The Anatomy of a Large-Scale Hypertextual Web Search Engine (1)
The Anatomy of a Large-Scale Hypertextual
Web Search Engine
Abstract
In this paper, we present Google, a prototype of a large-scale search engine which makes heavy
use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently
and produce much more satisfying search results than existing systems. The prototype with a full
text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/
To engineer a search engine is a challenging task. Search engines index tens to hundreds of
millions of web pages involving a comparable number of distinct terms. They answer tens of
millions of queries every day. Despite the importance of large-scale search engines on the web,
very little academic research has been done on them. Furthermore, due to rapid advance in
technology and web proliferation, creating a web search engine today is very different from three
years ago. This paper provides an in-depth description of our large-scale web search engine -- the
first such detailed public description we know of to date. Apart from the problems of scaling
traditional search techniques to data of this magnitude, there are new technical challenges involved
with using the additional information present in hypertext to produce better search results. This
paper addresses this question of how to build a practical large-scale system which can exploit the
additional information present in hypertext. Also we look at the problem of how to effectively deal
with uncontrolled hypertext collections where anyone can publish anything they want.
Keywords
World Wide Web, Search Engines, Information Retrieval, PageRank, Google
1. Introduction
(Note: There are two versions of this paper -- a longer full version and a shorter printed version. The
full version is available on the web and the conference CD-ROM.)
The web creates new challenges for information retrieval. The amount of information on the web is
growing rapidly, as well as the number of new users inexperienced in the art of web research. People are
likely to surf the web using its link graph, often starting with high quality human maintained indices
such as Yahoo! or with search engines. Human maintained lists cover popular topics effectively but are
subjective, expensive to build and maintain, slow to improve, and cannot cover all esoteric topics.
Automated search engines that rely on keyword matching usually return too many low quality matches.
To make matters worse, some advertisers attempt to gain people’s attention by taking measures meant to
mislead automated search engines. We have built a large-scale search engine which addresses many of
the problems of existing systems. It makes especially heavy use of the additional structure present in
hypertext to provide much higher quality search results. We chose our system name, Google, because it
is a common spelling of googol, or 10100 and fits well with our goal of building very large-scale search
engines.
1.1 Web Search Engines -- Scaling Up: 1994 - 2000
Search engine technology has had to scale dramatically to keep up with the growth of the web. In 1994,
one of the first web search engines, the World Wide Web Worm (WWWW) [McBryan 94] had an index
of 110,000 web pages and web accessible documents. As of November, 1997, the top search engines
claim to index from 2 million (WebCrawler) to 100 million web documents (from Search Engine
Watch). It is foreseeable that by the year 2000, a comprehensive index of the Web will contain over a
billion documents. At the same time, the number of queries search engines handle has grown incredibly
too. In March and April 1994, the World Wide Web Worm received an average of about 1500 queries
per day. In November 1997, Altavista claimed it handled roughly 20 million queries per day. With the
increasing number of users on the web, and automated systems which query search engines, it is likely
that top search engines will handle hundreds of millions of queries per day by the year 2000. The goal of
our system is to address many of the problems, both in quality and scalability, introduced by scaling
search engine technology to such extraordinary numbers.
1.2. Google: Scaling with the Web
Creating a search engine which scales even to today’s web presents many challenges. Fast crawling
technology is needed to gather the web documents and keep them up to date. Storage space must be used
efficiently to store indices and, optionally, the documents themselves. The indexing system must process
hundreds of gigabytes of data efficiently. Queries must be handled quickly, at a rate of hundreds to
thousands per second.
These tasks are becoming increasingly difficult as the Web grows. However, hardware performance and
cost have improved dramatically to partially offset the difficulty. There are, however, several notable
exceptions to this progress such as disk seek time and operating system robustness. In designing Google,
we have considered both the rate of growth of the Web and technological changes. Google is designed to
scale well to extremely large data sets. It makes efficient use of storage space to store the index. Its data
structures are optimized for fast and efficient access (see section 4.2). Further, we expect that the cost to
index and store text or HTML will eventually decline relative to the amount that will be available (see
Appendix B). This will result in favorable scaling properties for centralized systems like Google.
1.3 Design Goals
1.3.1 Improved Search Quality
Our main goal is to improve the quality of web search engines. In 1994, some people believed that a
complete search index would make it possible to find anything easily. According to Best of the Web
1994 -- Navigators, "The best navigation service should make it easy to find almost anything on the
Web (once all the data is entered)." However, the Web of 1997 is quite different. Anyone who has used
a search engine recently, can readily testify that the completeness of the index is not the only factor in
the quality of search results. "Junk results" often wash out any results that a user is interested in. In fact,
as of November 1997, only one of the top four commercial search engines finds itself (returns its own
search page in response to its name in the top ten results). One of the main causes of this problem is that
the number of documents in the indices has been increasing by many orders of magnitude, but the user’s
ability to look at documents has not. People are still only willing to look at the first few tens of results.
Because of this, as the collection size grows, we need tools that have very high precision (number of
relevant documents returned, say in the top tens of results). Indeed, we want our notion of "relevant" to
only include the very best documents since there may be tens of thousands of slightly relevant
documents. This very high precision is important even at the expense of recall (the total number of
relevant documents the system is able to return). There is quite a bit of recent optimism that the use of
more hypertextual information can help improve search and other applications [Marchiori 97] [Spertus
97] [Weiss 96] [Kleinberg 98]. In particular, link structure [Page 98] and link text provide a lot of
information for making relevance judgments and quality filtering. Google makes use of both link
structure and anchor text (see Sections 2.1 and 2.2).
1.3.2 Academic Search Engine Research
Aside from tremendous growth, the Web has also become increasingly commercial over time. In 1993,
1.5% of web servers were on .com domains. This number grew to over 60% in 1997. At the same time,
search engines have migrated from the academic domain to the commercial. Up until now most search
engine development has gone on at companies with little publication of technical details. This causes
search engine technology to remain largely a black art and to be advertising oriented (see Appendix A).
With Google, we have a strong goal to push more development and understanding into the academic
realm.
Another important design goal was to build systems that reasonable numbers of people can actually use.
Usage was important to us because we think some of the most interesting research will involve
leveraging the vast amount of usage data that is available from modern web systems. For example, there
are many tens of millions of searches performed every day. However, it is very difficult to get this data,
mainly because it is considered commercially valuable.
Our final design goal was to build an architecture that can support novel research activities on
large-scale web data. To support novel research uses, Google stores all of the actual documents it crawls
in compressed form. One of our main goals in designing Google was to set up an environment where
other researchers can come in quickly, process large chunks of the web, and produce interesting results
that would have been very difficult to produce otherwise. In the short time the system has been up, there
have already been several papers using databases generated by Google, and many others are underway.
Another goal we have is to set up a Spacelab-like environment where researchers or even students can
propose and do interesting experiments on our large-scale web data.
2. System Features
The Google search engine has two important features that help it produce high precision results. First, it
makes use of the link structure of the Web to calculate a quality ranking for each web page. This ranking
is called PageRank and is described in detail in [Page 98]. Second, Google utilizes link to improve
search results.
2.1 PageRank: Bringing Order to the Web
The citation (link) graph of the web is an important resource that has largely gone unused in existing
web search engines. We have created maps containing as many as 518 million of these hyperlinks, a
significant sample of the total. These maps allow rapid calculation of a web page’s "PageRank", an
objective measure of its citation importance that corresponds well with people’s subjective idea of
importance. Because of this correspondence, PageRank is an excellent way to prioritize the results of
web keyword searches. For most popular subjects, a simple text matching search that is restricted to web
page titles performs admirably when PageRank prioritizes the results (demo available at
google.stanford.edu). For the type of full text searches in the main Google system, PageRank also helps
a great deal.
2.1.1 Description of PageRank Calculation
Academic citation literature has been applied to the web, largely by counting citations or backlinks to a
given page. This gives some approximation of a page’s importance or quality. PageRank extends this
idea by not counting links from all pages equally, and by normalizing by the number of links on a page.
PageRank is defined as follows:
We assume page A has pages T1...Tn which point to it (i.e., are citations). The parameter d
is a damping factor which can be set between 0 and 1. We usually set d to 0.85. There are
more details about d in the next section. Also C(A) is defined as the number of links going
out of page A. The PageRank of a page A is given as follows:
PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))
Note that the PageRanks form a probability distribution over web pages, so the sum of all
web pages’ PageRanks will be one.
PageRank or PR(A) can be calculated using a simple iterative algorithm, and corresponds to the
principal eigenvector of the normalized link matrix of the web. Also, a PageRank for 26 million web
pages can be computed in a few hours on a medium size workstation. There are many other details
which are beyond the scope of this paper.
2.1.2 Intuitive Justification
PageRank can be thought of as a model of user behavior. We assume there is a "random surfer" who is
given a web page at random and keeps clicking on links, never hitting "back" but eventually gets bored
and starts on another random page. The probability that the random surfer visits a page is its PageRank.
And, the d damping factor is the probability at each page the "random surfer" will get bored and request
another random page. One important variation is to only add the damping factor d to a single page, or a
group of pages. This allows for personalization and can make it nearly impossible to deliberately
mislead the system in order to get a higher ranking. We have several other extensions to PageRank,
again see [Page 98].
Another intuitive justification is that a page can have a high PageRank if there are many pages that point
to it, or if there are some pages that point to it and have a high PageRank. Intuitively, pages that are well
cited from many places around the web are worth looking at. Also, pages that have perhaps only one
citation from something like the Yahoo! homepage are also generally worth looking at. If a page was not
high quality, or was a broken link, it is quite likely that Yahoo’s homepage would not link to it.
PageRank handles both these cases and everything in between by recursively propagating weights
through the link structure of the web.
2.2 Anchor Text
The text of links is treated in a special way in our search engine. Most search engines associate the text
of a link with the page that the link is on. In addition, we associate it with the page the link points to.
This has several advantages. First, anchors often provide more accurate descriptions of web pages than
the pages themselves. Second, anchors may exist for documents which cannot be indexed by a
text-based search engine, such as images, programs, and databases. This makes it possible to return web
pages which have not actually been crawled. Note that pages that have not been crawled can cause
problems, since they are never checked for validity before being returned to the user. In this case, the
search engine can even return a page that never actually existed, but had hyperlinks pointing to it.
However, it is possible to sort the results, so that this particular problem rarely happens.
This idea of propagating anchor text to the page it refers to was implemented in the World Wide Web
Worm [McBryan 94] especially because it helps search non-text information, and expands the search
coverage with fewer downloaded documents. We use anchor propagation mostly because anchor text
can help provide better quality results. Using anchor text efficiently is technically difficult because of
the large amounts of data which must be processed. In our current crawl of 24 million pages, we had
over 259 million anchors which we indexed.
2.3 Other Features
Aside from PageRank and the use of anchor text, Google has several other features. First, it has location
information for all hits and so it makes extensive use of proximity in search. Second, Google keeps track
of some visual presentation details such as font size of words. Words in a larger or bolder font are
weighted higher than other words. Third, full raw HTML of pages is available in a repository.
3 Related Work
Search research on the web has a short and concise history. The World Wide Web Worm (WWWW)
[McBryan 94] was one of the first web search engines. It was subsequently followed by several other
academic search engines, many of which are now public companies. Compared to the growth of the
Web and the importance of search engines there are precious few documents about recent search engines
[Pinkerton 94]. According to Michael Mauldin (chief scientist, Lycos Inc) [Mauldin], "the various
services (including Lycos) closely guard the details of these databases". However, there has been a fair
amount of work on specific features of search engines. Especially well represented is work which can
get results by post-processing the results of existing commercial search engines, or produce small scale
"individualized" search engines. Finally, there has been a lot of research on information retrieval
systems, especially on well controlled collections. In the next two sections, we discuss some areas where
this research needs to be extended to work better on the web.
3.1 Information Retrieval
Work in information retrieval systems goes back many years and is well developed [Witten 94].
However, most of the research on information retrieval systems is on small well controlled
homogeneous collections such as collections of scientific papers or news stories on a related topic.
Indeed, the primary benchmark for information retrieval, the Text Retrieval Conference [TREC 96],
uses a fairly small, well controlled collection for their benchmarks. The "Very Large Corpus"
benchmark is only 20GB compared to the 147GB from our crawl of 24 million web pages. Things that
work well on TREC often do not produce good results on the web. For example, the standard vector
space model tries to return the document that most closely approximates the query, given that both query
and document are vectors defined by their word occurrence. On the web, this strategy often returns very
short documents that are the query plus a few words. For example, we have seen a major search engine
return a page containing only "Bill Clinton Sucks" and picture from a "Bill Clinton" query. Some argue
that on the web, users should specify more accurately what they want and add more words to their
query. We disagree vehemently with this position. If a user issues a query like "Bill Clinton" they should
get reasonable results since there is a enormous amount of high quality information available on this
topic. Given examples like these, we believe that the standard information retrieval work needs to be
extended to deal effectively with the web.
3.2 Differences Between the Web and Well Controlled Collections
The web is a vast collection of completely uncontrolled heterogeneous documents. Documents on the
web have extreme variation internal to the documents, and also in the external meta information that
might be available. For example, documents differ internally in their language (both human and
programming), vocabulary (email addresses, links, zip codes, phone numbers, product numbers), type or
format (text, HTML, PDF, images, sounds), and may even be machine generated (log files or output
from a database). On the other hand, we define external meta information as information that can be
inferred about a document, but is not contained within it. Examples of external meta information include
things like reputation of the source, update frequency, quality, popularity or usage, and citations. Not
only are the possible sources of external meta information varied, but the things that are being measured
vary many orders of magnitude as well. For example, compare the usage information from a major
homepage, like Yahoo’s which currently receives millions of page views every day with an obscure
historical article which might receive one view every ten years. Clearly, these two items must be treated
very differently by a search engine.
Another big difference between the web and traditional well controlled collections is that there is
virtually no control over what people can put on the web. Couple this flexibility to publish anything with
the enormous influence of search engines to route traffic and companies which deliberately
manipulating search engines for profit become a serious problem. This problem that has not been
addressed in traditional closed information retrieval systems. Also, it is interesting to note that metadata
efforts have largely failed with web search engines, because any text on the page which is not directly
represented to the user is abused to manipulate search engines. There are even numerous companies
which specialize in manipulating search engines for profit.
Web Search Engine
Abstract
In this paper, we present Google, a prototype of a large-scale search engine which makes heavy
use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently
and produce much more satisfying search results than existing systems. The prototype with a full
text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/
To engineer a search engine is a challenging task. Search engines index tens to hundreds of
millions of web pages involving a comparable number of distinct terms. They answer tens of
millions of queries every day. Despite the importance of large-scale search engines on the web,
very little academic research has been done on them. Furthermore, due to rapid advance in
technology and web proliferation, creating a web search engine today is very different from three
years ago. This paper provides an in-depth description of our large-scale web search engine -- the
first such detailed public description we know of to date. Apart from the problems of scaling
traditional search techniques to data of this magnitude, there are new technical challenges involved
with using the additional information present in hypertext to produce better search results. This
paper addresses this question of how to build a practical large-scale system which can exploit the
additional information present in hypertext. Also we look at the problem of how to effectively deal
with uncontrolled hypertext collections where anyone can publish anything they want.
Keywords
World Wide Web, Search Engines, Information Retrieval, PageRank, Google
1. Introduction
(Note: There are two versions of this paper -- a longer full version and a shorter printed version. The
full version is available on the web and the conference CD-ROM.)
The web creates new challenges for information retrieval. The amount of information on the web is
growing rapidly, as well as the number of new users inexperienced in the art of web research. People are
likely to surf the web using its link graph, often starting with high quality human maintained indices
such as Yahoo! or with search engines. Human maintained lists cover popular topics effectively but are
subjective, expensive to build and maintain, slow to improve, and cannot cover all esoteric topics.
Automated search engines that rely on keyword matching usually return too many low quality matches.
To make matters worse, some advertisers attempt to gain people’s attention by taking measures meant to
mislead automated search engines. We have built a large-scale search engine which addresses many of
the problems of existing systems. It makes especially heavy use of the additional structure present in
hypertext to provide much higher quality search results. We chose our system name, Google, because it
is a common spelling of googol, or 10100 and fits well with our goal of building very large-scale search
engines.
1.1 Web Search Engines -- Scaling Up: 1994 - 2000
Search engine technology has had to scale dramatically to keep up with the growth of the web. In 1994,
one of the first web search engines, the World Wide Web Worm (WWWW) [McBryan 94] had an index
of 110,000 web pages and web accessible documents. As of November, 1997, the top search engines
claim to index from 2 million (WebCrawler) to 100 million web documents (from Search Engine
Watch). It is foreseeable that by the year 2000, a comprehensive index of the Web will contain over a
billion documents. At the same time, the number of queries search engines handle has grown incredibly
too. In March and April 1994, the World Wide Web Worm received an average of about 1500 queries
per day. In November 1997, Altavista claimed it handled roughly 20 million queries per day. With the
increasing number of users on the web, and automated systems which query search engines, it is likely
that top search engines will handle hundreds of millions of queries per day by the year 2000. The goal of
our system is to address many of the problems, both in quality and scalability, introduced by scaling
search engine technology to such extraordinary numbers.
1.2. Google: Scaling with the Web
Creating a search engine which scales even to today’s web presents many challenges. Fast crawling
technology is needed to gather the web documents and keep them up to date. Storage space must be used
efficiently to store indices and, optionally, the documents themselves. The indexing system must process
hundreds of gigabytes of data efficiently. Queries must be handled quickly, at a rate of hundreds to
thousands per second.
These tasks are becoming increasingly difficult as the Web grows. However, hardware performance and
cost have improved dramatically to partially offset the difficulty. There are, however, several notable
exceptions to this progress such as disk seek time and operating system robustness. In designing Google,
we have considered both the rate of growth of the Web and technological changes. Google is designed to
scale well to extremely large data sets. It makes efficient use of storage space to store the index. Its data
structures are optimized for fast and efficient access (see section 4.2). Further, we expect that the cost to
index and store text or HTML will eventually decline relative to the amount that will be available (see
Appendix B). This will result in favorable scaling properties for centralized systems like Google.
1.3 Design Goals
1.3.1 Improved Search Quality
Our main goal is to improve the quality of web search engines. In 1994, some people believed that a
complete search index would make it possible to find anything easily. According to Best of the Web
1994 -- Navigators, "The best navigation service should make it easy to find almost anything on the
Web (once all the data is entered)." However, the Web of 1997 is quite different. Anyone who has used
a search engine recently, can readily testify that the completeness of the index is not the only factor in
the quality of search results. "Junk results" often wash out any results that a user is interested in. In fact,
as of November 1997, only one of the top four commercial search engines finds itself (returns its own
search page in response to its name in the top ten results). One of the main causes of this problem is that
the number of documents in the indices has been increasing by many orders of magnitude, but the user’s
ability to look at documents has not. People are still only willing to look at the first few tens of results.
Because of this, as the collection size grows, we need tools that have very high precision (number of
relevant documents returned, say in the top tens of results). Indeed, we want our notion of "relevant" to
only include the very best documents since there may be tens of thousands of slightly relevant
documents. This very high precision is important even at the expense of recall (the total number of
relevant documents the system is able to return). There is quite a bit of recent optimism that the use of
more hypertextual information can help improve search and other applications [Marchiori 97] [Spertus
97] [Weiss 96] [Kleinberg 98]. In particular, link structure [Page 98] and link text provide a lot of
information for making relevance judgments and quality filtering. Google makes use of both link
structure and anchor text (see Sections 2.1 and 2.2).
1.3.2 Academic Search Engine Research
Aside from tremendous growth, the Web has also become increasingly commercial over time. In 1993,
1.5% of web servers were on .com domains. This number grew to over 60% in 1997. At the same time,
search engines have migrated from the academic domain to the commercial. Up until now most search
engine development has gone on at companies with little publication of technical details. This causes
search engine technology to remain largely a black art and to be advertising oriented (see Appendix A).
With Google, we have a strong goal to push more development and understanding into the academic
realm.
Another important design goal was to build systems that reasonable numbers of people can actually use.
Usage was important to us because we think some of the most interesting research will involve
leveraging the vast amount of usage data that is available from modern web systems. For example, there
are many tens of millions of searches performed every day. However, it is very difficult to get this data,
mainly because it is considered commercially valuable.
Our final design goal was to build an architecture that can support novel research activities on
large-scale web data. To support novel research uses, Google stores all of the actual documents it crawls
in compressed form. One of our main goals in designing Google was to set up an environment where
other researchers can come in quickly, process large chunks of the web, and produce interesting results
that would have been very difficult to produce otherwise. In the short time the system has been up, there
have already been several papers using databases generated by Google, and many others are underway.
Another goal we have is to set up a Spacelab-like environment where researchers or even students can
propose and do interesting experiments on our large-scale web data.
2. System Features
The Google search engine has two important features that help it produce high precision results. First, it
makes use of the link structure of the Web to calculate a quality ranking for each web page. This ranking
is called PageRank and is described in detail in [Page 98]. Second, Google utilizes link to improve
search results.
2.1 PageRank: Bringing Order to the Web
The citation (link) graph of the web is an important resource that has largely gone unused in existing
web search engines. We have created maps containing as many as 518 million of these hyperlinks, a
significant sample of the total. These maps allow rapid calculation of a web page’s "PageRank", an
objective measure of its citation importance that corresponds well with people’s subjective idea of
importance. Because of this correspondence, PageRank is an excellent way to prioritize the results of
web keyword searches. For most popular subjects, a simple text matching search that is restricted to web
page titles performs admirably when PageRank prioritizes the results (demo available at
google.stanford.edu). For the type of full text searches in the main Google system, PageRank also helps
a great deal.
2.1.1 Description of PageRank Calculation
Academic citation literature has been applied to the web, largely by counting citations or backlinks to a
given page. This gives some approximation of a page’s importance or quality. PageRank extends this
idea by not counting links from all pages equally, and by normalizing by the number of links on a page.
PageRank is defined as follows:
We assume page A has pages T1...Tn which point to it (i.e., are citations). The parameter d
is a damping factor which can be set between 0 and 1. We usually set d to 0.85. There are
more details about d in the next section. Also C(A) is defined as the number of links going
out of page A. The PageRank of a page A is given as follows:
PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))
Note that the PageRanks form a probability distribution over web pages, so the sum of all
web pages’ PageRanks will be one.
PageRank or PR(A) can be calculated using a simple iterative algorithm, and corresponds to the
principal eigenvector of the normalized link matrix of the web. Also, a PageRank for 26 million web
pages can be computed in a few hours on a medium size workstation. There are many other details
which are beyond the scope of this paper.
2.1.2 Intuitive Justification
PageRank can be thought of as a model of user behavior. We assume there is a "random surfer" who is
given a web page at random and keeps clicking on links, never hitting "back" but eventually gets bored
and starts on another random page. The probability that the random surfer visits a page is its PageRank.
And, the d damping factor is the probability at each page the "random surfer" will get bored and request
another random page. One important variation is to only add the damping factor d to a single page, or a
group of pages. This allows for personalization and can make it nearly impossible to deliberately
mislead the system in order to get a higher ranking. We have several other extensions to PageRank,
again see [Page 98].
Another intuitive justification is that a page can have a high PageRank if there are many pages that point
to it, or if there are some pages that point to it and have a high PageRank. Intuitively, pages that are well
cited from many places around the web are worth looking at. Also, pages that have perhaps only one
citation from something like the Yahoo! homepage are also generally worth looking at. If a page was not
high quality, or was a broken link, it is quite likely that Yahoo’s homepage would not link to it.
PageRank handles both these cases and everything in between by recursively propagating weights
through the link structure of the web.
2.2 Anchor Text
The text of links is treated in a special way in our search engine. Most search engines associate the text
of a link with the page that the link is on. In addition, we associate it with the page the link points to.
This has several advantages. First, anchors often provide more accurate descriptions of web pages than
the pages themselves. Second, anchors may exist for documents which cannot be indexed by a
text-based search engine, such as images, programs, and databases. This makes it possible to return web
pages which have not actually been crawled. Note that pages that have not been crawled can cause
problems, since they are never checked for validity before being returned to the user. In this case, the
search engine can even return a page that never actually existed, but had hyperlinks pointing to it.
However, it is possible to sort the results, so that this particular problem rarely happens.
This idea of propagating anchor text to the page it refers to was implemented in the World Wide Web
Worm [McBryan 94] especially because it helps search non-text information, and expands the search
coverage with fewer downloaded documents. We use anchor propagation mostly because anchor text
can help provide better quality results. Using anchor text efficiently is technically difficult because of
the large amounts of data which must be processed. In our current crawl of 24 million pages, we had
over 259 million anchors which we indexed.
2.3 Other Features
Aside from PageRank and the use of anchor text, Google has several other features. First, it has location
information for all hits and so it makes extensive use of proximity in search. Second, Google keeps track
of some visual presentation details such as font size of words. Words in a larger or bolder font are
weighted higher than other words. Third, full raw HTML of pages is available in a repository.
3 Related Work
Search research on the web has a short and concise history. The World Wide Web Worm (WWWW)
[McBryan 94] was one of the first web search engines. It was subsequently followed by several other
academic search engines, many of which are now public companies. Compared to the growth of the
Web and the importance of search engines there are precious few documents about recent search engines
[Pinkerton 94]. According to Michael Mauldin (chief scientist, Lycos Inc) [Mauldin], "the various
services (including Lycos) closely guard the details of these databases". However, there has been a fair
amount of work on specific features of search engines. Especially well represented is work which can
get results by post-processing the results of existing commercial search engines, or produce small scale
"individualized" search engines. Finally, there has been a lot of research on information retrieval
systems, especially on well controlled collections. In the next two sections, we discuss some areas where
this research needs to be extended to work better on the web.
3.1 Information Retrieval
Work in information retrieval systems goes back many years and is well developed [Witten 94].
However, most of the research on information retrieval systems is on small well controlled
homogeneous collections such as collections of scientific papers or news stories on a related topic.
Indeed, the primary benchmark for information retrieval, the Text Retrieval Conference [TREC 96],
uses a fairly small, well controlled collection for their benchmarks. The "Very Large Corpus"
benchmark is only 20GB compared to the 147GB from our crawl of 24 million web pages. Things that
work well on TREC often do not produce good results on the web. For example, the standard vector
space model tries to return the document that most closely approximates the query, given that both query
and document are vectors defined by their word occurrence. On the web, this strategy often returns very
short documents that are the query plus a few words. For example, we have seen a major search engine
return a page containing only "Bill Clinton Sucks" and picture from a "Bill Clinton" query. Some argue
that on the web, users should specify more accurately what they want and add more words to their
query. We disagree vehemently with this position. If a user issues a query like "Bill Clinton" they should
get reasonable results since there is a enormous amount of high quality information available on this
topic. Given examples like these, we believe that the standard information retrieval work needs to be
extended to deal effectively with the web.
3.2 Differences Between the Web and Well Controlled Collections
The web is a vast collection of completely uncontrolled heterogeneous documents. Documents on the
web have extreme variation internal to the documents, and also in the external meta information that
might be available. For example, documents differ internally in their language (both human and
programming), vocabulary (email addresses, links, zip codes, phone numbers, product numbers), type or
format (text, HTML, PDF, images, sounds), and may even be machine generated (log files or output
from a database). On the other hand, we define external meta information as information that can be
inferred about a document, but is not contained within it. Examples of external meta information include
things like reputation of the source, update frequency, quality, popularity or usage, and citations. Not
only are the possible sources of external meta information varied, but the things that are being measured
vary many orders of magnitude as well. For example, compare the usage information from a major
homepage, like Yahoo’s which currently receives millions of page views every day with an obscure
historical article which might receive one view every ten years. Clearly, these two items must be treated
very differently by a search engine.
Another big difference between the web and traditional well controlled collections is that there is
virtually no control over what people can put on the web. Couple this flexibility to publish anything with
the enormous influence of search engines to route traffic and companies which deliberately
manipulating search engines for profit become a serious problem. This problem that has not been
addressed in traditional closed information retrieval systems. Also, it is interesting to note that metadata
efforts have largely failed with web search engines, because any text on the page which is not directly
represented to the user is abused to manipulate search engines. There are even numerous companies
which specialize in manipulating search engines for profit.