AIIM icp Search

Question 1

Crawler

Answer 1

a machine that goes through the text and metadata to find things in the system

Question 2

Search engine index

Answer 2

creates and organizes a database of keywords for all the different docs

Question 3

Query Engine

Answer 3

plain language or boolean actually searches the documents for the words being searched

Question 4

Human-powered directories

Answer 4

the directories are created by humans (mahalo) may be subjective

Question 5

hybrid search engines

Answer 5

uses query and human powered directories (like google or yahoo)

Question 6

homogeneous search

Answer 6

one search tool, multiple repository indexes

Question 7

federated search

Answer 7

multiple search tools, multiple repository indexes (merlot)

Question 8

universal search

Answer 8

one search tool, one repository index (disregards any other search tools or pre-existing repository indexes)

Question 9

application search

Answer 9

built in searches in specific applications (like email search etc) keeps application security in place (but only limited to the application data)

Question 10

Parametric search

Answer 10

rules based, fielded search. look at attributes (parameters) already built-in to the documents. Most precise but only limited to the declared fields

Question 11

keyword search

Answer 11

a type of parametric search, they are set by the users. May be more precise because it won’t rely on only the words in the document and it applies human reasoning to the document a little inflexible since dependant on humans

Question 12

semantic / pattern search Natural language search

Answer 12

looks for the meaning behind the words, not just the specific word.

Question 13

Statistical search

Answer 13

using baseian probabilities looking on search. Also not based on language so it can fit no matter the language. uses relevancy ranking

Question 14

Concept and fuzzy search

Answer 14

looks on synonyms and simpliar spellling etc. but can get large lists.

Question 15

Concept clustering

Answer 15

using algorithims to profile the concepts and compared to the other documents creates a large organization of overlapping concepts

Question 16

Social search

Answer 16

looking at all the social search and creating more relevant documents. looking a docs your friends have liked

Question 17

Search Engine Optimization (SEO)

Answer 17

Works on content rather than the index to make the documents more “findable” ambient findability

Question 18

Effective keyword use

Answer 18

not just in text, but title meta, header of html and xml docs

Question 19

effective link-building

Answer 19

adding more links to make more effective, also plain-lanugage urls, image tags

Question 20

use of social tools

Answer 20

organizations wikis, tweet, and social to highlight the best documents for people

Question 21

thesaurus

Answer 21

manages and tracks the definitions of words and phrases and their relationships to one another in a heiarchical fashion to correlate between groups and applications

Question 22

semantic networks

Answer 22

like thesaurus but a higher view, using a metadata based infrastructure to connect search terms to other related terms

Question 23

comparison technics

Answer 23

going through and weeding out duplicates from results

Question 24

semantic feaure extraction and comparison

Answer 24

we use words to discribe things so if document uses the a portion of the same words to describe the document then it is likely referring to the same or similar thing

Question 25

hash code

Answer 25

a mathamatical description of a document, if identical then, maybe same document if not they are not same document