Automated integration of terminological information into a knowledge base

• US 7,512,575 B2
• Filed: 11/24/2003
• Issued: 03/31/2009
• Est. Priority Date: 03/01/1999
• Status: Expired due to Term

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Mahesh, Kavi
• Primary Examiner(s): STARKS, WILBERT L
• Application Number:
  US10/720,534
• Publication Number:
  US 20040133534A1
• Time in Patent Office: 1,954 Days
• Field of Search: 382/159, 704/9, 706/45
• US Class Current: 706/45
• CPC Class Codes: G06F 40/247 Thesauruses; Synonyms
• CPC Class Codes: G06F 40/44 Statistical methods, e.g. probability models
• CPC Class Codes: G06F 40/45 Example-based machine translation; Alignment
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation

Abstract

A terminological system automates the integration of terminological information into a built-in knowledge base. Input terminology information, which includes input terms and information that specifies relationships among at least two of the input terms, is input to the terminological system. The terminological system parses the input terminology information to generate a logical structure that depicts relationships among the input terms in a format compatible with the built-in knowledge base. Either an independent ontology, comprising the logical structure, is generated, or the knowledge base is extended by logically coupling the logical structure to a node that matches the input term. The terminological system also resolves conflicts if an input term that matches a terminological node in the knowledge base connotes a different meaning than the terminological node. A system that converts broader term and narrower term relationships, synonym relationships, related term (RT) relationships, and preferred term (PT) relationships in accordance with the ISO 2788 standard is disclosed.

Claims

1. A computer implemented method for automating integration of terminological information into a knowledge base, said method comprising the steps of:

receiving, into a computer, input terminology information comprising a plurality of input terms and information that specifies ontological relationships among at least two of said input terms;

storing, in said computer, a knowledge base comprising a plurality of ontologies, each one of said ontologies comprising a plurality of nodes hierarchically arranged to depict ontological relationships among said nodes, each node representing a term;

parsing said input terminology information to generate a logical structure that depicts ontological relationships among said input terms in a format compatible with said knowledge base;

determining whether at least one input term matches a node in said knowledge base;

if not, generating a new and independent ontology for said knowledge base comprising said logical structure of said ontological relationships; and

if so, extending said knowledge base by storing data that logically couples said logical structure of said ontological relationships to a node that matches an input term.

2. The method as set forth in claim 1, wherein said knowledge base is used by a language processing system to classify, search, or retrieve information.

3. A computer implemented method for automating integration of terminological information into a knowledge base, said method comprising the steps of:

receiving, into a computer, input terminology information comprising a plurality of input terms and at least one relationship indicator from a set of predetermined relationship indicators, each relationship indicator specifying an ontological relationship among at least two of said input terms;

storing, in said computer, a knowledge base comprising a plurality of ontologies, each one of said ontologies comprising a plurality of nodes, each node representing a term, and comprising associations among said nodes that depict ontological relationships among respective terms;

generating a logical structure of said input terms from said input terminology information using a mapping table comprising a mapping entry for each relationship indicator in said set of predetermined relationship indicators, each mapping entry comprising a mapping from a relationship indicator to a particular ontological relationship that is in a format compatible with said ontological relationships depicted in said knowledge base; and

integrating said logical structure of said input terms into said knowledge base, said integrating comprising;

determining whether at least one input term matches a node in said knowledge base;

if so, extending said knowledge base by storing data that logically couples said logical structure of said input terms to a node that matches an input term; and

if not, generating a new and independent ontology for said knowledge base comprising said logical structure of said input terms.

4. The method as set forth in claim 3, further comprising:

determining whether an input term that matches a node in said knowledge base connotes a different meaning than said term associated with a node;

if so, then;

deleting said node from its existing one or more associations;

logically coupling any hierarchical associations, if any, with said node so as to by pass said node deleted;

generating a new node for said input term; and

integrating said new node into said knowledge base based on ontological relationships with associated nodes.

5. The method as set forth in claim 3, further comprising generating alternate forms for said input terms prior to integrating said logical structure of said input terms into said knowledge base.

6. The method as set forth in claim 3, wherein receiving input terminology information comprises receiving information in an ISO 2788 format.

7. The method as set forth in claim 3, wherein:

receiving input terminology information comprises receiving broader term (“

BT”

) and narrower term (“

NT”

) relationships among two input terms;

storing a knowledge base comprising associations among said nodes that depict ontological relationships among respective terms comprises storing categories hierarchically arranged to include parent-child relationships and child-parent relationships among categories related hierarchically;

mapping said relationship information comprises mapping BT relationships to parent-child relationships among categories in said knowledge base and comprises mapping NT relationships to child-parent relationships among categories in said knowledge base; and

generating a logical structure comprises generating a parent-child relationship between two terms comprising a BT relationship in said input terminological information, and generating a child-parent relationship between two terms comprising a narrower term (NT) relationship in said input terminological information.

8. The method as set forth in claim 3, wherein:

receiving input terminology information comprises receiving synonym relationships between two terms;

storing a knowledge base comprising associations among said nodes that depict ontological relationships among respective terms comprises storing cross reference associations between nodes;

mapping said relationship information comprises mapping synonym relationships between two terms to cross reference associations between nodes; and

generating a logical structure comprises generating a cross reference association between two terms comprising a synonym relationship in said input terminological information.

9. The method as set forth in claim 3, wherein:

receiving input terminology information comprises receiving related term (“

RT”

) relationships among at least two input terms;

storing a knowledge base comprising associations among said nodes that depict ontological relationships among respective terms comprises storing cross reference associations between nodes;

mapping said relationship information comprises mapping RT relationships between two terms to cross reference associations between nodes; and

generating a logical structure comprises generating a cross reference association between two terms comprising a RT relationship in said input terminological information.

10. The method as set forth in claim 3, wherein:

receiving input terminology information comprises receiving preferred term (“

PT”

) relationships among at least two input terms;

storing a knowledge base comprises storing a canonical/alternate form index that indexes a canonical form from one or more alternative forms; and

generating a logical structure comprises generating a canonical/alternate form index between terms comprising a preferred term (PT) relationship in said input terminological information.

11. The method as set forth in claim 3, wherein said knowledge base is used by a language processing system to classify, search, or retrieve information.

12. A computer readable medium comprising a set of instructions, which when executed, cause the computer to perform the steps of:

receiving, into a computer, input terminology information comprising a plurality of input terms and at least one relationship indicator from a set of predetermined relationship indicators, each relationship indicator specifying an ontological relationship among at least two of said input terms;

storing, in said computer, a knowledge base comprising a plurality of ontologies, each one of said ontologies comprising a plurality of nodes, each node representing a term, and comprising associations among said nodes that depict ontological relationships among respective terms;

generating a logical structure of said input terms from said input terminology information using a mapping table comprising a mapping entry for each relationship indicator in said set of predetermined relationship indicators, each mapping entry comprising a mapping from a relationship indicator to a particular ontological relationship that is in a format compatible with said ontological relationships depicted in said knowledge base; and

integrating said logical structure of said input terms into said knowledge base, said integrating comprising;

determining whether at least one input term matches a node in said knowledge base;

if so, extending said knowledge base by storing data that logically couples said logical structure of said input terms to a node that matches an input term; and

if not, generating a new and independent ontology for said knowledge base comprising said logical structure of said input terms.

13. The computer readable medium as set forth in claim 12, further comprising:

determining whether an input term that matches a node in said knowledge base connotes a different meaning than said term associated with a node;

if so, then;

deleting said node from its existing one or more associations;

logically coupling any hierarchical associations, if any, with said node so as to by pass said node deleted;

generating a new node for said input term; and

integrating said new node into said knowledge base based on ontological relationships with associated nodes.

14. The computer readable medium as set forth in claim 12, further comprising generating alternate forms for said input terms prior to integrating said logical structure of said input terms into said knowledge base.

15. The computer readable medium as set forth in claim 12, wherein receiving input terminology information comprises receiving information in an ISO 2788 format.

16. The computer readable medium as set forth in claim 12, wherein:

receiving input terminology information comprises receiving broader term (“

BT”

) and narrower term (“

NT”

) relationships among two input terms;

storing a knowledge base comprising associations among said nodes that depict ontological relationships among respective terms comprises storing categories hierarchically arranged to include parent-child relationships and child-parent relationships among categories related hierarchically;

mapping said relationship information comprises mapping BT relationships to parent-child relationships among categories in said knowledge base and comprises mapping NT relationships to child-parent relationships among categories in said knowledge base; and

generating a logical structure comprises generating a parent-child relationship between two terms comprising a BT relationship in said input terminological information, and generating a child-parent relationship between two terms comprising a narrower term (NT) relationship in said input terminological information.

17. The computer readable medium as set forth in claim 12, wherein:

receiving input terminology information comprises receiving synonym relationships between two terms;

storing a knowledge base comprising associations among said nodes that depict ontological relationships among respective terms comprises storing cross reference associations between nodes;

mapping said relationship information comprises mapping synonym relationships between two terms to cross reference associations between nodes; and

generating a logical structure comprises generating a cross reference association between two terms comprising a synonym relationship in said input terminological information.

18. The computer readable medium as set forth in claim 12, wherein:

receiving input terminology information comprises receiving related term (“

RT”

) relationships among at least two input terms;

storing a knowledge base comprising associations among said nodes that depict ontological relationships among respective terms comprises storing cross reference associations between nodes;

mapping said relationship information comprises mapping RT relationships between two terms to cross reference associations between nodes; and

generating a logical structure comprises generating a cross reference association between two terms comprising a RT relationship in said input terminological information.

19. The computer readable medium as set forth in claim 12, wherein:

receiving input terminology information comprises receiving preferred term (“

PT”

) relationships among at least two input terms;

storing a knowledge base comprises storing a canonical/alternate form index that indexes a canonical form from one or more alternative forms; and

generating a logical structure comprises generating a canonical/alternate form index between terms comprising a preferred term (PT) relationship in said input terminological information.

20. The computer readable medium as set forth in claim 12, wherein said knowledge base is used by a language processing system to classify, search, or retrieve information.