WebSpidering

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Information Retrieval and Web Search: 

Information Retrieval and Web Search Web search. Spidering Instructor: Rada Mihalcea Invited Lecturer: Andras Csomai Class web page: http://lit.csci.unt.edu/~classes/CSCE5200/ (some of these slides were adapted from Ray Mooney’s IR course at UT Austin)

Today’s Topics: 

Today’s Topics Information Retrieval applied on the Web Web Search Spidering

Web Challenges for IR: 

Web Challenges for IR Distributed Data: Documents spread over millions of different web servers. Volatile Data: Many documents change or disappear rapidly (e.g. dead links). Large Volume: Billions of separate documents. Unstructured and Redundant Data: No uniform structure, HTML errors, up to 30% (near) duplicate documents. Quality of Data: No editorial control, false information, poor quality writing, typos, etc. Heterogeneous Data: Multiple media types (images, video, VRML), languages, character sets, etc.

The Web (Corpus) by the Numbers (1): 

The Web (Corpus) by the Numbers (1) 43 million web servers 167 Terabytes of data About 20% text/html 100 Terabytes in “deep Web” 440 Terabytes in emails Original content [Lyman & Varian: How much Information? 2003] http://www.sims.berkeley.edu/research/projects/how-much-info-2003/

The Web (Corpus) by the Numbers (2): 

The Web (Corpus) by the Numbers (2)

Zipf’s Law on the Web: 

Zipf’s Law on the Web Length of web pages has a Zipfian distribution. Number of hits to a web page has a Zipfian distribution.

Web Search Using IR: 

Web Search Using IR the spider represents the main difference compared to traditional IR

Spiders (Robots/Bots/Crawlers): 

Spiders (Robots/Bots/Crawlers) Start with a comprehensive set of root URL’s from which to start the search. Follow all links on these pages recursively to find additional pages. Index/Process all novel found pages in an inverted index as they are encountered. May allow users to directly submit pages to be indexed (and crawled from). You’ll need to build a simple spider for Assignment 1 to traverse the UNT webpages.

Search Strategies: 

Search Strategies Breadth-first Search

Search Strategies (cont): 

Search Strategies (cont) Depth-first Search

Search Strategy Trade-Off’s: 

Search Strategy Trade-Off’s Breadth-first explores uniformly outward from the root page but requires memory of all nodes on the previous level (exponential in depth). Standard spidering method. Depth-first requires memory of only depth times branching-factor (linear in depth) but gets “lost” pursuing a single thread. Both strategies can be easily implemented using a queue of links (URL’s).

Avoiding Page Duplication: 

Avoiding Page Duplication Must detect when revisiting a page that has already been spidered (web is a graph not a tree). Must efficiently index visited pages to allow rapid recognition test. Tree indexing (e.g. trie) Hashtable Index page using URL as a key. Must canonicalize URL’s (e.g. delete ending “/”) Not detect duplicated or mirrored pages. Index page using textual content as a key. Requires first downloading page.

Spidering Algorithm: 

Spidering Algorithm Initialize queue (Q) with initial set of known URL’s. Until Q empty or page or time limit exhausted: Pop URL, L, from front of Q. If L is not to an HTML page (.gif, .jpeg, .ps, .pdf, .ppt…) continue loop. If already visited L, continue loop. Download page, P, for L. If cannot download P (e.g. 404 error, robot excluded) continue loop. Index P (e.g. add to inverted index or store cached copy). Parse P to obtain list of new links N. Append N to the end of Q.

Queueing Strategy: 

Queueing Strategy How new links are added to the queue determines search strategy. FIFO (append to end of Q) gives breadth-first search. LIFO (add to front of Q) gives depth-first search. Heuristically ordering the Q gives a “focused crawler” that directs its search towards “interesting” pages.

Restricting Spidering: 

Restricting Spidering You can restrict spider to a particular site. Remove links to other sites from Q. You can restrict spider to a particular directory. Remove links not in the specified directory. Obey page-owner restrictions (robot exclusion).

Link Extraction: 

Link Extraction Must find all links in a page and extract URLs. <a href=“http://www.cs.unt.edu/~rada/CSCE5300”> <frame src=“site-index.html”> Must complete relative URL’s using current page URL: <a href=“proj3”> to http://www.cs.unt.edu/~rada/CSCE5300/proj3 <a href=“../cs5343/syllabus.html”> to http://www.cs.unt.edu/rada/cs5343/syllabus.html

URL Syntax: 

URL Syntax A URL has the following syntax: <scheme>://<authority><path>?<query>#<fragment> A query passes variable values from an HTML form and has the syntax: <variable>=<value>&<variable>=<value>… A fragment is also called a reference or a ref and is a pointer within the document to a point specified by an anchor tag of the form: <A NAME=“<fragment>”>

Link Canonicalization: 

Link Canonicalization Equivalent variations of ending directory normalized by removing ending slash. http://www.cs.unt.edu/~rada/ http://www.cs.unt.edu/~rada Internal page fragments (ref’s) removed: http://www.cs.unt.edu/~rada/welcome.html#courses http://www.cs.unt.edu/~rada/welcome.html

Anchor Text Indexing: 

Anchor Text Indexing Extract anchor text (between <a> and </a>) of each link followed. Anchor text is usually descriptive of the document to which it points. Add anchor text to the content of the destination page to provide additional relevant keyword indices. Used by Google: <a href=“http://www.microsoft.com”>Evil Empire</a> <a href=“http://www.ibm.com”>IBM</a>

Anchor Text Indexing (cont’d): 

Anchor Text Indexing (cont’d) Helps when descriptive text in destination page is embedded in image logos rather than in accessible text. Many times anchor text is not useful: “click here” Increases content more for popular pages with many in-coming links, increasing recall of these pages. May even give higher weights to tokens from anchor text.

Robot Exclusion: 

Robot Exclusion Web sites and pages can specify that robots should not crawl/index certain areas. Two components: Robots Exclusion Protocol: Site wide specification of excluded directories. Robots META Tag: Individual document tag to exclude indexing or following links.

Robots Exclusion Protocol: 

Robots Exclusion Protocol Site administrator puts a “robots.txt” file at the root of the host’s web directory. http://www.ebay.com/robots.txt http://www.cnn.com/robots.txt File is a list of excluded directories for a given robot (user-agent). Exclude all robots from the entire site: User-agent: * Disallow: /

Robot Exclusion Protocol Examples: 

Robot Exclusion Protocol Examples Exclude specific directories: User-agent: * Disallow: /tmp/ Disallow: /cgi-bin/ Disallow: /users/paranoid/ Exclude a specific robot: User-agent: GoogleBot Disallow: / Allow a specific robot: User-agent: GoogleBot Disallow:

Robot Exclusion Protocol Details: 

Robot Exclusion Protocol Details Only use blank lines to separate different User-agent disallowed directories. One directory per “Disallow” line. No regex patterns in directories.

Robots META Tag: 

Robots META Tag Include META tag in HEAD section of a specific HTML document. <meta name=“robots” content=“none”> Content value is a pair of values for two aspects: index | noindex: Allow/disallow indexing of this page. follow | nofollow: Allow/disallow following links on this page.

Robots META Tag (cont): 

Robots META Tag (cont) Special values: all = index,follow none = noindex,nofollow Examples: <meta name=“robots” content=“noindex,follow”> <meta name=“robots” content=“index,nofollow”> <meta name=“robots” content=“none”>

Robot Exclusion Issues: 

Robot Exclusion Issues META tag is newer and less well-adopted than “robots.txt”. Standards are conventions to be followed by “good robots.” Companies have been prosecuted for “disobeying” these conventions and “trespassing” on private cyberspace.

Multi-Threaded Spidering: 

Multi-Threaded Spidering Bottleneck is network delay in downloading individual pages. Best to have multiple threads running in parallel each requesting a page from a different host. Distribute URL’s to threads to guarantee equitable distribution of requests across different hosts to maximize through-put and avoid overloading any single server. Early Google spider had multiple co-ordinated crawlers with about 300 threads each, together able to download over 100 pages per second.

Directed/Focused Spidering: 

Directed/Focused Spidering Sort queue to explore more “interesting” pages first. Two styles of focus: Topic-Directed Link-Directed

Topic-Directed Spidering: 

Topic-Directed Spidering Assume desired topic description or sample pages of interest are given. Sort queue of links by the similarity (e.g. cosine metric) of their source pages and/or anchor text to this topic description. Related to Topic Tracking and Detection

Link-Directed Spidering: 

Link-Directed Spidering Monitor links and keep track of in-degree and out-degree of each page encountered. Sort queue to prefer popular pages with many in-coming links (authorities). Sort queue to prefer summary pages with many out-going links (hubs). Google’s PageRank algorithm

Keeping Spidered Pages Up to Date: 

Keeping Spidered Pages Up to Date Web is very dynamic: many new pages, updated pages, deleted pages, etc. Periodically check spidered pages for updates and deletions: Just look at header info (e.g. META tags on last update) to determine if page has changed, only reload entire page if needed. Track how often each page is updated and preferentially return to pages which are historically more dynamic. Preferentially update pages that are accessed more often to optimize freshness of more popular pages.

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