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Crawling the Hidden Web by Michael Weinberg Internet DB Seminar, The Hebrew University of Jerusalem, School of Computer Science and.

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Presentation on theme: "Crawling the Hidden Web by Michael Weinberg Internet DB Seminar, The Hebrew University of Jerusalem, School of Computer Science and."— Presentation transcript:

1 Crawling the Hidden Web by Michael Weinberg Internet DB Seminar, The Hebrew University of Jerusalem, School of Computer Science and Engineering, December 2001

2 23/12/2001Michael Weinberg, SDBI Seminar2 Agenda Hidden Web - what is it all about? Generic model for a hidden Web crawler HiWE (Hidden Web Exposer) LIE T LITE – Layout-based Information Extraction Technique Results from experiments conducted to test these techniques

3 23/12/2001Michael Weinberg, SDBI Seminar3 Web Crawlers Automatically traverse the Web graph, building a local repository of the portion of the Web that they visit Traditionally, crawlers have only targeted a portion of the Web called the publicly indexable Web (PIW) PIW – the set of pages reachable purely by following hypertext links, ignoring search forms and pages that require authentication

4 23/12/2001Michael Weinberg, SDBI Seminar4 The Hidden Web Recent studies show that a significant fraction of Web content in fact lies outside the PIW Large portions of the Web are ‘hidden’ behind search forms in searchable databases HTML pages are dynamically generated in response to queries submitted via the search forms Also referred as the ‘Deep’ Web

5 23/12/2001Michael Weinberg, SDBI Seminar5 The Hidden Web Growth Hidden Web continues to grow, as organizations with large amount of high-quality information are placing their content online, providing web- accessible search facilities over existing databases For example: – Census Bureau – Patents and Trademarks Office – News media companies lists over 10000 such databases

6 23/12/2001Michael Weinberg, SDBI Seminar6 Surface Web

7 23/12/2001Michael Weinberg, SDBI Seminar7 Deep Web

8 23/12/2001Michael Weinberg, SDBI Seminar8 Deep Web Content Distribution

9 23/12/2001Michael Weinberg, SDBI Seminar9 Deep Web Stats 500 The Deep Web is 500 times larger than PIW !!! Contains 7,500 terabytes of information (March 2000) More than 200,000 Deep Web sites exist Sixty of the largest Deep Web sites collectively contain about 750 terabytes of information 95% of the Deep Web is publicly accessible (no fees) 0.03 Google indexes about 16% of the PIW, so we search about 0.03% of the pages available today

10 23/12/2001Michael Weinberg, SDBI Seminar10 The Problem Hidden Web contains large amounts of high- quality information The information is buried on dynamically generated sites Search engines that use traditional crawlers never find this information

11 23/12/2001Michael Weinberg, SDBI Seminar11 The Solution Build a hidden Web crawler Can crawl and extract content from hidden databases Enable indexing, analysis, and mining of hidden Web content The content extracted by such crawlers can be used to categorize and classify the hidden databases

12 23/12/2001Michael Weinberg, SDBI Seminar12 Challenges Significant technical challenges in designing a hidden Web crawler Should interact with forms that were designed primarily for human consumption Must provide input in the form of search queries How equip the crawlers with input values for use in constructing search queries? task-specifichuman-assisted To address these challenges, we adopt the task-specific, human-assisted approach

13 23/12/2001Michael Weinberg, SDBI Seminar13 Task-Specificity Extract content based on the requirements of a particular application or task For example, consider a market analyst interested in press releases, articles, etc… pertaining to the semiconductor industry, and dated sometime in the last ten years

14 23/12/2001Michael Weinberg, SDBI Seminar14 Human-Assistance Human-assistance is critical to ensure that the crawler issues queries that are relevant to the particular task For instance, in the semiconductor example, the market analyst may provide the crawler with lists of companies or products that are of interest The crawler will be able to gather additional potential company and product names as it processes a number of pages

15 23/12/2001Michael Weinberg, SDBI Seminar15 Two Steps There are two steps in achieving our goal: – Resource discovery – identify sites and databases that are likely to be relevant to the task – Content extraction – actually visit the identified sites to submit queries and extract the hidden pages In this presentation we do not directly address the resource discovery problem

16 23/12/2001Michael Weinberg, SDBI Seminar16 Hidden Web Crawlers

17 23/12/2001Michael Weinberg, SDBI Seminar17 User form interaction Form page Response page Web query front-end (3) Fill-out form (1) Download form (5) Download response (2) View form (4) Submit form (6) View result Hidden Database

18 23/12/2001Michael Weinberg, SDBI Seminar18 Operation Model Our model of a hidden Web crawler consists of four components: – Internal Form Representation – Task-specific database – Matching function – Response Analysis Form Page – the page containing the search form Response Page – the page received in response to a form submission

19 23/12/2001Michael Weinberg, SDBI Seminar19 Generic Operational Model Internal Form Representation Task specific database Set of value- assignments Response Analysis Hidden Web Crawler Form page Response page Web query front-end Match Hidden Database Repository Download form Form submission Download response Form analysis

20 23/12/2001Michael Weinberg, SDBI Seminar20 Internal Form Representation Form F: is a set of n form elements S – submission information associated with the form: – submission URL – Internal identifiers for each form element M – meta-information about the form: – web-site hosting the form – set of pages pointing to this form page – other text on the page besides the form

21 23/12/2001Michael Weinberg, SDBI Seminar21 Task-specific Database The crawler is equipped with a task-specific database D Contains the necessary information to formulate queries relevant to the particular task In the ‘market analyst’ example, D could contain list of semiconductor company and product names The actual format and organization of D are specific for to a particular crawler implementation HiWE uses a set of labeled fuzzy sets

22 23/12/2001Michael Weinberg, SDBI Seminar22 Matching Function Matching algorithm properties: – – Input: Internal form representation and current contents of the database D – Output: Set of value assignments – associates value with element

23 23/12/2001Michael Weinberg, SDBI Seminar23 Response Analysis Module that stores the response page in the repository Attempts to distinguish between pages containing search results and pages containing error messages This feedback is used to tune the matching function

24 23/12/2001Michael Weinberg, SDBI Seminar24 Traditional Performance Metric Traditional crawlers performance metrics: – Crawling speed – Scalability – Page importance – Freshness These metrics are relevant to hidden web crawlers, but do not capture the fundamental challenges in dealing with the Hidden Web

25 23/12/2001Michael Weinberg, SDBI Seminar25 New Performance Metrics Coverage metric: – ‘Relevant’ pages extracted / ‘relevant’ pages present in the targeted hidden databases – Problem: difficult to estimate how much of the hidden content is relevant to the task

26 23/12/2001Michael Weinberg, SDBI Seminar26 New Performance Metrics – : the total number of forms that the crawler submits – : num of submissions which result in response page with one or more search results – Problem: the crawler is penalized if the database didn’t contain any relevant search results

27 23/12/2001Michael Weinberg, SDBI Seminar27 New Performance Metrics – : number of semantically correct form submissions – Penalizes the crawler only if a form submission is semantically incorrect – Problem: difficult to evaluate since a manual comparison is needed to decide whether the form is semantically correct

28 23/12/2001Michael Weinberg, SDBI Seminar28 Design Issues What information about each form element should the crawler collect? What meta-information is likely to be useful? How should the task-specific database be organized, updated and accessed? What Match function is likely to maximize submission efficiency? How to use the response analysis module to tune the Match function?

29 23/12/2001Michael Weinberg, SDBI Seminar29 HiWE: Hidden Web Exposer

30 23/12/2001Michael Weinberg, SDBI Seminar30 Basic Idea Extract descriptive information (label) for each element of a form Task-specific database is organized in terms of categories, each of which is also associated with labels Matching function attempts to match from form labels to database categories to compute a set of candidate values assignments

31 LVS Manager HiWE Architecture Label 1 Value-Set 1 Label 2 Value-Set 2 Label n Value-Set n Response Analyzer Form Processor Form Analyzer Crawl Manager Parser WWW URL 1 URL 2 URL N URL List Custom data sources LVS Table Form submission Response Feedback

32 23/12/2001Michael Weinberg, SDBI Seminar32 HiWE ’ s Main Modules URL List: – contains all the URLs the crawler has discovered so far Crawl Manager: – controls the entire crawling process Parser: – extracts hypertext links from the crawled pages and adds them to the URL list Form Analyzer, Form Processor, Response Analyzer: – Together implement the form processing and submission operations

33 23/12/2001Michael Weinberg, SDBI Seminar33 HiWE ’ s Main Modules LVS Manager: – Manages additions and accesses to the LVS table LVS table: – HiWE’s implementation of the task-specific database

34 23/12/2001Michael Weinberg, SDBI Seminar34 HiWE ’ s Form Representation Form – The third component of F is an empty set since current implementation of HiWE does not collect any meta- information about the form For each element, HiWE collects a domain Dom( ) and a label label( )

35 23/12/2001Michael Weinberg, SDBI Seminar35 HiWE ’ s Form Representation Domain of an element: – Set of values which can be associated with the corresponding form element – May be a finite set (e.g., domain of a selection list) – May be infinite set (e.g., domain of a text box) Label of an element: – The descriptive information associated with the element, if any – Most forms include some descriptive text to help users understand the semantics of the element

36 23/12/2001Michael Weinberg, SDBI Seminar36 Label(E 1 ) = "Document Type" Dom(E 1 ) = {Articles, Press Releases, Label(E 2 ) = "Company Name" Dom(E 2 ) = {s | s is a text string} Label(E 3 ) = "Sector" Dom(E 3 ) = {Entertainment, Automobile Reports} Element E 1 Element E 2 Information Technology, Construction} Element E 3 Form Representation - Figure

37 23/12/2001Michael Weinberg, SDBI Seminar37 HiWE ’ s Task-specific Database Task-specific information is organized in terms of a finite set of concepts or categories Each concept has one or more labels and an associated set of values For example the label ‘Company Name’ could be associated with the set of values {‘IBM’, ‘Microsoft’, ‘HP’,…}

38 23/12/2001Michael Weinberg, SDBI Seminar38 The concepts are organized in a table called the Label Value Set (LVS) Each entry in the LVS is of the form (L,V): – L : label – fuzzy set of values – Fuzzy set V has an associated membership function that assigns weights, in the range [0,1] to each member of the set – is a measure of the crawler’s confidence that the assignment of to E is semantically meaningful HiWE ’ s Task-specific Database

39 23/12/2001Michael Weinberg, SDBI Seminar39 For elements with a finite domain: – The set of possible values is fixed and can be exhaustively enumerated – In this example, the crawler can first retrieve all relevant articles, then all relevant press releases and finally all relevant reports HiWE ’ s Matching Function Label(E 1 ) = "Document Type" Dom(E 1 ) = {Articles, Press Releases, Reports} Element E 1

40 23/12/2001Michael Weinberg, SDBI Seminar40 For elements with an infinite domain: – HiWE textually matches the labels of these elements with labels in the LVS table – For example, if a textbox element has the label “Enter State” which best matches an LVS entry with the label “State”, the values associated with that LVS entry (e.g., “California”) can be used to fill the textbox – How do we match Form labels with LVS labels? HiWE ’ s Matching Function

41 23/12/2001Michael Weinberg, SDBI Seminar41 Two steps in matching Form labels with LVS labels: – 1. Normalization: includes conversion to a common case and standard style – 2. Use of an approximate string matching algorithm to compute minimum edit distances – HiWE employs D. Lopresti and A. Tomkins string matching algorithm that takes word reordering into account Label Matching

42 23/12/2001Michael Weinberg, SDBI Seminar42 Let LabelMatch( ) denote the LVS entry with the minimum distance to label( ) Threshold If all LVS entries are more than edit operations away from label( ), LabelMatch( ) = nil Label Matching

43 23/12/2001Michael Weinberg, SDBI Seminar43 For each element, compute (, ): – If has an infinite domain and (L,V) is the closest matching LVS entry, then = V and = – If has a finite domain, then =Dom( ) and The set of value assignments is computed as the product of all the `s: Too many assignments? Label Matching

44 23/12/2001Michael Weinberg, SDBI Seminar44 HiWE employs an aggregation function to compute a rank for each value assignment Uses a configurable parameter, a minimum acceptable value assignment rank ( ) The intent is to improve submission efficiency by only using ‘high-quality’ value assignments We will show three possible aggregation functions Ranking Value Assignments

45 23/12/2001Michael Weinberg, SDBI Seminar45 The rank of a value assignment is the minimum of the weights of all the constituent values. Very conservative in assigning ranks. Assigns a high rank only if each individual weight is high Fuzzy Conjunction

46 23/12/2001Michael Weinberg, SDBI Seminar46 The rank of a value assignment is the average of the weights of the constituent values Less conservative than fuzzy conjunction Average

47 23/12/2001Michael Weinberg, SDBI Seminar47 This ranking function treats weights as probabilities is the likelihood that the choice of is useful and is the likelihood that it is not The likelihood of a value assignment being useful is: Assigns low rank if all the individual weights are very low Probabilistic

48 23/12/2001Michael Weinberg, SDBI Seminar48 HiWE supports a variety of mechanisms for adding entries to the LVS table: – Explicit Initialization – Built-in entries – Wrapped data sources – Crawling experience Populating the LVS Table

49 23/12/2001Michael Weinberg, SDBI Seminar49 Supply labels and associated value sets at startup time Useful to equip the crawler with labels that the crawler is most likely to encounter In the ‘semiconductor’ example, we supply HiWE with a list of relevant company names and associate the list with labels ‘Company’, ‘Company Name’ Explicit Initialization

50 23/12/2001Michael Weinberg, SDBI Seminar50 HiWE has built-in entries for commonly used concepts: – Dates and Times – Names of months – Days of week Built-in Entries

51 23/12/2001Michael Weinberg, SDBI Seminar51 LVS Manager can query data sources through a well-defined interface The data source must be ‘wrapped’ by a program that supports two kinds of queries: – Given a set of labels, return a value set – Given a set of values, return other values that belong to the same value set Wrapped Data Sources

52 LVS Manager HiWE Architecture Label 1 Value-Set 1 Label 2 Value-Set 2 Label n Value-Set n Response Analyzer Form Processor Form Analyzer Crawl Manager Parser WWW URL 1 URL 2 URL N URL List Custom data sources LVS Table Form submission Response Feedback

53 23/12/2001Michael Weinberg, SDBI Seminar53 Finite domain form elements are a useful source of labels and associated value sets HiWE adds this information to the LVS table Effective when similar label is associated with a finite domain element in one form and with an infinite domain element in another Crawling Experience

54 23/12/2001Michael Weinberg, SDBI Seminar54 New value added to the LVS must be assigned a suitable weight Explicit initialization and build-in values have fixed weights Values obtained from external data sources or through the crawler’s own activity, are assigned weights that vary with time Computing Weights

55 23/12/2001Michael Weinberg, SDBI Seminar55 For external data sources - computed by the respective wrappers For values directly gathered by the crawler: – Finite domain element E with Dom(E) – = 1 iff – Three cases arise when incorporating Dom(E) into the LVS table Initial Weights

56 23/12/2001Michael Weinberg, SDBI Seminar56 Crawler successfully extracts label(E) and computes LabelMatch(E)=(L,V): – Replace the (L,V) entry by the entry – – Intuitively, Dom(E) provides new elements to the value set and ‘boosts’ the weights of existing elements Updating LVS – Case 1

57 23/12/2001Michael Weinberg, SDBI Seminar57 Crawler successfully extracts label(E) but LabelMatch(E) = nil: – A new entry ( label(E),Dom(E) ) is created in the LVS Updating LVS – Case 2

58 23/12/2001Michael Weinberg, SDBI Seminar58 Crawler can not extract label(E): – For each entry (L,V): Compute a score : Identify the entry with the maximum score Identify the value of the maximum score Replace entry with new entry Confidence of new values: Updating LVS – Case 3

59 23/12/2001Michael Weinberg, SDBI Seminar59 Initialization of the crawling activity includes: – Set of sites to crawl – Explicit initialization for the LVS table – Set of data sources – Label matching threshold – Minimum acceptable value assignment rank – Value assignment aggregation function Configuring HiWE

60 23/12/2001Michael Weinberg, SDBI Seminar60 Layout-based Information Extraction Technique Physical Layout of a page is also used to aid in extraction For example, a piece of text that is physically adjacent to a form element is very likely a description of that element Unfortunately, this semantic associating is not always reflected in the underlying HTML of the Web page Introducing LITE

61 23/12/2001Michael Weinberg, SDBI Seminar61 Layout-based Information Extraction Technique

62 23/12/2001Michael Weinberg, SDBI Seminar62 Accurate extraction of the labels and domains of form elements Elements that are visually close on the screen, may be separated arbitrarily in the actual HTML text Even when HTML provides a facility for semantic relationships, it’s not used in a majority of pages Accurate page layout is a complex process Even a crude approximate layout of portions of a page, can yield very useful semantic information The Challenge

63 23/12/2001Michael Weinberg, SDBI Seminar63 LITE-based heuristic: – Prune the form page and isolate elements which directly influence the layout – Approximately layout the pruned page using a custom layout engine – Identify the pieces of text that are physically closest to the form element (these are candidates) – Rank each candidate using a variety of measures – Choose the highest ranked candidate as the label Form Analysis in HiWE

64 23/12/2001Michael Weinberg, SDBI Seminar64 Pruning Before Partial Layout

65 23/12/2001Michael Weinberg, SDBI Seminar65 LITE - Figure Partial Layout DOM Parser DOM Representation Pruned Page Prune List of Elements Submission Info Labels & Domain Values DOM API Internal Form Representation Key Idea in LITE: Physical page layout embeds significant semantic information

66 23/12/2001Michael Weinberg, SDBI Seminar66 Experiments A number of experiments were conducted to study the performance of HiWE We will see how performance depends on: – Minimum form size – Crawler input to LVS table – Different ranking functions

67 23/12/2001Michael Weinberg, SDBI Seminar67 Parameter Values for Task 1 Task 1: News articles, reports, press releases and white papers relating to the semiconductor industry, dated sometime in the last ten years

68 23/12/2001Michael Weinberg, SDBI Seminar68 Variation of Performance with

69 23/12/2001Michael Weinberg, SDBI Seminar69 Effect of Crawler input to LVS

70 23/12/2001Michael Weinberg, SDBI Seminar70 Different Ranking Functions When using and the crawler’s submission efficiency is mostly above 80% performs poorly submits more forms than (less conservative)

71 23/12/2001Michael Weinberg, SDBI Seminar71 Label Extraction LITE-based heuristic achieved overall accuracy of 93% The test set was manually analyzed

72 23/12/2001Michael Weinberg, SDBI Seminar72 Conclusion Addressed the problem of extending current-day crawlers to build repositories that include pages from the ‘Hidden Web’ Presented a simple operation model of a hidden web crawler Described the implementation of a prototype crawler – HiWE Introduced a technique for Layout-based information extraction

73 23/12/2001Michael Weinberg, SDBI Seminar73 Bibliography Crawling the Hidden Web, by S. Raghavan and H. Garcia-Molina, Stanford University, 2001 white papers D. Lopresti and A. Tomkins. Block edit models for approximate string matching

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