1 CS 430: Information Discovery Lecture 15 Library Catalogs 3

2 Course Administration Midterm examination results have been sent by . Assignment 2 results will be mailed shortly. Assignment 3, due November 10, will be posted soon.

3 Automatic extraction of catalog data Example: Dublin Core records for web pages Strategies Manual by trained cataloguers - high quality records, but expensive and time consuming Entirely automatic - fast, almost zero cost, but poor quality Automatic followed by human editing - cost and quality depend on the amount of editing Manual collection level record, automatic item level record - moderate quality, moderate cost

4 DC-dot DC-dot is a Dublin Core metadata editor for web pages, created by Andy Powell at UKOLN DC-dot has two parts: (a) A skeleton Dublin Core record is created automatically from clues in the web page (b) A user interface is provided for cataloguers to edit the record

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6 Automatic record for CS 430 home page DC-dot applied to continued on next slide

7 Automatic record for CS 430 home page (continued) DC-dot applied to

8 Observations on DC-dot applied to CS430 home page DC.Title is a copy of the html field DC.Publisher is the owner of the IP address where the page was stored DC.Subject is a list of headings and noun phrases presented for editing DC.Date is taken from the Last-Modified field in the http header DC.Type and DC.Format are taken from the MIME type of the http response DC.Identifier was supplied by the user as input

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10 DC-dot applied to continued on next slide Automatic record for George W. Bush home page

11 DC-dot applied to Automatic record for George W. Bush home page (continued)

12 Observations on DC-dot applied to George W. Bush home page The home page has several meta tags: [The page has no html ] <META NAME="KEYWORDS" CONTENT="George W. Bush, Bush, George Bush, President, republican, 2000 election and more

13 Collection-level metadata Several of the most difficult fields to extract automatically are the same across all pages in a web site. Therefore create a collection record manually and combine it with automatic extraction of other fields at item level. For the CS 430 home page, collection-level metadata: See: Jenkins and Inman

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15 Metadata extracted automatically by DC-dot D.C. Field Qualifier Content title Digital Libraries and the Problem of Purpose subject not included in this slide publisher Corporation for National Research Initiatives date W3CDTF type DCMIType Text format text/html format bytes identifier

16 Collection-level record D.C. Field Qualifier Content publisher Corporation for National Research Initiatives type article type resource work relation rel-type InSerial relation serial-name D-Lib Magazine relation issn language English rights Permission is hereby given for the material in D-Lib Magazine to be used for...

17 Combined item-level record (DC-dot plus collection-level) D.C. Field Qualifier Content title Digital Libraries and the Problem of Purpose publisher (*) Corporation for National Research Initiatives date W3CDTF type (*) article type resource (*) work type DCMIType Text format text/html format bytes (*) indicates collection-level metadata continued on next slide

18 Combined item-level record (DC-dot plus collection-level) D.C. Field Qualifier Content relation rel-type (*) InSerial relation serial-name (*) D-Lib Magazine relation issn (*) language (*) English rights (*) Permission is hereby given for the material in D-Lib Magazine to be used for... identifier (*) indicates collection-level metadata

19 Manually created record D.C. Field Qualifier Content title Digital Libraries and the Problem of Purpose creator (+) David M. Levy publisher Corporation for National Research Initiatives date publication January 2000 type article type resource work (+) entry that is not in the automatically generated records continued on next slide

20 Manually created record D.C. Field Qualifier Content relation rel-type InSerial relation serial-name D-Lib Magazine relation issn relation volume (+) 6 relation issue (+) 1 identifier DOI (+) /january2000-levy identifier URL language English rights (+) Copyright (c) David M. Levy (+) entry that is not in the automatically generated records

21 Collection-level metadata Compare: (a) Metadata extracted automatically by DC-dot (b) Collection-level record (c) Combined item-level record (DC-dot plus collection-level) (d) Manual record For web pages information retrieval works better by automatic indexing, rather than automatic extraction of metadata followed by indexing of metadata. However, we will see later an effective example of automated extraction of metadata from video sequences (Informedia).

22 Metatest Metatest is a research project led by Liz Liddy at Syracuse with participation from the Human Computer Interaction group at Cornell. The aim is to compare the effectiveness as perceived by the user of indexing based on: (a) Manually created Dublin Core (b) Automatically created Dublin Core (higher quality than DC-dot) (c) Full text indexing Preliminary results suggest remarkably little difference in effectiveness.

23 Midterm Examination Q3 (a) The aggregate term weighting for term j in document i is sometimes written: w ij = tf ij * idf j Explain the purpose of tf ij and idf j. Term frequency assumes that the usefulness of a term for retrieval increase as the number of times that the term appears in the document increases. Inverse document frequency assumes that terms that apear in few documents are better discriminators than those that appear in many.

24 Midterm Examination Q3 (continued) In class, we recommended, the following term weighting for free text documents: tf ij = f ij / m i idf j = log 2 (n / n j ) + 1 (i) Explain why this form is frequently changed for the weighting of terms in documents, such as catalog records, that are not free text. The forms of tf and idf have been developed for free text. The distribution of terms are different in free text and catalog records. (ii) Explain why this form might give difficulties if the documents vary greatly in length. The scaling factors in tf and idf have been developed for collections of similar length records.

25 Midterm Examination Q3 (continued) (c) Consider the query: q: dog cat dog and the following set of documents: d1: bee dog bee cat bee elk elk d2: elk dog ant ant dog ant d2: cat cat cat cat dog (i) With no term weighting, what is the similarity between this query and each of the documents?

26 Midterm Examination Q3 (continued) Term vector matrix antbeecatdogelklength q11√2 d d2111√3 d311√2 Similarities qd1d2d3 q11/√21/√61

27 Midterm Examination Q3 (continued) (ii) Weighting both the query and the documents for term frequency, but not weighting for inverse document frequency, what is the similarity between this query and each of the documents?

28 Midterm Examination Q3 (continued) Term vector matrix antbeecatdogelklength q12√5 d13112√15 d2321√14 d341√17 Similarities qd1d2d3 q13/√754/√706/√85