Web Search

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<strong>Web</strong> Pre-History• Ted Nelson developed idea of hypertext in 1965.• Doug Engelbart invented the mouse and built thefirst implementation of hypertext in the late1960’s at SRI.• ARPANET was developed in the early 1970’s.• The basic technology was in place in the 1970’s;but it took the PC revolution and widespreadnetworking to inspire the web and make itpractical.3
<strong>Web</strong> Browser History• Early browsers were developed in 1992 (Erwise,ViolaWWW).• In 1993, Marc Andreessen and Eric Bina at UIUCNCSA developed the Mosaic browser anddistributed it widely.• Andreessen joined with James Clark (StanfordProf. and Silicon Graphics founder) to formMosaic Communications Inc. in 1994 (whichbecame Netscape to avoid conflict with UIUC).• Microsoft licensed the original Mosaic from UIUCand used it to build Internet Explorer in 1995.4
Page 1 and 2: Web SearchIntroduction1
Page 3: The World Wide Web• Developed by
Page 7 and 8: Web Search History• In 1993, earl
Page 9 and 10: Web Search Recent History• In 199
Page 11 and 12: Number of Web Pages IndexedSearchEn
Page 13 and 14: Graph Structure in the Webhttp://ww
Page 15 and 16: Web SearchSpidering1
Page 17 and 18: Spiders (Robots/Bots/Crawlers)• S
Page 19 and 20: Search Strategies (cont)Depth-first
Page 21 and 22: Avoiding Page Duplication• Must d
Page 23 and 24: Queueing Strategy• How new links
Page 25 and 26: Link Extraction• Must find all li
Page 27 and 28: Anchor Text Indexing• Extract anc
Page 29 and 30: Robots META Tag• Include META tag
Page 31 and 32: Robot Exclusion Issues• META tag
Page 33 and 34: Directed/Focused Spidering• Sort
Page 35 and 36: Link-Directed Spidering• Monitor
Page 37 and 38: Boolean and Vector SpaceRetrieval M
Page 39 and 40: Retrieval Models• A retrieval mod
Page 41 and 42: Other Model Dimensions• Logical V
Page 43 and 44: Common Preprocessing Steps• Strip
Page 45 and 46: Boolean Retrieval Model• Popular
Page 47 and 48: Boolean Models − Problems• Very
Page 49 and 50: Statistical Retrieval• Retrieval
Page 51 and 52: The Vector-Space Model• Assume t
Page 53 and 54: Document Collection• A collection
Page 55 and 56:
Term Weights: Inverse Document Freq
Page 57 and 58:
Computing TF-IDF -- An ExampleGiven
Page 59 and 60:
Similarity Measure• A similarity
Page 61 and 62:
Properties of Inner Product• The
Page 63 and 64:
Cosine Similarity Measure• Cosine
Page 65:
Comments on Vector Space Models•
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