Enhancing enterprise service design knowledge using ontology-based clustering

• US 9,177,289 B2
• Filed: 05/03/2012
• Issued: 11/03/2015
• Est. Priority Date: 05/03/2012
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP SE
• Inventors: Roy, Marcus, Wittstruck, Nicholas Alexander
• Primary Examiner(s): Reyes, Mariela
• Assistant Examiner(s): HARMON, COURTNEY N
• Application Number:
  US13/463,053
• Publication Number:
  US 20130297617A1
• Time in Patent Office: 1,279 Days
• Field of Search: 707/748
• US Class Current: 1/1
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

Methods, systems, and computer-readable storage media for extending a knowledge base, implementations including receiving an enterprise service signature (ESS) associated with an enterprise service (ES), the ESS being stored in a computer-readable repository, segmenting the ESS to provide a segmented ESS, identifying one or more unknown terms from the segmented ESS, determining that at least one unknown term of the one or more unknown terms comprises at least one of a specialization and a new entity, and extending the knowledge base in view of the at least one of the specialization and the new entity.

Claims

1. A computer-implemented method for extending a knowledge base, the method being executed using one or more processors and comprising:

receiving an enterprise service signature (ESS) associated with an enterprise service (ES), the ESS being stored in a computer-readable repository, the ES comprising a callable service that provides business functionality, the ESS identifying the ES and comprising a concatenation of terms and a notation;

segmenting, by the one or more processors, the ESS based on the notation to provide a segmented ESS comprising an array of terms;

comparing, by the one or more processors, terms of the array of terms to metadata of a taxonomic scheme, the metadata comprising one or more neighboring entities of a respective term in the array of terms;

identifying, by the one or more processors and based on the comparing, one or more known terms and one or more unknown terms from the segmented ESS, the one or more known terms matching the taxonomic scheme and the one or more unknown terms not being associated with the taxonomic scheme;

determining, by the one or more processors, that at least one unknown term of the one or more unknown terms comprises at least one of a specialization and a new entity based on combining a cohesion value and a correlation value, the cohesion value being based on a number of times the at least one unknown term is used with one of the one or more known terms and the correlation value being based on a number of times the at least one unknown term is used with other known terms; and

extending, by the one or more processors, the knowledge base in view of the at least one of the specialization and the new entity.

2. The method of claim 1, wherein determining that the at least one unknown term comprises at least one of a specialization and a new entity comprises:

determining one or more relationship measures associated with the at least one unknown term; and

identifying the at least one unknown term as a specialization or a new entity.

3. The method of claim 2, wherein the relationship measures comprise the cohesion value and the correlation value.

4. The method of claim 2, wherein the relationship measures are each determined based on known terms associated with one or more other ESSs.

5. The method of claim 3, wherein identifying the at least one unknown term as a specialization or a new entity comprises:

combining the cohesion value and the correlation value into a single score based on a ranking function; and

comparing the single score to one or more threshold scores, wherein whether the at least one unknown term is a specialization or a new entity is based on the comparing.

6. The method of claim 5, wherein combining the cohesion value and the correlation value into a single score is further based on a kernel function, the kernel function providing an approximation of ranking behavior.

7. The method of claim 1, wherein extending the knowledge base comprises appending one or more nodes to a metadata graph associated with the ES.

8. The method of claim 1, wherein extending the knowledge base comprises automatically generating an automata based on the at least one unknown term, the automata representing a naming convention of an ES.

9. The method of claim 8, wherein the at least one unknown term is a specialization and automatically generating the automata comprises appending a new transition to a pre-defined automata.

10. The method of claim 9, wherein the new transition is appended to be parallel to an existing transition provided in the pre-defined automata.

11. The method of claim 8, wherein the at least one unknown term is a new entity and automatically generating the automata comprises appending a new state and one or more associated transitions to a pre-defined automata.

12. The method of claim 11, wherein the new state is appended to be serial to one or more existing states provided in the pre-defined automata.

13. A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for extending a knowledge base, the operations comprising:

receiving an enterprise service signature (ESS) associated with an enterprise service (ES), the ESS being stored in a computer-readable repository, the ES comprising a callable service that provides business functionality, the ESS identifying the ES and comprising a concatenation of terms and a notation;

segmenting the ESS based on the notation to provide a segmented ESS comprising an array of terms;

comparing terms of the array of terms to metadata of a taxonomic scheme, the metadata comprising one or more neighboring entities of a respective term in the array of terms;

identifying based on the comparing, one or more known terms and one or more unknown terms from the segmented ESS, the one or more known terms matching the taxonomic scheme and the one or more unknown terms not being associated with the taxonomic scheme;

determining that at least one unknown term of the one or more unknown terms comprises at least one of a specialization and a new entity based on combining a cohesion value and a correlation value, the cohesion value being based on a number of times the at least one unknown term is used with one of the one or more known terms and the correlation value being based on a number of times the at least one unknown term is used with other known terms; and

extending the knowledge base in view of the at least one of the specialization and the new entity.

14. A system, comprising:

a computing device; and

a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations for extending a knowledge base, the operations comprising;

receiving an enterprise service signature (ESS) associated with an enterprise service (ES), the ESS being stored in a computer-readable repository, the ES comprising a callable service that provides business functionality, the ESS identifying the ES and comprising a concatenation of terms and a notation;

segmenting the ESS based on the notation-to provide a segmented ESS comprising an array of terms;

comparing terms of the array of terms to metadata of a taxonomic scheme, the metadata comprising one or more neighboring entities of a respective term in the array of terms;

identifying based on the comparing, one or more known terms and one or more unknown terms from the segmented ESS, the one or more known terms matching the taxonomic scheme and the one or more unknown terms not being associated with the taxonomic scheme;

determining that at least one unknown term of the one or more unknown terms comprises at least one of a specialization and a new entity based on combining a cohesion value and a correlation value, the cohesion value being based on a number of times the at least one unknown term is used with one of the one or more known terms and the correlation value being based on a number of times the at least one unknown term is used with other known terms; and

extending the knowledge base in view of the at least one of the specialization and the new entity.