: incorrect facts on a page, and incorrect extractions made by an extraction system.
第二個則是在效能上的改善：
Our second contribution is a new method to adaptively decide the granularity of sources to work with: if a specific webpage yields too few triples, we may aggregate it with other webpages from the same website. Conversely, if a website has too many triples, we may split it into smaller ones, to avoid computational bottlenecks (Section 4).
第三個則是提出好的分散式演算法，可以螞蟻雄兵計算出來:
The third contribution of this paper is a detailed, large-scale evaluation of the performance of our model.
KBT 並不是要取代 PageRank，而是跟 PageRank 互相配合，可以有效打擊內容農場 (Content farm) 這類網站，畢竟 PageRank 的假設在一般的狀況下是有邏輯的。
在「High PageRank but low KBT (top-left corner)」這段講到了這件事情：
We consider the 15 gossip websites listed in [16]. Among them, 14 have a PageRank among top 15% of the websites, since such websites are often popular. However, for all of them the KBT are in the bottom 50%; in other words, they are considered less trustworthy than half of the websites. Another kind of websites that often get low KBT are forum websites.
再找時間細讀其他類似的演算法...
