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COMP3410 DB32: Technologies for Knowledge Management Lecture 4: Inverted Files and Signature Files for IR By Eric Atwell, School of Computing, University.

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Presentation on theme: "COMP3410 DB32: Technologies for Knowledge Management Lecture 4: Inverted Files and Signature Files for IR By Eric Atwell, School of Computing, University."— Presentation transcript:

1 COMP3410 DB32: Technologies for Knowledge Management Lecture 4: Inverted Files and Signature Files for IR By Eric Atwell, School of Computing, University of Leeds (including re-use of teaching resources from other sources, esp. Stuart Roberts, School of Computing, Univ of Leeds)

2 Module Objectives On completion of this module, students should be able to: … describe classical and emerging information retrieval techniques, and their relevance to knowledge management; …

3 Todays objectives By the end of this lecture you should understand: why relational databases techniques eg BTree indexing are no use for IR queries; how inverted file structures work to provide efficient query processing. An alternative approach provided by signature files

4 The relational problem the simple approach to searching for a keyword uses leading (and trailing) wildcards: eg %graphics% there is no way other than brute force scan to match such a condition with the data records held in a traditional relational database.

5 The relational problem Rather than hold full text, why not do content analysis, extract the index terms (keywords), and hold these in a relational database? ModuleIndex term Indexed by module(mod_code, title, semester, …) term(term_id, value) index(mod_code, term_id)

6 Sample SQL query: OR find all modules matching: database or AI or knowledge base select distinct m.* from module m inner join index i on m.code = i.mod_code inner join term t on t.term_id = i.term_id where t.value = database OR t.value = AI OR t.value = knowledge base;

7 Another sample query: AND (?) find all modules matching: database and AI and knowledge base select distinct m.* from module m inner join index i on m.code = i.mod_code inner join term t on t.term_id = i.term_id where t.value = database AND t.value = AI AND t.value = knowledge base; This SQL query will not match any record; t.value cannot be simultaneously equal to database, AI and knowledge base. We cannot simply replace the ORs of the last SQL query with ANDs.

8 Corrected sample query: AND find all modules matching: database and AI select distinct m.* from module m inner join index i1 on m.code = i1.mod_code inner join term t1 on t1.term_id = i1.term_id inner join index i2 on m.code = i2.mod_code inner join term t2 on t2.term_id = i2.term_id where t1.value = database and t2.value = AI; Both tables index and term must be searched twice in order to establish whether, for each module, it is attached to both terms database and AI. If the query is a conjunction of N terms, the SQL would have 2N inner joins. AND is more complicated than OR (but common in IR)

9 Inverted file Non-DB structure, so not suitable for standard SQL each index term entry points to a list of document record identifiers (RIDs) standard indexing method for IR systems widely used for search engines can be extended to allow for positional (context) searches

10 Inverted file structure The idea of an inverted file is, as well as storing a document with its list of terms that are used to index it, we store the list of terms used in the whole collection of documents, and for each term point to the list of documents that are indexed by the term. So we have inverted the structure: D 1 : T 11, T 12, …, T 1k D 2 : T 21, T 22, …, T 2l … to give: T 1 : D 11, D 12, …, D 1m T 2 : D 21, D 22, …, D 2n …

11 Inverted file structure Term 1 (2) Term 2 (3) Term 3 (1) Term 4 (3) Term 5 (4) Doc 1 Doc2 Doc3 Doc4 Doc5 Doc dictionaryInverted or postings fileData file

12 Inverted file structure Term 1 (2) Term 2 (3) Term 3 (1) Term 4 (3) Term 5 (4) Doc 1 Doc2 Doc3 Doc4 Doc5 Doc dictionaryInverted or postings fileData file

13 Inverted file structure Term 1 (2) Term 2 (3) Term 3 (1) Term 4 (3) Term 5 (4) Doc 1 Doc2 Doc3 Doc4 Doc5 Doc dictionaryInverted or postings fileData file

14 Inverted file structure Term 1 (2) Term 2 (3) Term 3 (1) Term 4 (3) Term 5 (4) Doc 1 Doc2 Doc3 Doc4 Doc5 Doc dictionaryInverted or postings fileData file

15 Dictionary (in IR) list of terms including normalised keywords or stems plus object descriptors (eg author name) frequency with which that term occurs in the collection pointer to the inverted file access to dictionary is by standard file access method (binary search or Btree or hashing algorithm; DB21)

16 Inverted file for each entry in the dictionary: –a list of pointers into the data file (or object-ids, or URLs..) –identifying those objects indexed by the dictionary term inverted file may also contain: –positional information within each document –term frequency (or weight) within each document

17 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (A and C) or (B and C) or (A and B and C) (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc1 doc3 doc4 doc7 doc8 doc10 doc2 doc3 doc5 doc6 doc8 doc12 doc1 doc2 doc4 doc9 doc11 doc12

18 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc1 doc3 doc4 doc7 doc8 doc10 doc2 doc3 doc5 doc6 doc8 doc12 doc1 doc2 doc4 doc9 doc11 doc12 doc1: (1, 0, 1)

19 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc3 doc4 doc7 doc8 doc10 doc2 doc3 doc5 doc6 doc8 doc12 doc2 doc4 doc9 doc11 doc12 doc1: (1, 0, 1) doc2: (0, 1, 1)

20 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc3 doc4 doc7 doc8 doc10 doc3 doc5 doc6 doc8 doc12 doc4 doc9 doc11 doc12 doc1: (1, 0, 1) doc2: (0, 1, 1) doc3: (1, 1, 0)

21 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc4 doc7 doc8 doc10 doc5 doc6 doc8 doc12 doc4 doc9 doc11 doc12 doc1: (1, 0, 1) doc2: (0, 1, 1) doc3: (1, 1, 0) doc4: (1, 0, 1)

22 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc7 doc8 doc10 doc5 doc6 doc8 doc12 doc9 doc11 doc12 doc1: (1, 0, 1) doc2: (0, 1, 1) doc3: (1, 1, 0) doc4: (1, 0, 1) doc5: (0, 1, 0)

23 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc7 doc8 doc10 doc6 doc8 doc12 doc9 doc11 doc12 doc1: (1, 0, 1) doc2: (0, 1, 1) doc3: (1, 1, 0) doc4: (1, 0, 1) doc5: (0, 1, 0) doc6: (0, 1, 0)

24 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc7 doc8 doc10 doc8 doc12 doc9 doc11 doc12 doc1: (1, 0, 1) doc2: (0, 1, 1) doc3: (1, 1, 0) doc4: (1, 0, 1) doc5: (0, 1, 0) doc6: (0, 1, 0) doc7: (1, 0, 0)

25 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc8 doc10 doc8 doc12 doc9 doc11 doc12 doc1: (1, 0, 1) doc2: (0, 1, 1) doc3: (1, 1, 0) doc4: (1, 0, 1) doc5: (0, 1, 0) doc6: (0, 1, 0) doc7: (1, 0, 0) doc8: (1, 1, 0)

26 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc10doc12doc9 doc11 doc12 doc1: (1, 0, 1) doc2: (0, 1, 1) doc3: (1, 1, 0) doc4: (1, 0, 1) doc5: (0, 1, 0) doc6: (0, 1, 0) doc7: (1, 0, 0) doc8: (1, 1, 0) doc9: (0, 0, 1)

27 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc10doc12doc11 doc12 doc1: (1, 0, 1) doc2: (0, 1, 1) doc3: (1, 1, 0) doc4: (1, 0, 1) doc5: (0, 1, 0) doc6: (0, 1, 0) doc7: (1, 0, 0) doc8: (1, 1, 0) doc9: (0, 0, 1) doc10: (1, 0, 0)

28 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc12doc11 doc12 doc1: (1, 0, 1) doc2: (0, 1, 1) doc3: (1, 1, 0) doc4: (1, 0, 1) doc5: (0, 1, 0) doc6: (0, 1, 0) doc7: (1, 0, 0) doc8: (1, 1, 0) doc9: (0, 0, 1) doc10: (1, 0, 0) doc11: (0, 0, 1)

29 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) –retrieve lists of document ids from inverted file corresponding to A, B and C doc12 doc1: (1, 0, 1) doc2: (0, 1, 1) doc3: (1, 1, 0) doc4: (1, 0, 1) doc5: (0, 1, 0) doc6: (0, 1, 0) doc7: (1, 0, 0) doc8: (1, 1, 0) doc9: (0, 0, 1) doc10: (1, 0, 0) doc11: (0, 0, 1) doc12: (0, 1, 1) doc12

30 Use of inverted file Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) doc1: (1, 0, 1) doc2: (0, 1, 1) doc3: (1, 1, 0) doc4: (1, 0, 1) doc5: (0, 1, 0) doc6: (0, 1, 0) doc7: (1, 0, 0) doc8: (1, 1, 0) doc9: (0, 0, 1) doc10: (1, 0, 0) doc11: (0, 0, 1) doc12: (0, 1, 1)

31 Boolean query: (A or B) and C –disjunctive normal form: (1, 0, 1) OR (0, 1, 1) OR (1, 1, 1) doc1: (1, 0, 1) doc2: (0, 1, 1) doc4: (1, 0, 1) doc12: (0, 1, 1) doc1: (1, 0, 1) doc2: (0, 1, 1) doc4: (1, 0, 1) doc12: (0, 1, 1) report number of hits to user (4) (Note: can be done before any hits are retrieved retrieve all objects using pointers: doc1, doc2, doc4 and doc12 Use of inverted file

32 weighted query: A 0.5, B 0.7, C 1.0 form weighted vector: (0.5, 0.7, 1.0) retrieve lists of document ids from inverted file corresponding to A, B and C with weights doc1 (.2) doc3 (.6) doc4 (.7) doc7 (.3) doc8 (.5) doc10 (.5) doc2 (.6) doc3 (.8) doc5 (.9) doc6 (.3) doc8 (.5) doc12 (.2) doc1 (.4) doc2 (.4) doc4 (.7) doc9 (.6) doc11 (.3) doc12 (.6) Use of inverted file with weighted terms

33 weighted query: A 0.5, B 0.7, C 1.0 form weighted vector: (0.5, 0.7, 1.0) retrieve lists of document ids from inverted file corresponding to A, B and C with weights doc1 (.2) doc3 (.6) doc4 (.7) doc7 (.3) doc8 (.5) doc10 (.5) doc2 (.6) doc3 (.8) doc5 (.9) doc6 (.3) doc8 (.5) doc12 (.2) doc1 (.4) doc2 (.4) doc4 (.7) doc9 (.6) doc11 (.3) doc12 (.6) sim((0.5, 0.7, 1.0), (0.2, 0.0, 0.4)) = 0.85 doc1: 0.85 Use of inverted file

34 weighted query: A 0.5, B 0.7, C 1.0 form weighted vector: (0.5, 0.7, 1.0) retrieve lists of document ids from inverted file corresponding to A, B and C with weights doc3 (.6) doc4 (.7) doc7 (.3) doc8 (.5) doc10 (.5) doc2 (.6) doc3 (.8) doc5 (.9) doc6 (.3) doc8 (.5) doc12 (.2) doc2 (.4) doc4 (.7) doc9 (.6) doc11 (.3) doc12 (.6) sim((0.5, 0.7, 1.0), (0.0, 0.6, 0.4)) = 0.86 doc1: 0.85 doc2: 0.86 Use of inverted file

35 sort (rank) list according to similarity coefficient. retrieve first N ranked objects. present ranked list to user. offer to retrieve next N. Note that so far we have not retrieved any documents; this is particularly important if the ids are URLS - we dont need to start downloading web pages in order to rank them. Use of inverted file

36 proximity queries eg Q1: A B Q2: A(3)B (A…B) –postings file holds positional information –proceed as for A and B –keep positional information in (A B) list –filter (A B) list: for Q1 pos(A) = pos(B) -1 for Q2 |pos(B) - pos(A)| < 3 now we can distinguish Venetian blind from blind Venetian in principle this should help precision without affecting recall too much Use of inverted file

37 Pros and cons of inverted file can be used for Boolean, weighted and positional queries query processing can be completed without accessing data file number of hits for single term is available from dictionary expensive to update if information objects change content. demanding storage requirements (dictionary+inverted file approx same size as original data)

38 An alternative: Text signatures use hash algorithm to map a keyword onto one or more bits in a bit string: like Hashing (DB21) Simplest example: use one bit: –Bath = [66, 97, 116, 104] mod 32 ( ) = mod 32 (383) = 31 so represent Bath by setting bit 31 in 0-31 bits:

39 Text signatures Or use several bits: –Bath = [66, 97, 116, 104] Ba mod 32 (66+97) = 3, Bat mod 32 ( ) = 23 ath mod 32 ( ) = 29 th mod 32 ( ) = 28 represent Bath by: This may allow wildcards, eg Bat?

40 Document signatures superimpose keyword signatures Bath tub if each document has 6 keywords, there would be comb(32, 6) = different document signatures. Document signatures can be mapped onto numbers between 1 and

41 Using signature file Boolean query: (A or B) and C –superimpose signatures of A and C –superimpose signatures of B and C –for each signature, S, in the file: if either all bits of A&C are set in S or all bits in B&C are set, retrieve the document with signature S. –check document to see if it is a hit –bit comparisons are very fast compared to string comparisons.

42 Pros and cons of signature files Needs less space than inverted easier to update as documents change fast for queries with many keywords probabilistic - will return false hits cannot filter on positional information cannot hold keyword weights (or other weights) these last three points imply that further processing is required to filter retrieved documents.

43 Summary of key points standard relational databases do not provide suitable indexing for handling index terms. standard SQL is not good at expressing search-engine type queries inverted file structures are purpose made for these types of system storing frequencies/weights in the dictionary and inverted file allows for vector model queries storing positional information allows proximity queries, Knowledge Management v MK Signature files give faster matches but with limitations

44 Questions to think about Explain why the relational model is not good for IR. How is it that, using an inverted file, the number of hits can be reported without retrieving anything from the data file? Could this be achieved using signature files? What are proximity queries, and how can inverted file technology be used to deal with them? How can signature files be used for proximity queries?


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