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Searching Periodical Databases MIT 026B Winter 2002.

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Presentation on theme: "Searching Periodical Databases MIT 026B Winter 2002."— Presentation transcript:

1 Searching Periodical Databases MIT 026B Winter 2002

2 Database Records Accession Number Title Author Journal Language Abstract Descriptors “Record” “Fields”

3 CONTROLLED VOCABULARY Authors Descriptors

4 NATURAL LANGUAGE Title Abstract

5 Truncation Natur* Nature Natures Natural Naturalist Naturalistic Naturalism

6 Wildcards M?cDonald McDonald MacDonald

7 Positional Operators ADJ ADJ3 WITH NEAR

8 Training cats and dogs as pets Search for articles about:

9 Training cats and dogs as pets TrainingDogs CatsPets Search for articles about:

10 List of Documents How to train a dog to kill people. The difference between dogs and cats. Training cats as pets. Training dogs as pets. Training your skunk to be a pet. Why dogs make good pets. How to train a cat to kill people.

11 List of Documents How to train a dog to kill people. The difference between dogs and cats. Training cats as pets. Training dogs as pets. Training your skunk to be a pet. Why dogs make good pets. How to train a cat to kill people.

12 List of Documents How to train a dog to kill people. The difference between dogs and cats. Training cats as pets. Training dogs as pets. Training your skunk to be a pet. Why dogs make good pets. How to train a cat to kill people.

13 List of Documents How to train a dog to kill people. The difference between dogs and cats. Training cats as pets. Training dogs as pets. Training your skunk to be a pet. Why dogs make good pets. How to train a cat to kill people.

14 Document/Term Matrix Train*Dog*Cat*Pet* Doc. 1xx Doc. 2xx Doc. 3xxx Doc. 4xxx Doc. 5xx Doc. 6xx Doc. 7xx

15 Document Sets “Train”={d1, d3, d4, d5, d7} “Dog” ={d1, d2, d4, d6} “Cat”={d2, d3, d7} “Pet”={d3, d4, d5, d6}

16 “Dog” AND “Cat” d1 d4 d6 d3 d7 d2 dog AND cat = {d2}

17 “Dog” OR “Cat” d6 d1 d4 d2 d3 d7 dog OR cat = {d1, d2, d3, d4, d6, d7}

18 “Dog” NOT “Cat” d6 d1 d4 d2 d3 d7 dog NOT cat = {d1, d4, d6}

19 Hints for Searching Electronic Databases Plan ahead Don’t get discouraged Be prepared to repeat the search, using different strategies Use BOTH free-text AND controlled vocabulary terms

20 Recall vs. Precision Low High Low Recall Precision Search Results Building Blocks Citation Pearl Growing

21 Building Blocks Approach Identify the different concepts, or “facets” of the topic you wish to search Create a series of terms for each concept Combine the terms for each concept together into a single set, using the “OR” operator Combine the sets together, using “AND,” “OR,” or “NOT.”

22 Topic: Ecological concerns in American poetry

23 Concept Map EcologyUnited StatesPoetry Environment*United StatesPoe* Ecolog*America*

24 Building Blocks Approach Concept 1 Concept 2 Concept 3 Term 1a OR Term 1b OR Term 1c Term 2a OR Term 2b OR Term 2c Term 3a OR Term 3b OR Term 3c ANSWER SET AND…OR…NOT

25 Building Blocks Approach Concept 1 Concept 2 Concept 3 AND…OR…NOT

26 Successive Fractions Approach Concept 1 Concept 2 Concept 3 Term 1a OR Term 1b OR Term 1c Term 2a OR Term 2b OR Term 2c Term 3a OR Term 3b OR Term 3c INTERIM SET AND…OR…NOT ANSWER SET AND…OR…NOT

27 Fun with Your Thesaurus Novel (100 citations) Narrower terms: –American novel (25 citations) –Bildungsroman novel (25 citations) –Canadian novel (25 citations) –Postmodern novel (25 citations)

28 Searching the term, “Novel”: Novel100

29 “Exploding” a Term Searching the citations for a term AS WELL AS the citations for all the NARROWER terms under it Novel 100 American novel25 Bildungsroman25 Canadian novel25 Postmodern novel25 200 Citations

30 Citation Pearl Growing Start with 1 or a few documents that are KNOWN to be relevant to your search. Investigate the descriptors and natural language terms in the documents and develop a list of concepts Use these concepts to find more terms, and collect more documents Repeat the process as necessary/desired.

31 Citation Pearl Growing Title Author Abstract Descriptors

32 Citation Pearl Growing Title Author Abstract Descriptors Title Author Abstract Descriptors

33 Knowledge Growth General Relativity Quantum Theory (quantum ADJ theory) AND (general ADJ relativity): 45 45

34 Knowledge Discovery Vibration Theory Quantum Theory (quantum ADJ theory) AND (vibration ADJ theory): 0

35 Knowledge Discovery The detection of patterns in large data stores which leads to the extraction of previously unknown, potentially useful knowledge.

36 Knowledge Discovery Vibration Theory Quantum Theory


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