Have you ever wondered why Ask.com is so small?

Why Ask has such a small index


The Ask.com search engine produces relatively few results for many commercial queries when compared with results from Yahoo!, MSN, and Google. Ask's current technology is named ExpertRank, but it appears to have evolved from Jon Kleinberg's Hypertext-Induced Topic Search (usually called HITS), which served as the basis for IBM's CLEVER search engine.
HITS identifies authorities (pages pointed to by many hubs) and hubs (pages that point to many authorities). Intuitively, HITS looks like a chicken-and-egg scenario: which does it find first, authorities or hubs? Most likely, a HITS implementation separates pages into three groups: those pages with the most inbound links, those pages with the most outbound links, and those pages that fall into neither of the first two categories. Some refinement must then occur, grouping hubs with authorities. HITS assumes that authoritative documents rarely link directly to each other, but that they do link to hubs.
CLEVER uses on-page content to refine the results of HITS. HITS scans on-page content to select its base set of pages before sorting them into authorities, hubs, and remaining pages. CLEVER most likely looks more closely at the relationships between the hubs and authorities. The best hubs provide more information about the authorities they point to. The best authorities provide more information about their topics and they point to good hubs. Intuitively, both good hubs and good authorities should have more content relevant to the query than poor hubs and authorities.
Ask's ExpertRank apparently attempts to compensate for HITS' inability to distinguish between multiple meanings for words. "Jaguar" is a popular example, as this word is associated with animals, automobiles, Aztec culture and mythology, and discussions about the limitations of HITS. Ask will divide its collection of hubs and authorities into groups (called communities) that appear to be related more closely to just one of the specific meanings of the word. A LocalRank/PageRank-like scoring is then used to determine which of the pages is the most important within each community. That is, the community members' linkage to each other is used to determine how they collectively respect each other.
In essence, Ask is really only interested in highly linked documents. If they are hubs, they must be recognized by authorities. If they are authorities, they must be recognized by hubs that Ask trusts. Ask crawls many more pages than it indexes because it needs to determine where the links are pointing. There must therefore be a much larger index against which Ask builds the query-resolving index. This structure may seem familiar to people who recall Inktomi's old 2-index system, where only the smaller index was used to satisfy queries.

I have fits getting pages into Ask, until they are in DMOZ. I had one picked up by Ask before it was even completed however, for no apparent reason. It remained listed though the entire development, and even without a sitemap. I remains in the in the index today. Strange......

I am going to make good use of this resource!

Peace & SEO
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