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COMP3410 DB32: Technologies for Knowledge Management Lecture 7: Query Broadening to improve IR By Eric Atwell, School of Computing, University of Leeds.

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Presentation on theme: "COMP3410 DB32: Technologies for Knowledge Management Lecture 7: Query Broadening to improve IR By Eric Atwell, School of Computing, University of Leeds."— Presentation transcript:

1 COMP3410 DB32: Technologies for Knowledge Management Lecture 7: Query Broadening to improve 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 first we look at a method for query broadening that required input from the user then we look at an automatic method for query broadening using a thesaurus by the end of the lecture you should understand what a thesaurus, terminology-bank, ontology are, and how they are used to broaden queries

4 Some issues to be resolved Synonyms –football / soccer, tap / faucet: search for one, find both? homonyms –lead (metal or leash?), tap: find both, only want one? local/global contexts determine good terms –football articles: wont mention word football; will have particular meaning for the word goal Precoordination (proximity query): multi-word terms –Venetian blind vs blind Venetian

5 Evaluation/Effectiveness measures effort - required by the users in formulation of queries time - between receipt of user query and production of list of hits presentation - of the output coverage - of the collection recall - the fraction of relevant items retrieved precision - the fraction of retrieved items that are relevant user satisfaction – with the retrieved items

6 Better hits: Query Broadening User unaware of collection characteristics is likely to formulate a naïve query query broadening aims to replace the initial query with a new one featuring one or other of: –new index terms –adjusted term weights One method uses feedback information from the user Another method uses a thesaurus / term-bank / ontology

7 From response to initial query, gather relevance information H R = R H = set of retrieved, relevant hits H NR = H-R = set of retrieved, non-relevant hits replace query q with replacement query q' : q' = q d i / |H R | d i / |H NR | note: this moves the query vector closer to the centroid of the relevant retrieved document vectors and further from the centroid of the non-relevant retrieved documents. d i H NR d i H R Relevance Feedback

8 Using terms from relevant documents We expect documents that are similar to one another in meaning (or usefulness) to have similar index terms. The system creates a replacement query (q) based on q, but adds index terms that have been used to index known relevant documents, increases the relative weight of index terms in q that are also found in relevant documents, and reduces the weight of terms found in non-relevant documents.

9 How does this help? It could help if documents were being missed because of the synonym problem. The user uses the word jam, but some recipes use jelly instead. Once a hit that uses jelly has been recognized as relevant, then jelly will appear n the next version of the query. Now hits may use jelly but not jam. Conversely, it can help with the homonym problem. If the user wants references to lead (the metal), and gets documents relating to dog-walking, then by marking the dog-walking references as not relevant, key words associated with dog-walking will be reduced in weight

10 pros and cons of feedback If is set = 0, ignore non-relevant hits, a positive feedback system; often preferred the feedback formula can be applied repeatedly, asking user for relevance information at each iteration relevance feedback is generally considered to be very effective for high-use systems one drawback is that it is not fully automatic.

11 Simple feedback example: T = {pudding, jam, traffic, lane, treacle} d 1 = (0.8, 0.8, 0.0, 0.0, 0.4), d 2 = (0.0, 0.0, 0.9, 0.8, 0.0), d 3 = (0.8, 0.0, 0.0, 0.0, 0.8) d 4 = (0.6, 0.9, 0.5, 0.6, 0.0) Recipe for jam pudding DoT report on traffic lanes Radio item on traffic jam in Pudding Lane Recipe for treacle pudding Display first 2 documents that match the following query: q = (1.0, 0.6, 0.0, 0.0, 0.0) r = (0.91, 0.0, 0.6, 0.73) Retrieved documents are: d 1 : Recipe for jam pudding d 4 : Radio item on traffic jam relevant not relevant

12 Suppose we set and to 0.5, to 0.2 q' = q d i / | H R | d i / | H NR | = 0.5 q d d 4 = 0.5 (1.0, 0.6, 0.0, 0.0, 0.0) (0.8, 0.8, 0.0, 0.0, 0.4) 0.2 (0.6, 0.9, 0.5, 0.6, 0.0) = (0.78, 0.52, 0.1, 0.12, 0.2) (Note |Hn| = 1 and |Hnr| = 1) d i H R d i H NR Positive and Negative Feedback

13 Simple feedback example: T = {pudding, jam, traffic, lane, treacle} d 1 = (0.8, 0.8, 0.0, 0.0, 0.4), d 2 = (0.0, 0.0, 0.9, 0.8, 0.0), d 3 = (0.8, 0.0, 0.0, 0.0, 0.8) d 4 = (0.6, 0.9, 0.5, 0.6, 0.0) Display first 2 documents that match the following query: q = (0.78, 0.52, 0.1, 0.12, 0.2) r = (0.96, 0.0, 0.86, 0.63) Retrieved documents are: d 1 : Recipe for jam pudding d 3 : Recipe for treacle pud relevant

14 Thesaurus a thesaurus or ontology may contain –controlled vocabulary of terms or phrases describing a specific restricted topic, –synonym classes, –hierarchy defining broader terms (hypernyms) and narrower terms (hyponyms) –classes of related terms. a thesaurus or ontology may be: –generic (as Rogets thesaurus, or WordNet) –specific to a certain domain of knowledge, eg medical

15 Language normalisation Content analysis Uncontrolled keywords Thesaurus Index terms User query Normalised query match by replacing words from documents and query words with synonyms from a controlled language, we can improve precision and recall:

16 Thesaurus / Ontology construction Include terms likely to be of value in content analysis for each term, form classes of related words (separate classes for synonyms, hypernyms, hyponyms) form separate classes for each relevant meaning of the word terms in a class should occur with roughly equal frequency (not easy – NL has Zipfs law word-freq ) avoid high-frequency terms it involves some expert judgment that will not be easy to automate.

17 Example thesaurus A public-domain thesaurus (WORDNET) is available from: /home/cserv1_a/staff/nlplib/WordNet/2.0 /home/cserv1_a/staff/extras/nltk/1.4.2/corpora/wordnet computer data processor electronic computer information processing system synonyms (sense 1):

18 Example thesaurus A public-domain thesaurus (WORDNET) is available from: computer calculator reckoner figurer estimator synonyms (sense 2):

19 Hypernym is the generic term used to designate a whole class of specific instances. Y is a hypernym of X if X is a (kind of) Y. Hyponym is the generic term used to designate a member of a class. X is a hyponym of Y if X is a (kind of) Y. Coordinate words are words that have the same hypernym. Hypernym synsets are preceded by "->", and hyponym synsets are preceded by "=>". Terminology (from WordNet Help)

20 Hypernyms Sense 1 computer, data processor, electronic computer, information processing system -> machine -> device -> instrumentality, instrumentation -> artifact, artefact -> object, physical object -> entity, something Hypernym synsets are preceded by "->", and hyponym synsets are preceded by "=>".

21 Hyponyms Sense 1 computer, data processor, electronic computer, information processing system => analog computer, analogue computer => digital computer => node, client, guest => number cruncher => pari-mutuel machine, totalizer, totaliser, totalizator, totalisator => server, host Hypernym synsets are preceded by "->", and hyponym synsets are preceded by "=>".

22 Sense 1 computer, data processor, electronic computer, information processing system -> machine => assembly => calculator, calculating machine => calendar => cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM => computer, data processor, electronic computer, information processing system => concrete mixer, cement mixer => corker => cotton gin, gin => decoder Coordinate terms

23 Thesaurus use replace term in document and/or query with term in controlled language replace term in query with related or broader term to increase recall suggest to user narrower terms to increase precision Doc: Query: Thesaurus computer (sense 1) match S

24 Thesaurus use replace term in document and/or query with term in controlled language replace term in query with related or broader term to increase recall suggest to user narrower terms to increase precision Thesaurus Query: match All collection Query: match All collection B

25 Thesaurus use replace term in document and/or query with term in controlled language replace term in query with related or broader term to increase recall suggest to user narrower terms to increase precision Thesaurus Query: client match All collection match All collection N Query: User

26 Key points a thesaurus or ontology can be used to normalise a vocabulary and queries (?or documents?) it can be used (with some human intervention) to increase recall and precision generic thesaurus/ontology may not be effective in specialized collections and/or queries Semi-automatic construction of thesaurus/ontology based on the retrieved set of documents has produced some promising results.


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