Knowledge base system for setting attribute value derivation data independently from attribute value derivation procedure and shared data management apparatus for selectively locking attribute

• US 5,651,101 A
• Filed: 08/30/1995
• Issued: 07/22/1997
• Est. Priority Date: 08/31/1994
• Status: Expired due to Fees

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Tsutsumitake, Yasuko, Sasaki, Kouichi, Gotoh, Kazuyuki
• Primary Examiner(s): Kulik, Paul V.
• Application Number:
  US08/521,588
• Time in Patent Office: 692 Days
• Field of Search: 395/601, 395/608, 395/611, 395/614, 395/616, 395/617, 395/60, 395/62, 395/50, 395/54, 395/65
• US Class Current: 706/53
• CPC Class Codes: G06N 5/027 Frames
• CPC Class Codes: Y10S 707/99938 Concurrency, e.g. lock management in shared database

Abstract

A method-to-derive-attribute-value is set of a procedure inherent to an attribute for deriving an attribute value at each attribute defined in each class in knowledge base. A data-for-value-derivation referred by the set method-to-derive-attribute-value is set at each class defined or inherited by the attribute independently from the method-to-derive-attribute-value. The attribute value is derived by referring to the data-for-value-derivation to be input as the specification of the instance by executing the method-to-derive-attribute-value when the instance is prepared as novel or altered, to thereby set the attribute value of each attribute of the instance.

Claims

1. A knowledge base system comprising:

means for setting method-to-derive-attribute-value of a procedure inherent for an attribute for deriving an attribute value at each attribute defined for each class in a knowledge base;

means for setting data-for-value-derivation referred by the method-to-derive-attribute-value thus set independently from said method-to-derive-attribute-value at each class defined or inherited by the attribute; and

means for setting an attribute value of each attribute of an instance by deriving the attribute value by referring to the data-for-value-derivation to be input as specification of said instance or the data-for-value-derivation set to the attribute of said class corresponding to said instance and deriving the attribute value by executing said method-to-derive-attribute-value at each attribute when the instance is prepared as novel or altered.

2. A knowledge base system according to claim 1, further comprising:

means for determining a class corresponding to the instance to be prepared as novel by referring to said data-for-value-derivation.

3. A knowledge base system according to claim 2, wherein said means for determining the class corresponding to the instance to be prepared as novel by referring to said data-for-value-derivation comprises:

means for setting method-to-evaluate-similarity-of-attribute-patterns for evaluating similarity of the data-for-value-derivation of the attribute in the specification of the input instance to be prepared as novel to the data-for-value-derivation set to the attribute of said class corresponding to said instance at each attribute defined in each class in said knowledge base;

means for calculating the similarity of each class to the specification of the input instance to be prepared as novel at each subclass of all the designated classes as a value obtained by dividing total sum of the similarities evaluated by executing said method-to-evaluate-similarity-of-attribute-patterns at each attribute by number of the attributes; and

means for selecting the class having the maximum similarity thus calculated or the class having the similarity larger than a predetermined threshold value as a candidate of the class corresponding to the instance to be prepared as novel.

4. A knowledge base system according to claim 3, further comprising:

means for evaluating the similarity of said data-for-value-derivation set to each attribute as the specification of the class to be added as novel to said knowledge base to said data-for-value-derivation set to the attribute of said existing class in said knowledge base by said method-to-evaluate-similarity-of-attribute-patterns set to the attribute of said existing class; and

means for calculating the similarity of each existing class to the specification of the input class to be added as novel at each existing subclass of all the designated existing classes by executing said method-to-evaluate-similarity-of-attribute-patterns at each attribute.

5. A knowledge base system according to claim 3, further comprising:

means for designating an attribute for evaluating the similarity to calculate the similarity of the input specification to the class in said input specification when said class is selected.

6. A knowledge base system according to claim 3, further comprising:

means for displaying a subtree of a hierarchy including all candidate classes obtained as a selected result of said class and displaying a class name, calculated similarity, attribute contributing to calculation of the similarity, its data-for-value-derivation and information regarding existing instance of the class at each node for representing each candidate class; and

means for selecting the class from the candidate class by designating the node on the subtree of displayed said class hierarchy.

7. A knowledge base system according to claim 1, further comprising:

means for selectively locking an edit request for each attribute value derived by referring to said deriving data.

8. A shared data management apparatus comprising:

first storage means for simultaneously storing data to be edited from a plurality of input tools;

second storage means for storing as knowledge a type of edit to be executed by said input tool, type of attribute of data to be edited and an operation status of said tool at each input tool for editing the data;

third storage means for storing as knowledge dependence relation between the data, dependence relation between the attributes of the data, edit status of each data and relation between edited results executed by said input tool;

means for deriving a limit to be applied to said edit request to avoid occurrence of collision of the edit of the same data based on knowledge stored in said second and third storage means in response to the edit request for said data given from said input tool;

means for accepting the edit request of said tool with limit when the limit to be applied to said edit request is derived; and

means for deriving a cause of occurrence of the collision of the edit for the same data when the limit to be applied to said edit request is not derived, deriving an operation to be executed on said other input tool to execute the edit as said edit request based on the operation status of the other input tool as the cause of the occurrence of the collision and proposing the cause of the occurrence of the derived collision and the operation to be executed.