Well this is what really started it all the seems to be the base premise of Google today. I thought this might make for some interesting reading.---Joe
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Hilltop: A Search Engine based on Expert Documents
Krishna Bharat
Compaq, Systems Research Center, Palo Alto, CA 94301
(Current Address: Google Inc., 2400 Bayshore Parkway,
Mountain View, CA 94043)
krishna@google.com
George A. Mihaila
Department of Computer Science
University of
Toronto.
georgem@cs.toronto.edu
Abstract:
In response to a query a search engine returns a ranked list of
documents. If the query is broad (i.e., it matches many documents)
then the returned list is usually too long to view fully. Studies show
that users usually look at only the top 10 to 20 results. In this
paper, we propose a novel ranking scheme for broad queries that places
the most authoritative pages on the query topic at the top of
the ranking. Our algorithm operates on a special index of "expert
documents." These are a subset of the pages on the WWW identified as
directories of links to non-affiliated sources on specific topics.
Results are ranked based on the match between the query and relevant
descriptive text for hyperlinks on expert pages pointing to a given
result page. We present a prototype search engine that implements our
ranking scheme and discuss its performance. With a relatively small
(2.5 million page) expert index, our algorithm was able to perform
comparably on broad queries with the best of the mainstream search
engines.
1 Introduction
When searching the WWW broad queries tend to produce a large result
set. This set is hard to rank based on content alone, since the
quality and "authoritativeness" of a page (namely, a measure of how
authoritative the page is on the subject) cannot be assessed
solely by analyzing its content. In traditional information retrieval
we make the assumption that the articles in the corpus originate from
a reputable source and all words found in an article were intended for
the reader. These assumptions do not hold on the WWW since content is
authored by sources of varying quality and words are often added
indiscriminately to boost the page's ranking. For example, some pages
are created to purposefully mislead search engines, and are known
popularly as "spam" pages. The most virulent of spam techniques
involves deliberately returning someone else's popular page to search
engine robots instead of the actual page, to steal their traffic. Even
when there is no intention to mislead search engines, the WWW tends to
be crowded with information on topics popular with users.
Consequently, for broad queries keyword matching seems inadequate.
Prior approaches that have used content analysis to rank broad
queries on the WWW cannot distinguish between authoritative and
non-authoritative pages (e.g., they fail to detect spam pages). Hence
the ranking tends to be poor and search services have turned to other
sources of information besides content to rank results. We next
describe some of these ranking strategies, followed by our new
approach to authoritative ranking - which we call Hilltop.
1.1 Related Work
Three approaches to improve the authoritativeness of ranked results
have been taken in the past: Ranking Based on Human
Classification: Human editors have been used by companies such as
Yahoo! and
Mining Company to manually
associate a set of categories and keywords with a subset of documents
on the web. These are then matched against the user's query to return
valid matches. The trouble with this approach is that: (a) it is slow
and can only be applied to a small number of pages, and (b) often the
keywords and classifications assigned by the human judges are
inadequate or incomplete. Given the rate at which the WWW is growing
and the wide variation in queries this is not a comprehensive
solution.
Ranking Based on Usage Information: Some services such as
DirectHit collect information on:
(a) the queries individual users submit to search services and (b) the
pages they look at subsequently and the time spent on each page. This
information is used to return pages that most users visit after
deploying the given query. For this technique to succeed a large
amount of data needs to be collected for each query. Thus, the
potential set of queries on which this technique applies is small.
Also, this technique is open to spamming.
Ranking Based on Connectivity: This approach involves
analyzing the hyperlinks between pages on the web on the assumption
that: (a) pages on the topic link to each other, and (b) authoritative
pages tend to point to other authoritative pages.
PageRank [Page et al 98] is an algorithm to rank pages based
on assumption b. It computes a query-independent authority
score for every page on the Web and uses this score to rank the result
set. Since PageRank is query-independent it cannot by itself
distinguish between pages that are authoritative in general and pages
that are authoritative on the query topic. In particular a web-site
that is authoritative in general may contain a page that
matches a certain query but is not an authority on the topic of the
query. In particular, such a page may not be considered valuable
within the community of users who author pages on the topic of the
query.
An alternative to PageRank is Topic Distillation
[Kleinberg 97, Chakrabarti et al 98, Bharat et al 98, Chakrabarti et
al 99]. Topic distillation first computes a query specific subgraph of
the WWW. This is done by including pages on the query topic in the
graph and ignoring pages not on the topic. Then the algorithm computes
a score for every page in the subgraph based on hyperlink
connectivity: every page is given an authority score. This score is
computed by summing the weights of all incoming links to the page. For
each such reference, its weight is computed by evaluating how good a
source of links the referring page is. Unlike PageRank,
Topic Distillation is only applicable to broad queries, since
it requires the presence of a community of pages on the topic.
A problem with Topic Distillation is that computing the
subgraph of the WWW which is on the query topic is hard to do in
real-time. In the ideal case every page on the WWW that deals with the
query topic would need to be considered. In practice an approximation
is used. A preliminary ranking for the query is done with content
analysis. The top ranked result pages for the query are selected. This
creates a selected set. Then, some of the pages within one or
two links from the selected set are also added to the selected set if
they are on the query topic. This approach can fail because it is
dependent on the comprehensiveness of the selected set for success. A
highly relevant and authoritative page may be omitted from the ranking
by this scheme if it either did not appear in the initial selected
set, or some of the pages pointing to it were not added to the
selected set. A "focused crawling" procedure to crawl the entire web
to find the complete subgraph on the query's topic has been proposed
[Chakrabarti et al 99] but this is too slow for online searching.
Also, the overhead in computing the full subgraph for the query is not
warranted since users only care about the top ranked results.
1.2 Hilltop Algorithm Overview
Our approach is based on the same assumptions as the other
connectivity algorithms, namely that the number and quality of the
sources referring to a page are a good measure of the page's quality.
The key difference consists in the fact that we are only considering
"expert" sources - pages that have been created with the specific
purpose of directing people towards resources. In response to a query,
we first compute a list of the most relevant experts on the query
topic. Then, we identify relevant links within the selected set of
experts, and follow them to identify target web pages. The targets are
then ranked according to the number and relevance of non-affiliated
experts that point to them. Thus, the score of a target page reflects
the collective opinion of the best independent experts on the query
topic. When such a pool of experts is not available, Hilltop provides
no results. Thus, Hilltop is tuned for result accuracy and not query
coverage.
Our algorithm consists of two broad phases:
(i) Expert Lookup
We define an expert page as a page that is about a certain topic
and has links to many non-affiliated pages on that topic. Two pages
are non-affiliated conceptually if they are authored by authors from
non-affiliated organizations. In a pre-processing step, a subset of
the pages crawled by a search engine are identified as experts. In our
experiment we classified 2.5 million of the 140 million or so pages in
AltaVista's index to be experts. The pages in this subset are indexed
in a special inverted index.
Given an input query, a lookup is done on the expert-index to find
and rank matching expert pages. This phase computes the best expert
pages on the query topic as well as associated match information.
(ii) Target Ranking
We believe a page is an authority on the query topic if and only if
some of the best experts on the query topic point to it. Of course in
practice some expert pages may be experts on a broader or related
topic. If so, only a subset of the hyperlinks on the expert page may
be relevant. In such cases the links being considered have to be
carefully chosen to ensure that their qualifying text matches the
query. By combining relevant out-links from many experts on the query
topic we can find the pages that are most highly regarded by the
community of pages related to the query topic. This is the basis of
the high relevance that our algorithm delivers.
Given the top ranked matching expert-pages and associated match
information, we select a subset of the hyperlinks within the
expert-pages. Specifically, we select links that we know to have all
the query terms associated with them. This implies that the link
matches the query. With further connectivity analysis on the selected
links we identify a subset of their targets as the top-ranked pages on
the query topic. The targets we identify are those that are linked to
by at least two non-affiliated expert pages on the topic. The
targets are ranked by a ranking score which is computed by combining
the scores of the experts pointing to the target.
1.3 Roadmap
The rest of the paper is organized as follows: Section 2 describes the
selection and indexing of expert documents; Section 3 provides a
detailed description of the ranking scheme used in query processing;
Section 4 presents a user-based evaluation of our prototype
implementation; and Section 5 concludes the paper.
2 Expert Documents
Broad subjects are well represented on the Web and as such are also
likely to have numerous human-generated lists of resources. There is
value for the individual or organization that creates resource lists
on specific topics since this boosts their popularity and influence
within the community interested in the topic. The authors of these
lists thus have an incentive to make their lists as comprehensive and
up to date as possible. We regard these links as recommendations, and
the pages that contain them, as experts. The problem is, how can we
distinguish an expert from other types of pages? In other words
what makes a page an expert? We felt than an expert page needs
to be objective and diverse: that is, its recommendations should be
unbiased and point to numerous non-affiliated pages on the
subject. Therefore, in order to find the experts, we needed to detect
when two sites belong to the same or related organizations.
2.1 Detecting Host Affiliation
We define two hosts as affiliated if one or both of the following is true:
-
They share the same first 3 octets of the IP address.
-
The rightmost non-generic token in the hostname is the same.
We consider tokens to be substrings of the hostname delimited by "."
(period). A suffix of the hostname is considered generic if it is a
sequence of tokens that occur in a large number of distinct hosts.
E.g., ".com" and ".co.uk" are domain names that occur in a large
number of hosts and are hence generic suffixes. Given two hosts, if
the generic suffix in each case is removed and the subsequent
right-most token is the same, we consider them to be affiliated.
E.g., in comparing "www.ibm.com" and "ibm.co.mx" we ignore the
generic suffixes ".com" and ".co.mx" respectively. The resulting
rightmost token is "ibm", which is the same in both cases. Hence they
are considered to be affiliated. Optionally, we could require the
generic suffix to be the same in both cases.
The affiliation relation is transitive: if A and B are affiliated
and B and C are affiliated then we take A and C to be affiliated even
if there is no direct evidence of the fact. In practice some
non-affiliated hosts may be classified as affiliated, but that is
acceptable since this relation is intended to be conservative.
In a preprocessing step we construct a host-affiliation lookup.
Using a union-find algorithm we group hosts, that either share the
same rightmost non-generic suffix or have an IP address in common,
into sets. Every set is given a unique identifier (e.g., the host with
the lexicographically lowest hostname). The host-affiliation lookup
maps every host to its set identifier or to itself (when there is no
set). This is used to compare hosts. If the lookup maps two hosts to
the same value then they are affiliated; otherwise they are
non-affiliated.
2.2 Selecting the Experts
In this step we process a search engine's database of pages (we used
AltaVista's crawl from April 1999) and select a subset of pages which
we consider to be good sources of links on specific topics, albeit
unknown. This is done as follows:
Considering all pages with out-degree greater than a threshold,
k (e.g., k=5) we test to see if these URLs point to k
distinct non-affiliated hosts. Every such page is considered an
expert page.
If a broad classification (such as Arts, Science,
Sports etc.) is known for every page in the search engine
database then we can additionally require that most of the k
non-affiliated URLs discovered in the previous step point to pages
that share the same broad classification. This allows us to
distinguish between random collections of links and resource
directories. Other properties of the page such as regularity in
formatting can be used as well.
2.3 Indexing the Experts
To locate expert pages that match user queries we create an inverted
index to map keywords to experts on which they occur. In doing so we
only index text contained within "key phrases" of the expert. A key
phrase is a piece of text that qualifies one or more URLs in the page.
Every key phrase has a scope within the document text. URLs located
within the scope of a phrase are said to be "qualified" by it. For
example, the title, headings (e.g., text within a pair of <H1>
</H1> tags) and anchor text within the expert page are
considered key phrases. The title has a scope that qualifies all URLs
in the document. A heading's scope qualifies all URLs until the next
heading of the same or greater importance. An anchor's scope only
extends over the URL it is associated with.
The inverted index is organized as a list of match positions within
experts. Each match position corresponds to an occurrence of a certain
keyword within a key phrase of a certain expert page. All match
positions for a given expert occur in sequence for a given keyword. At
every match position we also store:
-
An identifier to identify the phrase uniquely within the document
-
A code to denote the kind of phrase it is (title, heading or anchor)
-
The offset of the word within the phrase.
In addition, for every expert we maintain the list of URLs within it
(as indexes into a global list of URLs) and for each URL we maintain
the identifiers of the key phrases that qualify it.
To avoid giving long key phrases an advantage, the number of keywords
within any key phrase is limited (e.g., to 32).
3 Query Processing
In response to a user query, we first determine a list of N
experts that are the most relevant for that query. E.g. N = 200
in our experiment. Then, we rank results by selectively following the
relevant links from these experts and assigning an authority score to
each such page. In this section we describe how the expert and
authority scores are computed.
3.1 Computing the Expert Score
For an expert to be useful in response to a query, the minimum
requirement is that there is at least one URL which contains all the
query keywords in the key phrases that qualify it. A fast
approximation is to require all query keywords to occur in the
document. Furthermore, we assign to each candidate expert a score
reflecting the number and importance of the key phrases that contain
the query keywords, as well as the degree to which these phrases match
the query.
Thus, we compute the score of an expert as as a 3-tuple of the form
(S0, S1, S2).
Let k be the number of terms in the input query, q. The
component Si of the score is computed by considering
only key phrases that contain precisely k - i of the query
terms. E.g., S0 is the score computed from phrases
containing all the query terms.
Si = SUM{key phrases p with k - i
query terms} LevelScore(p) * FullnessFactor(p,
q)
LevelScore(p) is a score assigned to the phrase by virtue
of the type of phrase it is. For example, in our implementation we use
a LevelScore of 16 for title phrases, 6 for headings and 1 for anchor
text. This is based on the assumption that the title text is more useful
than the heading text, which is more useful than an anchor text match in
determining what the expert page is about.
FullnessFactor(p, q) is a measure of the number of
terms in p covered by the terms in q. Let plen be
the length of p. Let m be the number of terms in
p which are not in q (i.e., surplus terms in the
phrase). Then, FullnessFactor(p, q) is computed as
follows:
-
If m <= 2, FullnessFactor(p, q) = 1
-
If m > 2, FullnessFactor(p, q) = 1 - (m - 2) / plen
Our goal is to prefer experts that match all of the query keywords over
experts that match all but one of the keywords, and so on. Hence we rank
experts first by S0. We break ties by S1 and further
ties by S2. The score of each expert is converted to a scalar
by the weighted summation of the three components:
Expert_Score = 232 * S0 + 216
* S1 + S2.
3.2 Computing the Target Score
We consider the top N experts by the ranking from the previous step
(e.g., the top 200) and examine the pages they point to. These are called
targets.
It is from this set of targets that we select top ranked documents. For
a target to be considered it must be pointed to by at least 2 experts on
hosts that are mutually non-affiliated and are not affiliated to the target.
For all targets that qualify we compute a target score reflecting both
the number and relevance of the experts pointing to it and the
relevance of the phrases qualifying the links.
The target score T is computed in three steps:
-
For every expert E that points to target T we draw a directed
edge (E,T). Consider the following "qualification" relationship
between key phrases and edges:
-
The title phrase qualifies all edges coming out of the expert
-
A heading qualifies all edges whose corresponding hyperlinks occur
in the document after the given heading and before the next
heading of equal or greater importance.
-
A hyperlink's anchor text qualifies the edge corresponding to the
hyperlink.
For each query keyword w, let occ(w, T) be the number
of distinct key phrases in E that contain w and qualify
the edge (E,T). We define an "edge score" for the edge (E,T)
represented by Edge_Score(E,T), which is computed thus:
-
If occ(w, T) is 0 for any query keyword then the Edge_Score(E,T)
= 0.
-
Otherwise, Edge_Score(E,T) = Expert_Score(E)
* Sum{query keywords w} occ(w, T)
-
We next check for affiliations between expert pages that point to the same
target. If two affiliated experts have edges to the same target T,
we then discard one of the two edges. Specifically, we discard the edge
which has the lower Edge_Score of the two.
-
To compute the Target_Score of a target we sum the Edge_Scores
of all edges incident on it.
The list of targets is ranked by Target_Score. Optionally, this
list can be filtered by testing if the query keywords are present in the
targets. Optionally, we can match the query keywords against each target
to compute a Match_Score using content analysis, and combine the
Target_Score
with the Match_Score before ranking the targets.
Figure 1. Hilltop Ranking for the Query: "jobs"
4 Evaluation
In order to evaluate our prototype search engine, we conducted two user
studies aiming to estimate the recall and precision. Both experiments also
involved three other search engines, namely AltaVista, DirectHit
and Google, for comparison and were done in August 1999. Note that
the current rankings by these engines may differ.
4.1 Locating Specific Popular Targets
For the first experiment we asked seven volunteers to suggest the home
pages of ten organizations of their choice (companies, universities, stores,
etc.). Some of the queries are reproduced in the table below:
| Alpha Phi Omega |
Best Buy |
Digital |
Disneyland |
| Dollar Bank |
Grouplens |
INRIA |
Keebler |
| Mountain View Public Library |
Macy's |
Minneapolis City Pages |
Moscow Aviation Institute |
| MENSA |
OCDE |
ONU |
Pittsburg Steelers |
| Pizza Hut |
Rice University |
SONY |
Safeway |
| Stanford Shopping Center |
Trek Bicycle |
USTA |
Vanguard Investments |
The same query was sent to all four search engines. We assume that there
is exactly one home page in each case. Every time the home page was found
within the first ten results, its rank was recorded. Figure 2 summarizes
the average recall for the ranks 1 to 10 for each of the four engines:
our engine Hilltop (HT), Google (GG), AltaVista (AV),
and DirectHit (DH). Average recall at rank k for this experiment
is the probability of finding the desired home page within the first k
results.
Figure 2. Average Recall vs. Rank
Our engine performed well on these queries. Thus, for about 87%
of the queries, Hilltop returned the desired page as the first result,
comparable with Google at 80% of the queries, while DirectHit
and AltaVista succeeded at rank 1 only in 43% and 20% of the cases,
respectively. As we look at more results, the average recall increases
to 100% for Google, 97% for Hilltop, 83% for DirectHit,
and 30% for AltaVista.
4.2 Gathering Relevant Pages
In order to estimate Hilltop's ability to generate a good first page of
results for broad queries, we asked our volunteers to think of broad topics
(i.e., topics for which it is likely that many good pages exist) and formulate
queries. We collected 25 such queries, listed below:
| Aerosmith |
Amsterdam |
backgrounds |
chess |
dictionary |
| fashion |
freeware |
FTP search |
Godzilla |
Grand Theft Auto |
| greeting cards |
Jennifer Love Hewitt |
Las Vegas |
Louvre |
Madonna |
| MEDLINE |
MIDI |
newspapers |
Paris |
people search |
| real audio |
software |
Starr report |
tennis |
UFO |
We then used a script to submit each query to all four search
engines and collect the top 10 results from each engine, recording for
each result the URL, the rank, and the engine that found it. We needed
to determine which of the results were relevant in an unbiased manner.
For each query we generated the list of unique URLs in the union of
the results from all engines. This list was then presented to a judge
in a random order, without any information about the ranks of page or
their originating engine. The judge rated each page for relevance to
the given query on a binary scale (1 = "good page on the topic", 0 =
"not relevant or not found"). Then, another script combined these
ratings with the information about provenance and rank and computed
the average precision at rank k (for k = 1, 5, and 10).
The results are summarized in Figure 3.
Figure 3. Average Precision at Rank k
These results indicate that for broad subjects our engine returns a
large percentage of highly relevant pages among the ten best ranked
pages, comparable with Google and DirectHit, and better
than AltaVista. At rank 1 both Hilltop and
DirectHit have an average precision of 0.92. Average precision
at 10 for Hilltop was 0.77, roughly equal to the best search
engine, namely Google, with a precision of 0.79 at rank 10.
5 Conclusions
We described a new ranking algorithm for broad queries called
Hilltop and the implementation of a search engine based on it.
Given a broad query Hilltop generates a list of target pages
which are likely to be very authoritative pages on the topic of the
query. This is by virtue of the fact that they are highly valued by
pages on the WWW which address the topic of the query. In computing
the usefulness of a target page from the hyperlinks pointing to it, we
only consider links originating from pages that seem to be experts.
Experts in our definition are directories of links pointing to many
non-affiliated sites. This is an indication that these pages were
created for the purpose of directing users to resources, and hence we
regard their opinion as valuable. Additionally, in computing the level
of relevance, we require a match between the query and the text on the
expert page which qualifies the hyperlink being considered. This
ensures that hyperlinks being considered are on the query topic. For
further accuracy, we require that at least 2 non-affiliated experts
point to the returned page with relevant qualifying text describing
their linkage. The result of the steps described above is to generate
a listing of pages that are highly relevant to the user's query and of
high quality.
Hilltop most resembles the connectivity techniques,
PageRank and Topic Distillation. Unlike PageRank
our technique is a dynamic one and considers connectivity in a graph
specifically about the query topic. Hence, it can evaluate relevance
of content from the point of view of the community of authors
interested in the query topic. Unlike Topic Distillation we
enumerate and consider all good experts on the subject and
correspondingly all good target pages on the subject. In order to find
the most relevant experts we use a custom keyword-based approach,
focusing only on the text that best captures the domain of expertise
(the document title, section headings and hyperlink anchor-text).
Then, in following links, we boost the score of those targets whose
qualifying text best matches the query. Thus, by combining content and
connectivity analysis, we are both more comprehensive and more
precise. An important property is that unlike Topic
Distillation approaches, we can prove that if a page does
not appear in our output it lacks the connectivity support to justify
its inclusion. Thus we are less prone to omit good pages on the topic,
which is a problem with Topic Distillation systems. Also, since
we use an index optimized to finding experts, our implementation uses
less data than Topic Distillation and is therefore faster.
The indexing of anchor-text was first suggested in WWW Worm
[McBryan 94]. In some Topic Distillation systems such as
Clever [Chakrabarti et al 1998] and in the Google search
engine [Page et al 98] anchor-text is considered in evaluating a
link's relevance. We generalize this to other forms of text that are
seen to "qualify" a hyperlink at its source, and include headings and
title-text as well. Also, unlike Topic Distillation systems, we
evaluate experts on their content match to the user's query, rather
than on their linkage to good target pages. This prevents the scores
of "niche experts" (i.e., experts that point to new or relative poorly
connected pages) from being driven to zero, as is often the case in
Topic Distillation algorithms.
In a blind evaluation we found that Hilltop delivers a high
level of relevance given broad queries, and performs comparably to the
best of the commercial search engines tested.
6 References
[Kleinberg 97]
J. Kleinberg. Authoritative sources in a hyperlinked environment.
To appear in the Journal of the ACM, 1999. Also appears as
IBM Research Report RJ 10076, May 1997.
http://www.cs.cornell.edu/home/kleinber/auth.ps
[Chakrabarti et al 98]
S. Chakrabarti, B. Dom, D. Gibson, J. Kleinberg,
P. Raghavan, and S. Rajagopalan. Automatic Resource Compilation by Analyzing
Hyperlink Structure and Associated Text. Proceedings of the 7th World-Wide
Web conference, 1998.
http://decweb.ethz.ch/WWW7/1898/com1898.htm
[Chakrabarti et al 99]
S. Chakrabarti, M. van den Berg and B. Dom. Focused
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8th
World Wide Web Conference, Toronto, May 1999.
http://www.cs.berkeley.edu/~soumen/doc/www99focus/html/
[Bharat et al 98]
K. Bharat and M. Henzinger. Improved algorithms for
topic distillation in a hyperlinked environment. In SIGIR Conference
on Research and Development in Information Retrieval, volume 21. ACM,
1998. ftp://ftp.digital.com/pub/DEC/SRC/publications/monika/sigir98.pdf.
[Page et al 98]
S. Brin and L. Page. The anatomy of a large-scale hypertextual
web search engine. In WWW Conference, volume 7, 1998.
http://www7.scu.edu.au/programme/fullpapers/1921/com1921.htm
[McBryan 94] Oliver A. McBryan. GENVL and WWWW: Tools for Taming the
Web. First International Conference on the World Wide Web. CERN,
Geneva (Switzerland), May 25-26-27 1994.
http://www.cs.colorado.edu/home/mcbryan/mypapers/www94.ps
Krishna Bharat is a member of the research staff at Google Inc.
in Mountain View, California. Formerly he was at Compaq Computer
Corporation's Systems Research Center, which is where the research
described here was done. His research interests include Web content
discovery and retrieval, user interface issues in Web search and task
automation, and relevance assessments on the Web. He received his Ph.D.
in Computer Science from Georgia Institute of Technology in 1996,
where he worked on tool and infrastructure support for building
distributed user interface applications.
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George Andrei
Mihaila is a Ph.D. student in the Department of Computer
Science at the University of Toronto. During the summer of 1999 he was
an intern at Compaq Computer Corporation's Systems Research Center,
which is where this research was done. His research interests
include query languages, information discovery tools, Web-based
information systems and database integration. He received his M.Sc. in
Computer Science from the University of Toronto in 1996 with the
thesis WebSQL - an SQL-like Query Language for the World Wide
Web.
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