1. 1 CS 430: Information Discovery Lecture 3 Inverted Files

2. 2 Course Administration Course team: Bill Armswya@cs.cornell.edu Matt Schultzschultz@cs.cornell.edu Doug Mitarotonda dcm14@cornell.edu Trishul Pateltyp1@cornell.edu Email sent to: cs430@cs.cornell.edu will go to these four people

3. 3 Course Administration Assignment 1 will be posted early next week. It is a programming assignment and is due on Friday, September 20 at 5 p.m. A text book was left in the Discussion Class yesterday.

4. 4 Inverted File -- Concept (Basic Definition) Inverted file: a list of the words in a set of documents and the documents in which they appear. Word Document abacus 3 19 22 actor 2 19 29 aspen 5 atoll 11 34 Stop words are removed and stemming carried out before building the index.

5. 5 Inverted List -- Concept Inverted list: All the entries in an inverted file that apply to a specific word, e.g. abacus 3 19 22 Posting: Entry in an inverted list, e.g., the postings for "abacus" are documents 3, 19, 22.

6. 6 Keywords and Controlled Vocabulary Keyword: A term that is used to describe the subject matter in a document. It is sometimes called an index term. Keywords can be extracted automatically from a document or assigned by a human cataloguer or indexer. Controlled vocabulary: A list of words that can be used as keywords, e.g., in a medical system, a list of medical terms. Inverted file (more complete definition): A list of the keywords that apply to a set of documents, the documents in which they appear and related information.

7. 7 Enhancements to Inverted Files -- Concept Location: The inverted file holds information about the location of each term within the document. Uses adjacency and near operators user interface design -- highlight location of search term Frequency: The inverted file includes the number of postings for each term. Uses term weighting query processing optimization

8. 8 Inverted File -- Concept (Enhanced) WordPostings DocumentLocation abacus4 3 94 19 7 19 212 2256 actor3 2 66 19 213 29 45 aspen1 5 43 atoll3 11 3 1170 34 40

9. 9 Example: Boolean Queries Boolean query: two or more search terms, related by logical operators, e.g., andornot Examples: abacus and actor abacus or actor (abacus and actor) or (abacus and atoll) not actor

10. 10 Boolean Diagram A B A and B A or B not (A or B)

11. 11 Evaluating a Boolean Query 3 19 22 2 19 29 To evaluate the and operator, merge the two inverted lists with a logical AND operation. Examples: abacus and actor Postings for abacus Postings for actor Document 19 is the only document that contains both terms, "abacus" and "actor".

12. 12 Adjacent and Near Operators abacus adj actor Terms abacus and actor are adjacent to each other as in the string "abacus actor" abacus near 4 actor Terms abacus and actor are near to each other as in the string "the actor has an abacus" Some systems support other operators, such as with (two terms in the same sentence) or same (two terms in the same paragraph).

13. 13 Evaluating an Adjacency Operation Examples: abacus adj actor Postings for abacus Postings for actor Document 19, locations 212 and 213, is the only occurrence of the terms "abacus" and "actor" adjacent. 3 94 19 7 19 212 22 56 2 66 19 213 29 45

14. 14 Evaluation of Boolean Operators Precedence of operators must be defined: adj, nearhigh and, not or low Example A and B or C and B is evaluated as (A and B) or (C and B)

15. 15 SetRecordsUnique Terms A2,6535,123 B38,304c.25,000 Sizes of Inverted Files Set A has an average of 14 postings per term and a maximum of over 2,000 postings per term. Set B has an average of 88 postings per record. Examples from Harman and Candela, 1990

16. 16 Representation of Inverted Files Index (word list, vocabulary) file: Stores list of terms (keywords). Designed for rapid searching and processing range queries (lexicographic index). Often held in memory. Postings file: Stores an inverted list (postings list) of postings for each term. Designed for rapid merging of lists. Each list may be stored sequentially. Document file: Stores the documents. Important for user interface design. [Repositories for the storage of document collections are covered in CS 502.]

17. 17 Organization of Inverted Files TermPointer to postings ant bee cat dog elk fox gnu hog Inverted lists Index filePostings file Documents file

18. 18 Decisions in Building Inverted Files Underlying character set, e.g., printable ASCII, Unicode, UTF8. Whether to use a controlled vocabulary. If so, what words to include. List of stopwords. Rules to decide the beginning and end of words, e.g., spaces or punctuation. Character sequences not to be indexed, e.g., sequences of numbers.

19. 19 Efficiency Criteria Storage Inverted files are big, typically 10% to 100% the size of the collection of documents. Update performance It must be possible, with a reasonable amount of computation, to: (a) Add a large batch of documents (b) Add a single document Retrieval performance Retrieval must be fast enough to satisfy users and not use excessive resources.

20. 20 Index Files On disk If an index is held on disk, search time is dominated by the number of disk accesses. In memory Suppose that an index has 1,000,000 distinct terms. Each index entry consists of the term and a pointer to the inverted list, average 100 characters. Size of index is 100 megabytes, which can easily be held in memory.

21. 21 Postings File Merging inverted lists is the most computationally intensive task in many information retrieval systems. Since inverted lists may be very long, it is important to match postings efficiently. Usually, the inverted lists will be held on disk and paged into memory for matching. Therefore algorithms for matching postings process the lists sequentially. For efficient matching, the inverted lists should all be sorted in the same sequence. Inverted lists are commonly cached to minimize disk accesses.

22. 22 Efficiency and Query Languages Some query options may require huge computation, e.g., Regular expressions If inverted files are stored in alphabetical order, comp* can be processed efficiently *comp cannot be processed efficiently Boolean terms If A and B are search terms A or B can be processed by comparing two moderate sized lists (not A) or (not B) requires two very large lists

23. 23 SMART System An experimental system for automatic information retrieval automatic indexing to assign terms to documents and queries collect related documents into common subject classes identify documents to be retrieved by calculating similarities between documents and queries procedures for producing an improved search query based on information obtained from earlier searches Gerald Salton and colleagues Harvard 1964-1968 Cornell 1968-1988

24. 24 Vector Space Methods Problem: Given two text documents, how similar are they? (One document may be a query.) Vector space methods that measure similarity do not assume exact matches. Benefits of similarity measures rather than exact matches Encourage long queries, which are rich in information. An abstract should be very similar to its source document. Accept probabilistic aspects of writing and searching. Different words will be used if an author writes the same document twice.

25. 25 Vector Space Revision x = (x 1, x 2, x 3,..., x n ) is a vector in an n-dimensional vector space Length of x is given by (extension of Pythagoras's theorem) |x| 2 = x 1 2 + x 2 2 + x 3 2 +... + x n 2 If x 1 and x 2 are vectors: Inner product (or dot product) is given by x 1.x 2 = x 11 x 21 + x 12 x 22 + x 13 x 23 +... + x 1n x 1n Cosine of the angle between the vectors x 1 and x 2: cos (  ) = x 1.x 2 |x 1 | |x 2 |