1. LIS618 lecture 6 Thomas Krichel 2003-03-05

2. structure DIALOG –basic vs additional index –initial database file selection (files) Lexis/Nexis

3. basic vs additional index the basic index –has information that is relevant to the substantive contents of the data –usually is indexed by word, i.e. connectors are required the additional index –has data that is not relevant to the substantive matter –usually indexed by phrase, i.e. connectors are not required

4. search options: basic index select without qualifiers searches in all fields in the basic index bluesheet lists field indicators available for a database also note if field is indexed by word or phrase. proximity searching only works with word indices. when phrases are indexed you don't need proximity indicators

5. search in basic index a field in the basic index is queried through term/IN, where term is a search term and IN is a field indicator Thomas calls this a appending indicator several field indicators can be ORed by giving a comma separated list for example mate/ti,de searches for mate in the title or descriptor fields

6. limiters and sorting Some databases allow to restrict the search using limiters. For example –/ABSrequire abstract present –/ENGEnglish language publication Some fields are sortable with the sort command, i.e. records can be sorted by the values in the fields. Example: sort /ti Such features are database specific.

7. additional indices additional indices lists those terms that can lead a query. Often, these are phrase indexed. Such fields a queried by prefix IN=term where IN is the field abbreviator and term is the search term Thomas calls this a pre-pending indicator

8. expanding queries names have to be entered as they appear in the database. The "expand" command can be used to see varieties of spelling of a value It has to be used in conjunction with a field identifier, example expand au=cruz, b? to search for misspellings of José Manuel Barrueco Cruz

9. expanding queries II search produces results of the form Ref Items Index-term –Ref is a reference number –Items is the number of items where the index term appears –Index-term is the index term "s Ref" searches for the reference term.

10. add/repeat add number, number adds databases by files to the last query example "add 297" to see what the bible says about it repeat repeats previous query with database added

11. Initial file selection On the main menu, go to the database menu. After the principle menu, you get a search box There you can enter full-text queries for all the databases You can then select the database you want And get to the begin databases stage.

12. database categories In order to help people to find databases (files), DIALOG have grouped databases by categories. categories are listed at http://library.dialog.com/bluesheets/html/blo.html http://library.dialog.com/bluesheets 'b category' will select databases from the category category at the start. 'sf category' selects files belonging to a category category at other times.

13. Nexis

14. Lexis/Nexis Lexis is a specialized legal research service Nexis is primarily a news services adds an important temporal component to all its contents restricts contents as compared to Dialog potentially bad competition from Google

15. compilation of Nexis Uses a number of news sources such as newspapers. Uses company reports databases Uses web sites, the URLs of which are found in the news sources There is a problem with quality control of web sites, some pornographic sites are included

16. Smart indexing Nexis keep a list of terms that are used for indexing. A computer program will relate synonyms to an official term. –Example: replace LIU with Long Island University Queries are not pre-processed.

17. Nexis basic search implicit Boolean "or" between terms Otherwise double quotes for in fact searches –Smart index keywords extracted –HLEAD for news –TITLE for legal documents –WEB-SEARCH-TEXT for web pages

18. relevance ranking Lexis is based on the vector model The precise relevance ranking seems a company secret. Ranking depends on –where terms appear within the document –how many occurrences of the search terms appear in the document –how often those search terms appear throughout the document