Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2000-02-02
2004-12-07
Robinson, Greta (Department: 2177)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000, C704S009000
Reexamination Certificate
active
06829603
ABSTRACT:
FIELD OF THE INVENTION
This invention relates to database searching and queries, and more particularly to natural language based interactive database searching and queries in network environment.
BACKGROUND OF THE INVENTION
Databases and database search techniques are very well known in the computer arts. Databases have various structures and include any given type of information. In many cases some or all of this information is retrieved by using one or more queries. A query is a request for information from the database that has a structure compatible with the database. Generally, the query is processed in a search that returns results to user.
One common technique for natural language access to databases is to convert natural language sentences to SQL statements. Some examples of SQL statements are shown below:
Query: Show me the names and batting averages of all players who batted above 0.250.
SELECT
Name, Average
FROM
Player
WHERE
Average>0.250
Query: Show me the names and batting averages of all Oriole, Red Sox, and Expo players who batted above 0.300.
SELECT
Player.Name, Average
FROM
Player,Team
WHERE
Average>0.300
AND
Player.Team=Team.Team
AND
Team.Name IN (‘Orioles’, ‘Red Sox’, ‘Expos’)
Query: Show me the sum of all batting averages of all players except these from the White Sox and Diamondbacks.
SELECT
SUM(Average)
FROM
Player,Team
WHERE
Player.Team=Team.Team
AND
Team.Name
NOT IN
(‘White Sox’,‘Diamondbacks’)
A paper titled “Natural Language interfaces to databases—an introduction” by I. Androutsopoulos and G. D. Ritchie, appeared in Natural Language Engineering 1(1): 29-81; 1995 Cambridge University Press, which is herein incorporated by reference in its entirety, presents a history of natural language access to databases and provides a survey of the most significant problems that a program that provides such access must face. State-of-the-art database searching includes interactive search, natural language queries and search via internet. One non-natural language interactive database searching technique is described in U.S. Pat. No. 5,426,781 entitled “Computerized report-based interactive database query interface” that discloses a method and system for interactively and iteratively constructing a query using a table metaphor displayed on a user display. Alterations are made directly to the table metaphor by the database user. The alterations relate to adding, deleting, or combining columns of attributes and limiting ranges of attribute values. The alterations are registered and the table metaphor updated to reflect the registered alterations. The table metaphor can be repeatedly used to further register additional alterations. The query corresponding to the table metaphor in its final form is run against the full database to generate a report in the format indicated by the table metaphor.
Using natural language queries to access the information system is also well known. U.S. Pat. No. 5,574,908 entitled “Method and apparatus for generating a query to an information system specified using natural language-like constructs” (herein incorporated by reference in its entirety) discloses an apparatus for generating a query to an information system using a drag-and-drop information system specification means utilizing a computer language having both textual and graphical forms for translating natural language-like constructs into object-role modeling symbology.
Doing database searching over a general network, e.g. the internet, an intranet, etc. is also well known. In this type of database searching, one or more clients generate a query that is transmitted over the network, a process running on a search processes the query against one or more databases, and returns result to the client back over the network.
U.S. Pat. No. 5,737,592 entitled “Accessing a relational database over the Internet using macro language files” (herein incorporated by reference in its entirety) discloses a method for executing Structured Query Language (SQL) queries in a computer-implemented relational database management system via a network.
One popular way of searching over a network (Internet) is to use a search engine. Most search engines are keyword based search such as YAHOO (http://www.yahoo.com), LYCOS (http://www.lycos.com) etc., where no user interaction is supported. The user is asked to input the keywords that best represent their interests, then the search engine will look for those keywords (and possibly the synonyms of those keywords) against the document collections. Where a match is found in the document, that document will be retrieved and presented to the user. A typical user is forced to manually go through the many “matches” for a query and find the relevant information herself.
Similar procedures are in place for searching for products. The customers either have to go through a possibly long series of clicking the hyperlinks, or use one of the search mechanisms described above.
Recently, some websites (www.AskJeeves.com, www.Neuromedia.com) have started search operations on question-answer mode. Natural language search engines, such as AskJeeves, use a relatively simple technical approach of keywords, and templates to give the user a feeling of a “natural language interface”. For example, a query “What is the capital of Kenya?” returns a pointer to several Web sites including one about Kenya where the correct answer is included in the text. However, a question “How long does it take to fly from London to Paris on the Concorde?” produces a set of similar questions to the one asked however none of them is related to the answer—example: “Where can I find cheap flights from the UK?”. The method used to produce answers seems to consist of a 5-steps: (a) partly parse the query; (b) map to a canned set of questions/question-templates; (c) map canned questions go to existing knowledge bases (AskJeeves points to other people's web sites for the real data/FAQs.); (d) do a meta search on the 5 big search engines (and return their results too); and (e) if there was no match in “b” then record the query for later human analysis. Note that “b” is essentially a person-intensive task-creating the list of key phrases and the canned questions they map to (and then the underlying web pages they map to). Such systems provide a reasonable front end to a large knowledge base/FAQ. They are better than a raw search engine, because they have the human touch of mapping phrases to canned questions/templates (backed up with the search engines).
Other sites, such as Neuromedia (www.neuromedia.com), BigScience(www.bigscience.com), Novator(www.novator.com), PersonalLogic (www.personallogic.com) try to offer more interactivity to the user. By interactivity we mean the capability of a system to jointly define parameters required for mutual understanding in a series of exchanges. These might be some action parameters, such as Amount, Account_to, Account_from for transferring money, or a set of preferences for a computer notebook. These parameters may be established either by user providing information to the system or the system suggesting some or all of them. What is important is that the system remembers current (and possibly previous) user's preferences, and is using this information in an intelligent manner to make the interaction more satisfying for the user. The above sites, offer more interactivity, by extending the question answer mode of operation with contextual history in the interaction.
PROBLEMS WITH PRIOR ART
The prior art systems fail primarily in three areas:
1. Efficiency: many rounds of interaction are needed to accomplish a task. A typical buying request on average takes about 20 mouse clicks.
2. Lack of deeper understanding of queries. Natural language engines such as AskJeeves cannot be used to accomplish transactions, such as buying clothes, because: (a) a keyword search cannot understand that “summer dress” should be looked upon in women's clothing dept. under “dresses” and “dress shirt” most likely in men's under “shirts”, and (
Chai Joyce Yue
Fujisaki Tetsunosuke
Govindappa Sunil Subramanyam
Kambhatla Nandakishore
Radev Dragomir Radkov
International Business Machines Corp.
Law Office of Charles W. Peterson, Jr.
Percello Louis J.
Robinson Greta
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