Simple Search Engines
[The specification may change, Please check the change log on this page.]
Change log:
Nothing as yet!
Objectives
to implement simple search engines using well known algorithms like (Weighted) PageRank and tfidf, simplified versions for this assignment! to give you further practice with C and data structures (Graph ADT)
to give you experience working in a team
Admin
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Marks |
20 marks (14 marks towards total course mark) |
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Group |
This assignment is completed in group of two, based on your current lab group. |
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Due |
11:59:00 pm Monday Week13 |
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Late Penalty |
2 marks per day off the ceiling. Last day to submit this assignment is 5pm Friday Week13, of course with late penalty. |
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Submit |
Read instructions in the “Submission” section below. |
Aim
In this assignment, your task is to implement simple search engines using well known algorithms like (Weighted) PageRank and tfidf, simplified for this assignment, of course!. You should start by reading the wikipedia entries on these topics. Later I will also discuss these topics in the lecture.
PageRank (read up to the section “Damping factor”)
The main focus of this assignment is to build a graph structure, calculate Weighted PageRank, tfidf, etc. and rank pages based one these values. You don’t need to spend time crawling, collecting and parsing weblinks for this assignment. You will be provided with a collection of “web pages” with the required information for this assignment in a easy to use format. For example, each page has two sections,
Section1 contains urls representing outgoing links. Urls are separated by one or more blanks, across multiple lines.
Section2 contains selected words extracted from the url. Words are separated by one or more spaces, spread across multiple lines.
Hint: If you use fscanf to read the body of a section above, you do not need to impose any restriction on line length. I suggest you should try to use this approach use fscanf! However, if you want to read line by line using say fgets, you can assume that maximum length of a line would be 1000 characters.
Hint: You need to use a dynamic data structure(s) to handle words in a file and across files, no need to know max words beforehand. Example file url31.txt
#start Section-1
url2 url34 url1 url26 url52 url21
url74 url6 url82
#end Section-1
#start Section-2
Mars has long been the subject of human interest. Early telescopic observations revealed color changes on the surface that were attributed to seasonal vegetation and apparent linear features were ascribed to intelligent design.
#end Section-2
In Part1: Graph structurebased search engine, you need to create a graph structure that represents a hyperlink structure of given collection of “web pages” and for each page (node in your graph) calculate Weighted PageRank and other graph properties. You need to create “inverted index” that provides a list of pages for every word in a given collection of pages. Your graphstructure based search
engine will use this inverted index to find pages where query term(s) appear and rank these pages using their Weighted PageRank values.
In Part2: Contentbased search engine, you need to calculate tfidf values for each query term in a page, and rank pages based on the summation of tfidf values for all query terms. Use “inverted index” you created in Part1 to locate matching pages for query terms.
In Part3: Hybrid search engine, you need to combine both PageRank and tfidf values in order to rank pages.
Additional files: You can submit additional supporting files, *.cand *.h, for this assignment. For example, you may implement your graph adt in files graph.c and graph.h and submit these two files along with other required files as mentioned below.
Sample files
Part1: Graph structurebased Search Engine
Hints on “How to Implement Ass2 (Part1)” , to be discussed in the lecture.
A: Calculate Weighted PageRanks
You need to write a program in the file pagerank.c that reads data from a given collection of pages in the file collection.txt and builds a graph structure using Adjacency Matrix or List Representation. Using the algorithm described below, calculate Weighted PageRank for every url in the file collection.txt. In this file, urls are separated by one or more spaces or/and new line character. Add suffix .txt to a url to obtain file name of the corresponding “web page”. For example, file url24.txt contains the required information for url24.
Example file collection.txt
url25 url31 url2 url102 url78
url32 url98 url33
Simplified Weighted PageRank Algorithm you need to implement (for this assignment) is shown below. Please note that the formula to calculate PR values is slightly different to the one provided in the corresponding paper (for explanation, read Damping factor).
PageRankW(d, diffPR, maxIterations)
Read “web pages” from the collection in file “collection.txt” and build a graph structure using Adjacency List Representation
N = number of urls in the collection For each url pi in the collection
End For
iteration = 0;
diff = diffPR; // to enter the following loop
While (iteration < maxIteration AND diff >= diffPR) iteration++;
End While
Your program in pagerank.c will take three arguments (d damping factor, diffPR difference in PageRank sum, maxIterations maximum iterations) and using the algorithm described in this section, calculate Weighted PageRank for every url.
For example,
% pagerank 0.85 0.00001 1000
Your program should output a list of urls in descending order of Weighted PageRank values (use format string “%.7f” to 8 significant digits) to a file named pagerankList.txt. The list should also include out degrees (number of out going links) for each url, along with its Weighted PageRank value. The values in the list should be comma separated. For example, pagerankList.txt may contain the following:
Example file pagerankList.txt
url31, 3, 0.2623546
url21, 1, 0.1843112
url34, 6, 0.1576851
url22, 4, 0.1520093
url32, 6, 0.0925755
url23, 4, 0.0776758
url11, 3, 0.0733884
Sample Files for 1A
You can download the following three sample files with expected pagerankList.txt files. For your reference, I have also included the file “log.txt” which includes values of Win, Wout, etc. Please note that you do NOT need to generate such a log file.
Use format string “%.7f” to output pagerank values. Please note that your pagerank values might be slightly different to that provided in these samples. This might be due to the way you carry out calculations. However, make sure that your pagerank values match to say first 6 decimal points to the expected values. For example, say an expected value is 0.1843112, your value could be 0.184311x where x could be any digit.
All the sample files were generated using the following command:
% pagerank 0.85 0.00001 1000
B: Inverted Index
You need to write a program in the file named inverted.c that reads data from a given collection of pages in collection.txt and generates an “inverted index” that provides a sorted list (set) of urls for every word in a given collection of pages. Before inserting words in your index, you need to “normalise” words by,
removing leading and trailing spaces, converting all characters to lowercase,
remove the following punctuation marks, if they appear at the end of a word: ‘.’ (dot), ‘,’ (comma), ‘;’ (semicolon), ? (question mark)
In each sorted list (set), duplicate urls are not allowed. Your program should output this “inverted index” to a file named invertedIndex.txt. One line per word, words should be alphabetically ordered, using ascending order. Each list of urls (for a single word) should be alphabetically ordered, using ascending order.
Example file invertedIndex.txt
design url2 url25 url31 url61 mars url25 url31 url101 vegetation url31 url61
C: Search Engine
Write a simple search engine in file searchPagerank.c that given search terms (words) as commandline arguments, finds pages with one or more search terms and outputs (to stdout) top 30 pages in descending order of number of search terms found and then within each group, descending order of Weighted PageRank. If number of matches are less than 30, output all of them.
Your program must use data available in two files invertedIndex.txt and pagerankList.txt, and must derive result from them. We will test this program independently to your solutions for “A” and “B”.
Example:
% searchPagerank mars design url31
url25
Part2: Contentbased Search Engine
In this part, you need to implement a contentbased search engine that uses tfidf values of all query terms for ranking. You need to calculate tfidf values for each query term in a page, and rank pages based on the summation of tfidf values for all query terms. Use “inverted index” you created in Part1 to locate matching pages for query terms.
Read the following wikipedia page that describes how to calculate tfidf values:
For this assignment,
calculate term frequency tf(t,d) adjusted for document length,
i.e. (frequency of term t in d) / (number of words in d). See the example below.
calculate inverse document frequency idf(t, D) by dividing the total number of documents by the number of documents containing the term, and then taking the logarithm of that quotient:
See the example below.
Write a contentbased search engine in file searchTfIdf.c that given search terms (words) as commandline arguments, outputs (to stdout) top 30 pages in descending order of number of search terms found and then within each group, descending order of summation of tfidf values of all search terms found. Your program must also output the corresponding summation of tfidf along with each page, separated by a space and using format “%.6f”, see example below.
If number of matches are less than 30, output all of them. Your program must use data available in two files invertedIndex.txt and
collection.txt, and must derive result from them. We will test this program independently to your solutions for Part1.
Example:
% searchTfIdf mars design url25 1.902350
url31 0.434000
Part3: Hybrid/Meta Search Engine using Rank Aggregation
In this part, you need to combine search results (ranks) from multiple sources (say from Part1 and Part2) using “Scaled Footrule Rank Aggregation” method, described below. All the required information for this method are provided below. However, if you are interested, you may want to check out this presentation on “Rank aggregation method for the web”.
Let T1 and T2 are search results (ranks) obtained using two different criteria (say Part1 and Part2). Please note that we could use any suitable criteria, including manually generated rank lists.
A weighted bipartite graph for scaled footrule optimization (C,P,W) is defined as,
C = set of nodes to be ranked (say a union of T1 and T2) P = set of positions available (say {1, 2, 3, 4, 5})
W(c,p) is the scaledfootrule distance (from T1 and T2 ) of a ranking that places element ‘c’ at position ‘p’, given by
where
n is the cardinality (size) of C,
|T1| is the cardinality (size) of T1,
|T2| is the cardinality (size) of T2, T1(x3) is the position of x3 in T1, k is number of rank lists.
For example,
The final ranking is derived by finding possible values of position ‘P’ such that the scaledfootrule distance is minimum. There are many different ways to assign possible values for ‘P’. In the above example P = [1, 3, 2, 5, 4]. Some other possible values are, P = [1, 2, 4, 3, 5], P = [5, 2, 1, 4, 3], P =
[1, 2, 3, 4, 5], etc. For n = 5, possible alternatives are 5! For n = 10, possible alternatives would be 10! that is 3,628,800 alternatives. A very simple and obviously inefficient approach could use bruteforce search and generate all possible alternatives, calculate scaledfootrule distance for each alternative, and find the alternative with minimum scaledfootrule distance.
If you use such a bruteforce search, you will receive maximum of 65% for Part3. However, you will be rewarded 100% for Part3 if you implement a “smart” algorithm that avoids generating unnecessary alternatives, in the process of finding the minimum scaledfootrule distance. Please document your algorithm such that your tutor can easily understand your logic, and clearly outline how you plan to reduce search space, otherwise you will not be awarded mark for your “smart” algorithm! Yes, it’s only 35% of part3 marks, but if you try it, you will find it very challenging and rewarding.
Write a program scaledFootrule.c that aggregates ranks from files given as commandline arguments, and output aggregated rank list with minimum scaled footrule distance.
Example, file rankA.txt
url1 url3 url5 url4 url2
Example, file rankD.txt
url3 url2 url1 url4
The following command will read ranks from files “rankA.txt” and “rankD.txt” and outputs minimum scaled footrule distance (using format %.6f) on the first line, followed by the corresponding aggregated rank list.
% scaledFootrule rankA.txt rankD.txt
For the above example, there are two possible answers, with minimum distance of 1.400000. Two possible values of P with minnimum distance are:
C = [url1, url2, url3, url4, url5] P = [1, 4, 2, 5, 3] and
P = [1, 5, 2, 4, 3]
By the way, you need to select any one of the possible values of P that has minium distance, so there could be multiple possible answers. Note that you need to output only one such list.
One possible answer for the above example, for P = [1, 4, 2, 5, 3] :
1.400000
url1 url3 url5 url2 url4
Another possible answer for the above example, P = [1, 5, 2, 4, 3] :
1.400000
url1 url3 url5 url4 url2
Please note that your program should also be able to handle multiple rank files, for example:
% scaledFootrule rankA.txt rankD.txt newSearchRank.txt myRank.txt
Assignment2 Group Creation
Later, instructions on how to create groups will be posted here.
Submission
Additional files: You can submit additional supporting files, *.c and *.h, for this assignment.
IMPORTANT: Make sure that your additional files (*.c) DO NOT have “main” function.
For example, you may implement your graph adt in files graph.c and graph.h and submit these two files along with other required files as mentioned below. However, make sure that these files do not have “main” function.
I explain below how we will test your submission, hopefully this will answer all of your questions. You need to submit the following files, along with your supporting files (*.c and *.h):
pagerank.c inverted.c searchPagerank.c searchTfIdf.c scaledFootrule.c
Now say we want to mark your pagerank.c program. The auto marking program will take all your supporting files (other *.h and *.c) files, along with pagerank.c and execute the following command to generate executable file say called pagerank. Note that the other four files from the above list (inverted.c, searchPagerank.c, searchTfIdf.c and scaledFootrule.c) will be removed from the dir:
% gcc -Wall -lm -std=c11 *.c -o pagerank
So we will not use your Makefile (if any). The above command will generate object files from your supporting files and the file to be tested (say pagerank.c), links these object files and generates executable file, say pagerank in the above example. Again, please make sure that you DO NOT have main function in your supporting files (other *.c files you submit).
We will use similar approach to generate other four executables (from inverted.c, searchPagerank.c, searchTfIdf.c and
scaledFootrule.c).
How to Submit
Later, instructions on how to submit will be posted here.
Plagiarism
This is a group assignment. You are not allowed to use code developed by persons other than in your group. In particular, it is not permitted to exchange code or pseudocode between groups. You are allowed to use code from the course material (for example, available as part of the labs, lectures and tutorials). If you use code from the course material, please clearly acknowledge it by including a comment(s) in your file. If you have questions about the assignment, ask your tutor.
Before submitting any work you should read and understand the sub section named Plagiarism in the section “Student Conduct” in the course outline. We regard unacknowledged copying of material, in whole or part, as an extremely serious offence. For further information, see the course outline.
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