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Web Information retrieval (Web IR)

Handout #1:Web characteristics

Ali Mohammad Zareh BidokiECE Department, Yazd University

alizareh@yaduni.ac.ir

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Outline

• Web challenges• SE & Web IR challenges• Web Structure (Graph)• Web characteristics• Zip law

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Web Challenges

• Huge size of information– 11.5 billions pages (2005)– 64 billions pages (05 June, 2008)

• Proliferation and dynamic nature– New pages are created at the rate of 8% per week– Only 20% of the current pages will be accessible after one

year – New links are created at rate 25% per week

• Heterogeneous contents– HTML/Text/Audio/…

• Users of web are growing exponentially

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What is the success reason of the Web?

• A distributed system• A simple protocol• Production and generation is very

simple

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Information Retrieval Definition

• IR deals with the representation, storage, organization of, and access to information items (relevant to user query)

• Information retrieval (IR) is the science of searching for documents, for information within documents, and for metadata about documents

• An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines.

• In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevancy.

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Web Retrieval

User Space

User Space

Information Space

Information Space

Matching

RetrievalBrowsing

Index termsFull text

Full text + Structure (e.g. hypertext)

Search Engine

Search engine is an IR system!

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IR vs Data Retrieval

• A data retrieval aims at retrieving all objects which satisfy clearly defined conditions in regular expression

• DR does not solve the problem of retrieving information about subject or object

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Comparing IR to databases (vs data

retrieval)

Databases IR

Data Structured Unstructured

Fields Clear semantics (SSN, age)

No fields (other than text)

QueriesDefined (relational algebra, SQL)

Free text (“natural language”), Boolean

Query specification

Complete Incomplete

MatchingExact (results are always “correct”)

Imprecise (need to measure effectiveness)

Error response

Sensitive Insensitive

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Main points in IR

• What is the definition of relevancy?• Evaluation!

– Subjective (opposite to hardware, network)

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Web IR (SE) Challenges (1)

• The definition of Relevancy• The connectivity with content in Web

– A huge graph

• Different type of Queries– Narrow

• Needle in a haystack– Wide

• Overlapping with many areas• User have Poor patience: they commonly browse

through the first ten results (i.e. one screen) hoping to find there the “right” document for their query

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Web IR (SE) Challenges (2)

• Spamming phenomenon– it is crucial for business sites to be ranked highly by

the major search engines. – There are quite a few companies who sell this kind of

expertise (also known as “search engine optimization”) and actively research ranking algorithms and heuristics of search engines, and know how many keywords to place (and where) in a Web page so as to improve the page’s ranking

– SEO Books

• Content & Connectivity Spamming• Anti Spamming solutions

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Web IR (SE) Challenges (3)

• Rich-get-richer problem– It takes a long time for a young high quality web

pages to receive an appropriate quality– Unfairness– Bad directions in growing web contents

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Web IR (SE) Challenges (4)

• Crawling challenges– Huge size of information with dynamic nature– Freshness & converge

• Google covers only 70% of the Web

– An suitable scheduling policy– Hidden web (600 times bigger)

• Using meta search engines to increase coverage– Merging and ranking problem

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Web IR (SE) Challenges (5)

• User evaluation is subjective and changes in time– Relevancy between a query and document depends

on user and time– Two users with the same query expect different

results

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Web IR (SE) Challenges (6)

• Query Ambiguity– Python– Car & automobile

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Web Dynamics

• For each page p and each visit, the following information is available:– The access time-stamp of the page: visitp.– The last-modified time-stamp (given by mostWeb servers;

about 80%-90%of the requests in practice): modifiedp.– The text of the page, which can be compared to an older

copy to detect changes, especially if modifiedp– is not provided.– The following information can be estimated if the re-

visiting period is short:– The time at which the page first appeared: createdp.– The time at which the page was no longer reachable:

deletedp

• In all cases, the results are only an estimation of the actual values

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Estimating freshness and age

• The probability that a copy of p is up-to-date at time t, up(t) decreases with time if the page is not re-visited.

• When page changes are modeled as a Poisson process, if t units of time have passed since the last visit, then:

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Characterization of Web page changes

• Age: visitp-modifiedp.• Lifespan: deletedp-createdp.• Number of changes during the lifespan:

changesp.• Average change interval:

lifespanp/changesp.

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Freshness && Age

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Web a Scale Free Network

• A scale-free network is characterized by a few highly-linked nodes that act as “hubs” connecting several nodes to the network.

• It follows Power Law

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Random Vs Scale-Free

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Distribution of Web Graph: Power-Law

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Power-Law and Zipf Law

xxP )(

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Zipf Law for Content

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Macroscopic Structure of Web

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User Sessions

• User sessions on the Web are usually characterized through models of random surfers

• The most used source for data about the browsing activities of users are the access log files of Web Servers, Proxies, SEs– Caching

• Modeling User behavior• Eye tracking

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Next Lecture

• Information Retrieval Models– Boolean– Vector Space– Realistic