  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.

Information Retrieval;Digital Libraries