SLIDE 1IS 240 – Spring 2007 Prof. Ray Larson University of California, Berkeley School of Information Tuesday and Thursday 10:30 am - 12:00 pm Spring Principles of Information Retrieval Lecture 22: Web Searching

SLIDE 2IS 240 – Spring 2007 Today Review –XML Retrieval and fusion approaches Web Crawling and Search Issues –Web Crawling –Web Search Engines and Algorithms Credit for some of the slides in this lecture goes to Marti Hearst

SLIDE 3IS 240 – Spring 2007 Cheshire SGML/XML Support Underlying native format for all data is SGML or XML The DTD defines the database contents Full SGML/XML parsing SGML/XML Format Configuration Files define the database location and indexes Various format conversions and utilities available for Z39.50 support (MARC, GRS-1

SLIDE 4IS 240 – Spring 2007 SGML/XML Support Configuration files for the Server are SGML/XML: –They include elements describing all of the data files and indexes for the database. –They also include instructions on how data is to be extracted for indexing and how Z39.50 attributes map to the indexes for a given database.

SLIDE 5IS 240 – Spring 2007 Indexing Any SGML/XML tagged field or attribute can be indexed: –B-Tree and Hash access via Berkeley DB (Sleepycat) –Stemming, keyword, exact keys and “special keys” –Mapping from any Z39.50 Attribute combination to a specific index –Underlying postings information includes term frequency for probabilistic searching Component extraction with separate component indexes

SLIDE 6IS 240 – Spring 2007 XML Element Extraction A new search “ElementSetName” is XML_ELEMENT_ Any Xpath, element name, or regular expression can be included following the final underscore when submitting a present request The matching elements are extracted from the records matching the search and delivered in a simple format..

SLIDE 7IS 240 – Spring 2007 XML Extraction % zselect sherlock 372 {Connection with SHERLOCK (sherlock.berkeley.edu) database 'bibfile' at port 2100 is open as connection #372} % zfind topic mathematics {OK {Status 1} {Hits 26} {Received 0} {Set Default} {RecordSyntax UNKNOWN}} % zset recsyntax XML % zset elementset XML_ELEMENT_Fld245 % zdisplay {OK {Status 0} {Received 10} {Position 1} {Set Default} {NextPosition 11} {RecordSyntax XML }} { Singularitâes áa Cargáese … etc…

SLIDE 8IS 240 – Spring 2007 Probability of relevance is based on Logistic regression from a sample set of documents to determine values of the coefficients. At retrieval the probability estimate is obtained by: For the 6 X attribute measures shown on the next slide TREC3 Logistic Regression

SLIDE 9IS 240 – Spring 2007 TREC3 Logistic Regression Average Absolute Query Frequency Query Length Average Absolute Component Frequency Document Length Average Inverse Component Frequency Number of Terms in both query and Component

SLIDE 10IS 240 – Spring 2007 Okapi BM25 Where: Q is a query containing terms T K is k 1 ((1-b) + b.dl/avdl) k 1, b and k 3 are parameters, usually 1.2, 0.75 and tf is the frequency of the term in a specific document qtf is the frequency of the term in a topic from which Q was derived dl and avdl are the document length and the average document length measured in some convenient unit w (1) is the Robertson-Sparck Jones weight.

SLIDE 11IS 240 – Spring 2007 Combining Boolean and Probabilistic Search Elements Two original approaches: –Boolean Approach –Non-probabilistic “Fusion Search” Set merger approach is a weighted merger of document scores from separate Boolean and Probabilistic queries

SLIDE 12IS 240 – Spring 2007 Subquery INEX ‘04 Fusion Search Merge multiple ranked and Boolean index searches within each query and multiple component search resultsets –Major components merged are Articles, Body, Sections, subsections, paragraphs Subquery Comp. Query Results Comp. Query Results Fusion/ Merge Final Ranked List

SLIDE 13IS 240 – Spring 2007 Merging and Ranking Operators Extends the capabilities of merging to include merger operations in queries like Boolean operators Fuzzy Logic Operators (not used for INEX) –!FUZZY_AND –!FUZZY_OR –!FUZZY_NOT Containment operators: Restrict components to or with a particular parent –!RESTRICT_FROM –!RESTRICT_TO Merge Operators –!MERGE_SUM –!MERGE_MEAN –!MERGE_NORM –!MERGE_CMBZ

SLIDE 14IS 240 – Spring 2007 Today Review –XML Retrieval and fusion approaches Web Crawling and Search Issues –Web Crawling –Web Search Engines and Algorithms Credit for some of the slides in this lecture goes to Marti Hearst

SLIDE 15IS 240 – Spring 2007 Standard Web Search Engine Architecture crawl the web create an inverted index Check for duplicates, store the documents Inverted index Search engine servers user query Show results To user DocIds

SLIDE 16IS 240 – Spring 2007 Standard Web Search Engine Architecture crawl the web create an inverted index Check for duplicates, store the documents Inverted index Search engine servers user query Show results To user DocIds

SLIDE 17IS 240 – Spring 2007 Web Crawling How do the web search engines get all of the items they index? Main idea: –Start with known sites –Record information for these sites –Follow the links from each site –Record information found at new sites –Repeat

SLIDE 18IS 240 – Spring 2007 Web Crawlers How do the web search engines get all of the items they index? More precisely: –Put a set of known sites on a queue –Repeat the following until the queue is empty: Take the first page off of the queue If this page has not yet been processed: –Record the information found on this page »Positions of words, links going out, etc –Add each link on the current page to the queue –Record that this page has been processed In what order should the links be followed?

SLIDE 19IS 240 – Spring 2007 Page Visit Order Animated examples of breadth-first vs depth-first search on trees: – Structure to be traversed

SLIDE 20IS 240 – Spring 2007 Page Visit Order Animated examples of breadth-first vs depth-first search on trees: – Breadth-first search (must be in presentation mode to see this animation)

SLIDE 21IS 240 – Spring 2007 Page Visit Order Animated examples of breadth-first vs depth-first search on trees: – Depth-first search (must be in presentation mode to see this animation)

SLIDE 22IS 240 – Spring 2007 Page Visit Order Animated examples of breadth-first vs depth-first search on trees:

SLIDE 23IS 240 – Spring 2007 Sites Are Complex Graphs, Not Just Trees Page 1 Page 3 Page 2 Page 1 Page 2 Page 1 Page 5 Page 6 Page 4 Page 1 Page 2 Page 1 Page 3 Site 6 Site 5 Site 3 Site 1 Site 2

SLIDE 24IS 240 – Spring 2007 Web Crawling Issues Keep out signs –A file called robots.txt tells the crawler which directories are off limits Freshness –Figure out which pages change often –Recrawl these often Duplicates, virtual hosts, etc –Convert page contents with a hash function –Compare new pages to the hash table Lots of problems –Server unavailable –Incorrect html –Missing links –Infinite loops Web crawling is difficult to do robustly!

SLIDE 25IS 240 – Spring 2007 Today Review –Geographic Information Retrieval –GIR Algorithms and evaluation based on a presentation to the 2004 European Conference on Digital Libraries, held in Bath, U.K. Web Crawling and Search Issues –Web Crawling –Web Search Engines and Algorithms Credit for some of the slides in this lecture goes to Marti Hearst

SLIDE 26IS 240 – Spring 2007 Searching the Web Web Directories versus Search Engines Some statistics about Web searching Challenges for Web Searching Search Engines –Crawling –Indexing –Querying Note: This is full of old data. The search companies are less forthcoming with exact numbers than they were a few years ago.

SLIDE 27IS 240 – Spring 2007 Directories vs. Search Engines Directories –Hand-selected sites –Search over the contents of the descriptions of the pages –Organized in advance into categories Search Engines –All pages in all sites –Search over the contents of the pages themselves –Organized after the query by relevance rankings or other scores

SLIDE 28IS 240 – Spring 2007 Search Engines vs. Internal Engines Not long ago HotBot, GoTo, Yahoo and Microsoft were all powered by Inktomi Today Google is the search engine behind many other search services

SLIDE 29IS 240 – Spring 2007 Statistics from Inktomi Statistics from Inktomi, August 2000, for one client, one week –Total # queries: –Number of repeated queries: –Number of queries with repeated words: –Average words/ query: 2.39 –Query type: All words: ; Any words: ; Some words: –Boolean: ( AND / OR / NOT) –Phrase searches: –URL searches: –URL searches w/http: – searches: –Wildcards: ( '?'s ) frac '?' at end of query: interrogatives when '?' at end: composed of: –who: what: when: why: how: where where-MIS can,etc.: do(es)/did: 0.0

SLIDE 30IS 240 – Spring 2007 What Do People Search for on the Web? Topics –Genealogy/Public Figure:12% –Computer related:12% –Business:12% –Entertainment: 8% –Medical: 8% –Politics & Government 7% –News 7% –Hobbies 6% –General info/surfing 6% –Science 6% –Travel 5% –Arts/education/shopping/images 14% (from Spink et al. 98 study)

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SLIDE 33 Searches Per Day (2000)

SLIDE 34IS 240 – Spring 2007 Searches Per Day (2001)

SLIDE 35IS 240 – Spring 2007 Searches per day (current) Don’t have exact numbers for Google, but they have stated in their “press” section that they handle 200 Million searches per day They index over 8 Billion web pages – Now just says “Billions” - maybe they have stopped counting…

SLIDE 36IS 240 – Spring 2007 Challenges for Web Searching: Data Distributed data Volatile data/”Freshness”: 40% of the web changes every month Exponential growth Unstructured and redundant data: 30% of web pages are near duplicates Unedited data Multiple formats Commercial biases Hidden data

SLIDE 37IS 240 – Spring 2007 Challenges for Web Searching: Users Users unfamiliar with search engine interfaces (e.g., Does the query “apples oranges” mean the same thing on all of the search engines?) Users unfamiliar with the logical view of the data (e.g., Is a search for “Oranges” the same things as a search for “oranges”?) Many different kinds of users

SLIDE 38IS 240 – Spring 2007 Web Search Queries Web search queries are SHORT –~2.4 words on average (Aug 2000) –Has increased, was 1.7 (~1997) User Expectations –Many say “the first item shown should be what I want to see”! –This works if the user has the most popular/common notion in mind

SLIDE 39IS 240 – Spring 2007 Search Engines Crawling Indexing Querying

SLIDE 40IS 240 – Spring 2007 Web Search Engine Layers From description of the FAST search engine, by Knut Risvik

SLIDE 41IS 240 – Spring 2007 Standard Web Search Engine Architecture crawl the web create an inverted index Check for duplicates, store the documents Inverted index Search engine servers user query Show results To user DocIds

SLIDE 42IS 240 – Spring 2007 More detailed architecture, from Brin & Page 98. Only covers the preprocessing in detail, not the query serving.

SLIDE 43IS 240 – Spring 2007 Indexes for Web Search Engines Inverted indexes are still used, even though the web is so huge Most current web search systems partition the indexes across different machines –Each machine handles different parts of the data (Google uses thousands of PC-class processors and keeps most things in main memory) Other systems duplicate the data across many machines –Queries are distributed among the machines Most do a combination of these

SLIDE 44IS 240 – Spring 2007 Search Engine Querying In this example, the data for the pages is partitioned across machines. Additionally, each partition is allocated multiple machines to handle the queries. Each row can handle 120 queries per second Each column can handle 7M pages To handle more queries, add another row. From description of the FAST search engine, by Knut Risvik

SLIDE 45IS 240 – Spring 2007 Querying: Cascading Allocation of CPUs A variation on this that produces a cost- savings: –Put high-quality/common pages on many machines –Put lower quality/less common pages on fewer machines –Query goes to high quality machines first –If no hits found there, go to other machines

SLIDE 46IS 240 – Spring 2007 Google Google maintains (probably) the worlds largest Linux cluster (over 15,000 servers) These are partitioned between index servers and page servers –Index servers resolve the queries (massively parallel processing) –Page servers deliver the results of the queries Over 8 Billion web pages are indexed and served by Google

SLIDE 47IS 240 – Spring 2007 Search Engine Indexes Starting Points for Users include Manually compiled lists –Directories Page “popularity” –Frequently visited pages (in general) –Frequently visited pages as a result of a query Link “co-citation” –Which sites are linked to by other sites?

SLIDE 48IS 240 – Spring 2007 Starting Points: What is Really Being Used? Todays search engines combine these methods in various ways –Integration of Directories Today most web search engines integrate categories into the results listings Lycos, MSN, Google –Link analysis Google uses it; others are also using it Words on the links seems to be especially useful –Page popularity Many use DirectHit’s popularity rankings

SLIDE 49IS 240 – Spring 2007 Web Page Ranking Varies by search engine –Pretty messy in many cases –Details usually proprietary and fluctuating Combining subsets of: –Term frequencies –Term proximities –Term position (title, top of page, etc) –Term characteristics (boldface, capitalized, etc) –Link analysis information –Category information –Popularity information

SLIDE 50IS 240 – Spring 2007 Ranking: Hearst ‘96 Proximity search can help get high- precision results if >1 term –Combine Boolean and passage-level proximity –Proves significant improvements when retrieving top 5, 10, 20, 30 documents –Results reproduced by Mitra et al. 98 –Google uses something similar

SLIDE 51IS 240 – Spring 2007 Ranking: Link Analysis Assumptions: –If the pages pointing to this page are good, then this is also a good page –The words on the links pointing to this page are useful indicators of what this page is about –References: Page et al. 98, Kleinberg 98

SLIDE 52IS 240 – Spring 2007 Ranking: Link Analysis Why does this work? –The official Toyota site will be linked to by lots of other official (or high-quality) sites –The best Toyota fan-club site probably also has many links pointing to it –Less high-quality sites do not have as many high-quality sites linking to them

SLIDE 53IS 240 – Spring 2007 Ranking: PageRank Google uses the PageRank 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. d is usually set to 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

SLIDE 54IS 240 – Spring 2007 PageRank T2Pr=1 T1Pr=.725 T6Pr=1 T5Pr=1 T4Pr=1 T3Pr=1 T7Pr=1 T8Pr= X1 X2 APr= Note: these are not real PageRanks, since they include values >= 1

SLIDE 55IS 240 – Spring 2007 PageRank Similar to calculations used in scientific citation analysis (e.g., Garfield et al.) and social network analysis (e.g., Waserman et al.) Similar to other work on ranking (e.g., the hubs and authorities of Kleinberg et al.) How is Amazon similar to Google in terms of the basic insights and techniques of PageRank? How could PageRank be applied to other problems and domains?

SLIDE 56IS 240 – Spring 2007 Other Issues and Approaches Eric Brewer on Search Engine vs Database Technology: – acad/brewer