链接告诉我们词汇和语义的Web内容
Links tell us about lexical and semantic Web content
摘要：
最新一代的Web搜索工具开始利用超文本链接信息来改进排名\cite{Brin98,Kleinberg98}和爬行\cite{Menczer00,Ben-Shaul99etal,Chakrabarti99}算法。这种方法背后隐藏的假设，网络的图形结构和它的内容之间的相关性，还没有得到明确的测试，尽管越来越多的研究Web拓扑\cite{Lawrence98,Albert99,Adamic99,Butler00}。在这里，我形式化和定量验证两个Astructures绘制连接链接信息的词汇和语义的Web内容。叮当内容猜想指出，一个页面与链接到它的页面相似，即，人们可以通过查看链接到一个页面的页面来推断该页面的词汇内容。2我还表明，基于链接线索的词汇推断在Web社区中是相当异构的。链接聚类猜想指出，关于同一主题的页面被聚类在一起，即，人们可以通过查看相邻页面来推断页面的含义。这些结果解释了最新搜索技术的成功，并为以主题或用户驱动的方式定位信息的更动态和可扩展的方法开辟了道路。
英文摘要：
The latest generation of Web search tools is beginning to exploit hypertext link information to improve ranking\cite{Brin98,Kleinberg98} and crawling\cite{Menczer00,Ben-Shaul99etal,Chakrabarti99} algorithms. The hidden assumption behind such approaches, a correlation between the graph structure of the Web and its content, has not been tested explicitly despite increasing research on Web topology\cite{Lawrence98,Albert99,Adamic99,Butler00}. Here I formalize and quantitatively validate two conjectures drawing connections from link information to lexical and semantic Web content. The clink-content conjecture states that a page is similar to the pages that link to it, i.e., one can infer the lexical content of a page by looking at the pages that link to it. I also show that lexical inferences based on link cues are quite heterogeneous across Web communities. The link-cluster conjecture states that pages about the same topic are clustered together, i.e., one can infer the meaning of a page by looking at its neighbours. These results explain the success of the newest search technologies and open the way for more dynamic and scalable methods to locate information in a topic or user driven way.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
