EXPERT DISCOVERY VIA SEARCH IN SHARED CONTENT
Determining experts based on a search query of a user includes identifying items in a content collection that correspond to the search query, determining authors of the items, and ranking the authors according to relevance to the search query for each of the items for each of the authors. Determining experts based on a search query of a user may also include complementing the query with additional public search results prior to identifying the items. Complementing the query may include using an external data source to search based on the query. The external data source may be selected from the group consisting of Google Search, Yahoo Search, and Microsoft Bing. Determining experts based on a search query of a user may also include presenting the authors to the user in order of ranking The query may be a natural language query.
1 . A method of determining experts based on a search query of a user, comprising:
identifying items in a content collection that correspond to the search query;
determining authors of the items; and
ranking the authors according to relevance to the search query for each of the items for each of the authors.
2 . A method, according to claim 1 , further comprising:
identifying additional items in a supplemental content collection that correspond to the search query;
determining additional authors of the additional items; and
ranking the authors and the additional authors according to relevance to the search query for each of the items and each of the additional items for each of the authors and each of the additional authors.
3 . A method, according to claim 2 , wherein the content collection is a private database and the supplemental content collection is a public database.
4 . A method, according to claim 1 , further comprising:
complementing the query with additional public search results prior to identifying the items.
5 . A method, according to claim 4 , wherein complementing the query includes using an external data source to search based on the query.
6 . A method, according to claim 5 , wherein the external data source is selected from the group consisting of Google Search, Yahoo Search, and Microsoft Bing.
7 . A method, according to claim 1 , further comprising:
presenting the authors to the user in order of ranking
8 . A method, according to claim 7 , wherein the user is provided with additional information indicating the basis of the ranking.
9 . A method, according to claim 8 , wherein the additional information indicating the basis of the ranking is shown to the user according to access privileges of the user.
10 . A method, according to claim 1 , wherein the query is a natural language query.
11 . A method, according to claim 1 , wherein identifying items in a content collection that correspond to the search query is based on linguistic similarity.
12 . A method, according to claim 11 , wherein linguistic similarity varies according to a product of term frequency and inverse document frequency of terms in the query and an item.
13 . A method, according to claim 1 , wherein ranking the authors includes evaluating an amount of contribution of an item and relevance of the item to the query.
14 . A method, according to claim 13 , wherein evaluating an amount of contribution includes providing different weights to different portions of items of the collection.
15 . A method, according to claim 14 , wherein the different portions include a title, a main content portion, and tags.
16 . Computer software, provided in a non-transitory computer-readable medium, that determines experts based on a search query of a user, the software comprising:
executable code that identifies items in a content collection that correspond to the search query;
executable code that determines authors of the items; and
executable code that ranks the authors according to relevance to the search query for each of the items for each of the authors.
17 . Computer software, according to claim 16 , further comprising:
executable code that identifies additional items in a supplemental content collection that correspond to the search query;
executable code that determines additional authors of the additional items; and
executable code that ranks the authors and the additional authors according to relevance to the search query for each of the items and each of the additional items for each of the authors and each of the additional authors.
18 . Computer software, according to claim 17 , wherein the content collection is a private database and the supplemental content collection is a public database.
19 . Computer software, according to claim 16 , further comprising:
executable code that complements the query with additional public search results prior to identifying the items.
20 . Computer software, according to claim 19 , wherein complementing the query includes using an external data source to search based on the query.
21 . Computer software, according to claim 20 , wherein the external data source is selected from the group consisting of Google Search, Yahoo Search, and Microsoft Bing.
22 . Computer software, according to claim 16 , further comprising:
executable code that presents the authors to the user in order of ranking
23 . Computer software, according to claim 22 , wherein the user is provided with additional information indicating the basis of the ranking.
24 . Computer software, according to claim 23 , wherein the additional information indicating the basis of the ranking is shown to the user according to access privileges of the user.
25 . Computer software, according to claim 16 , wherein the query is a natural language query.
26 . Computer software, according to claim 16 , wherein executable code that identifies items in a content collection that correspond to the search query uses linguistic similarity.
27 . Computer software, according to claim 26 , wherein linguistic similarity varies according to a product of term frequency and inverse document frequency of terms in the query and an item.
28 . Computer software, according to claim 16 , wherein executable code that ranks the authors evaluates an amount of contribution of an item and relevance of the item to the query.
29 . Computer software, according to claim 28 , wherein evaluating an amount of contribution includes providing different weights to different portions of items of the collection.
30 . Computer software, according to claim 29 , wherein the different portions include a title, a main content portion, and tags.