SEMANTICS GRAPHS FOR ENTERPRISE COMMUNICATION NETWORKS

• US 20150074121A1
• Filed: 11/14/2014
• Published: 03/12/2015
• Est. Priority Date: 01/31/2013
• Status: Abandoned Application

• Current Assignee: Micro Focus LLC (Open Text Corporation)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Ozonat, Mehmet Kivanc, Bartolini, Claudio
• Application Number:
  US14/542,098
• Publication Number:
  US 20150074121A1
• Time in Patent Office: Days
• US Class Current: 707/748
• CPC Class Codes: G06F 16/36 Creation of semantic tools, e.g. ontology or thesauri
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/9027 Trees
• CPC Class Codes: G06F 16/95 Retrieval from the web
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

Building a semantics graph for an enterprise communication network can include calculating a distance metric between a first signifier and a second signifier associated with an enterprise communication network, wherein the distance metric includes a plurality of relationships defined based on a frequency of co-occurrences of the first signifier and the second signifier, and building a semantics graph for the enterprise communication network using the calculated distance metric.

Claims

1. A method for building a semantics graph for an enterprise communication network, comprising:

calculating a distance metric between a first signifier and a second signifier associated with the enterprise communication network, wherein the distance metric includes a plurality of relationships defined based on a frequency of co-occurrences of the first signifier and the second signifier using a computing device; and

building a semantics graph for the enterprise communication network using the calculated distance metric.

2. The method of claim 1, wherein calculating the distance metric includes calculating a weighted Euclidean distance including constructing an n-dimensional feature vector.

3. The method of claim 1, wherein building the semantics graph includes adding the first signifier and the second signifier as nodes on the semantics graph with an edge connecting the first signifier and the second signifier.

4. The method of claim 3, including weighting the edge with a score defined by the calculated distance metric.

5. The method of claim 1, including searching an enterprise network for a plurality of signifiers, including words, phrases, and/or acronyms using a search tool.

6. The method of claim 1, including calculating the distance metric based on a plurality of criteria.

7. A non-transitory computer-readable medium storing a set of instructions executable by a processing resource, wherein the set of instructions can be executed by the processing resource to:

calculate a distance metric between a first signifier and a second signifier associated with an enterprise communication network, wherein the distance metric includes a plurality of relationships defined based on;

a frequency of co-related services associated with the first signifier and the second signifier;

a frequency of co-related phrases associated with the first signifier and the second signifier; and

an average location of the first signifier and the second signifier on the enterprise network; and

build a semantics graph for the enterprise communication network using the calculated distance metric.

8. The medium of claim 7, wherein the instructions executable by the processing resource include instructions executable to extract a plurality of signifiers from the enterprise network using an extraction tool, wherein the plurality of signifiers include the first signifier and the second signifier.

9. The medium of claim 8, wherein the instructions executable by the processing resource include instructions executable to calculate a distance metric for related signifiers among the plurality of signifiers, including the distance metric for the first signifier and the second signifier.

10. The medium of claim 7, wherein the instructions executable by the processing resource include instructions executable to:

divide the frequency of co-related services associated with the first signifier and the second signifier by a frequency of services related independently to the first signifier and services related independently to the second signifier to determine a ratio of co-related services of the first signifier and the second signifier; and

divide the frequency of co-related phrases associated with the first signifier and the second signifier by a frequency of phrases related independently to the first signifier and phrases related independently to the second signifier to determine a ratio of co-related phrases of the first signifier and the second signifier.

11. The medium of claim 10, wherein the instructions executable by the processing resource to calculate the distance metric between a first signifier and a second signifier include instructions executable to define the distance metric as a sum of the ratio of co-related services, the ratio of co-related phrases, and the average location.

12. The medium of claim 7, wherein the instructions executable by the processing resource to build the semantics graph include instructions executable to add a first node, a second node, and an edge to the semantics graph, wherein the first node represents the first signifier, the second node represents the second signifier, and the edge connects the first node and the second node.

13. The medium of claim 12, wherein the edge is weighted by the distance metric.

14. A system for building a semantics graph for an enterprise communication network comprising:

a processing resource; and

a memory resource communicatively coupled to the processing resource containing instructions executable by the processing resource to;

extract a plurality of signifiers from an enterprise network using an extraction tool;

define a distance metric between pairs of related signifiers among the plurality of signifiers, wherein defining a distance metric between a first signifier and a second signifier includes;

calculate a frequency of co-related services associated with the first signifier and the second signifier;

calculate a frequency of co-related phrases associated with the first signifier and the second signifier;

average a location of the first signifier and the second signifier on the enterprise network; and

define the distance metric as a sum of the frequency of co-related services, the frequency of co-related phrases, and the average location; and

build a semantics graph for the enterprise communication network using the defined distance metrics between pairs of related signifiers, including the defined distance metric of the first signifier and the second signifier.

15. The system of claim 14, wherein related signifiers include signifiers that have a co-occurrence on the enterprise network.