Automated ontology development

• US 10,679,134 B2
• Filed: 07/01/2019
• Issued: 06/09/2020
• Est. Priority Date: 02/06/2013
• Status: Active Grant

• Current Assignee: Verint Systems Incorporated
• Original Assignee: Verint Systems Limited (Verint Systems Incorporated)
• Inventors: Romano, Roni, Horesh, Yair, Dreyfuss, Jeremie
• Primary Examiner(s): Opsasnick, Michael N
• Application Number:
  US16/458,482
• Publication Number:
  US 20190325324A1
• Time in Patent Office: 344 Days
• Field of Search: None
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

Systems and methods of automated ontology development include a corpus of communication data. The corpus of communication data includes communication data from a plurality of interactions and is processed. A plurality of terms are extracted from the corpus. Each term of the plurality is a plurality of words that identify a single concept within the corpus. An ontology is automatedly generated from the extracted terms.

Claims

1. A method of automated ontology development for processing communication data via a computer system, wherein the ontology is a structural representation of language elements and relationships between those language elements within a domain stored in the memory of the computer system the method comprising:

processing a corpus of communication data, the corpus comprising communication data from a plurality of interactions;

extracting a plurality of terms from the corpus, wherein each term of the plurality is a plurality of words that identify a single concept within the corpus;

automatedly generating an ontology from the extracted term by at least creating two context vectors for each of the plurality of terms and comparing the context vectors for each of the plurality of terms to one another to categorize the terms into a plurality of relations, wherein a first of the two context vectors of a given term predicts terms that will appear to the left of the given term based on a calculated score for terms to the left of the given term, wherein a second of the two context vectors predicts terms that will appear to the right of the given term based on a calculated score for terms to the right of the given term; and

storing the automatedly generated ontology in an ontology database in the memory of the computer system.

2. The method of claim 1, wherein processing the corpus further comprises:

receiving raw communication data; and

applying a rank filter to select a portion of the raw communication data as the corpus of communication data.

3. The method of claim 2, wherein the raw communication data comprises transcriptions of interactions, agent scripts, service manuals, and product manuals.

4. The method of claim 1, wherein processing the corpus further comprises:

identifying scripts within the corpus, wherein scripts are recurring patterns of three or more words.

5. The method of claim 4, wherein processing the corpus further comprises:

zoning the communication data to segment the communication data into meaning units.

6. The method of claim 5, wherein the plurality of terms are extracted from the corpus on a meaning unit-by-meaning unit basis.

7. The method of claim 1, wherein the plurality of interactions are customer service interactions and the ontology is tailored for use in analyzing customer service interactions.

8. The method of claim 1, wherein the ontology comprises a plurality of terms, a plurality of relations, and a plurality of themes identified from the corpus.

9. The method of claim 1, wherein the plurality of interactions is from multiple platforms.

10. The method of claim 1, wherein the first of the two context vectors of a given term is a list of terms that predicts terms that will appear to the left of a given term, the second of the two context vectors is a second list of terms that predicts terms that will appear to the right of the given term, and each of the context vectors includes up to a predetermined number of potential terms in the first or second list of terms.

11. The method of claim 1, wherein automatedly generating the ontology further comprises:

comparing the plurality of relations to one another to categorize the relations into a plurality of themes.

12. A method of automated ontology development, the method comprising:

processing a corpus of communication data, the corpus comprising communication data from a plurality of interactions, by zoning the communication data to segment the communication data into a plurality of meaning units;

extracting a plurality of terms from each of the plurality of meaning units, wherein each term of the plurality is a plurality of words that identify a single concept within the corpus;

automatedly generating an ontology that comprises the extracted terms by at least creating two context vectors for each of the plurality of terms and comparing the context vectors for each of the plurality of terms to one another to categorize the terms into a plurality of relations, wherein a first of the two context vectors of a given term predicts terms that will appear to the left of the given term based on a calculated score for terms to the left of the given term, wherein a second of the two context vectors predicts terms that will appear to the right of the given term based on a calculated score for terms to the right of the given term; and

storing the automatedly generated ontology in an ontology database.

13. The method of claim 12, wherein processing the corpus further comprises:

receiving raw communication data; and

applying a rank filter to select a portion of the raw communication data as the corpus of communication data.

14. The method of claim 13, wherein the rank filter selects data files from the raw communication data that include a threshold of identified related terms to the domain of the ontology that is to be developed.

15. The method of claim 14, wherein the raw communication data comprises interaction data from the interactions from multiple platforms including interactions made via one or more of by phone, email, internee chat, text message, web page comment, social media interaction, customer surveys, an audio recording, streaming audio, a transcription of spoken content, or written correspondence.

16. The method of claim 12, wherein automatedly generating the ontology further comprises:

comparing the plurality of relations to one another to categorize the relations into a plurality of themes.

17. The method of claim 16, wherein the ontology further comprises the plurality of relations and the plurality of themes.

18. A system for automated ontology development, the system comprising:

a communication data database populated with communication data;

a processor communicatively connected to the database of communication data and communicatively connected to a computer readable medium programmed with computer readable code that upon execution by the processor causes the processor to;

process a corpus of communication data received from the database;

extract a plurality of terms from the corpus, wherein each term of the plurality is a plurality of words that identify a single concept within the corpus; and

automatedly generate an ontology from the extracted terms by at least creating two context vectors for each of the plurality of terms and comparing the context vectors for each of the plurality of terms to one another to categorize the terms into a plurality of relations, wherein a first of the two context vectors of a given term predicts terms that will appear to the left of the given term based on a calculated score for terms to the left of the given term, wherein a second of the two context vectors predicts terms that will appear to the right of the given term based on a calculated score for terms to the right of the given term; and

an ontology database upon which the processor stores the automatedly generated ontology.

19. The system of claim 18, wherein the communication data comprises transcriptions of interactions, agent scripts, service manuals, and product manuals.

20. The system of claim 18, further comprising:

a script database communicatively connected to the processor; and

wherein execution of the computer readable code by the processor further causes the processor to;

surface a plurality of scripts from the communication data;

store the plurality of scripts at the script database; and

apply the plurality of scripts from the script database to the corpus of communication data to identify scripts within the corpus of communication data.