Knowledge graph generating apparatus, method, and non-transitory computer readable storage medium thereof

Patent metadata

Field	Value
Publication number	US-11250035-B2
Application number	US-201816206844-A
Country	US
Kind code	B2
Filing date	Nov 30, 2018
Priority date	Oct 25, 2018
Publication date	Feb 15, 2022
Grant date	Feb 15, 2022

Abstract

A knowledge graph generating apparatus, method and non-transitory computer readable storage medium thereof are provided. The apparatus marks an entity-relationship of the template of goods information in the template of webpage according to the operating signal and generates an extraction rule set, wherein the template of webpage is one of multiple goods webpages and has a template format. The apparatus extracts a plurality of first product information of the first goods webpages according to the extraction rule set, wherein the first goods webpages have the template format and are selected from the goods webpages. The apparatus generates a classified goods information result through a product information classification model, wherein the product information classification model is generated based on the first product information and the entity-relationship of the template of goods information. The apparatus converts the classified goods information result into several semantic triples to generate a knowledge graph.

First claim

What is claimed is:

1. A knowledge graph generating apparatus, comprising:
a network interface, being configured to connect to a plurality of goods webpages;
an operating interface, being configured to generate an operating signal; and
a processor, being electrically connected to the network interface and the operating interface and configured to perform the following operations:
(a) annotating an entity-relationship of a piece of template of goods information in a template of webpage according to the operating signal and generating a first extraction rule set, wherein the template of webpage is one of the goods webpages and the template of webpage has a template format;
(b) connecting to a plurality of first goods webpages through the network interface, and extracting a plurality of pieces of first product information of the first goods webpages according to the first extraction rule set, wherein the first goods webpages have the template format and the first goods webpages are selected from the goods webpages;
(c) generating a first classified goods information result through a product information classification model, wherein the product information classification model is generated based on the first product information and the entity-relationship of the template of goods information;
wherein the product information classification model comprises the following operations:
making a word segmentation according to the first product information to generate a word segmentation result; and
extracting a relation result according to the entity-relationship of the template of goods information and the word segmentation result to generate the first classified goods information result, wherein the first classified goods information result indicates the entity-relationship of the first product information; and

(d) converting the first classified goods information result into a plurality of semantic triples to generate a knowledge graph.

2. The knowledge graph generating apparatus of claim 1 , wherein the template format is a webpage table or a Cascading Style Sheet (CSS).

3. The knowledge graph generating apparatus of claim 1 , wherein the first extraction rule set is related to at least one category of the template format.

4. The knowledge graph generating apparatus of claim 1 , wherein the entity-relationship at least contains an entity, a relation and at least one attribute corresponding to the relation, of the template of goods information.

5. The knowledge graph generating apparatus of claim 1 , wherein the processor further executes the following operations:
extracting a piece of second product information of a second goods webpage according to the first extraction rule set, wherein the second goods webpage has the template format;
generating a second classified goods information result through the product information classification model, wherein the second classified goods information result indicates the entity-relationship of the second product information; and
updating the knowledge graph according to the second classified goods information result.

6. The knowledge graph generating apparatus of claim 1 , wherein the processor further executes the following operations:
annotating an entity-relationship of a second product in a second template of webpage according to a second operating signal and generating a second extraction rule set, wherein the second template of webpage is one of the goods webpages and the second template of webpage has a second template format; and
generating an extraction rule model according to the first extraction rule set and the second extraction rule set.

7. The knowledge graph generating apparatus of claim 1 , wherein the processor further executes the following operations:
re-extracting the first goods webpages according to the first extraction rule set to obtain updated first product information when there is an update version of the first goods webpages;
generating an updated first classified goods information result through the product information classification model; and
updating the knowledge graph according to the updated first classified goods information result.

8. A knowledge graph generating method for use in a knowledge graph generating apparatus, the knowledge graph generating apparatus being configured to generate an operating signal, the knowledge graph generating method being performed by the knowledge graph generating apparatus and comprising:
(a) annotating an entity-relationship of a piece of template of goods information in a template of webpage according to the operating signal and generating a first extraction rule set, wherein the template of webpage is one of a plurality of goods webpages and the template of webpage has a template format;
(b) extracting a plurality of pieces of first product information of a plurality of first goods webpages according to the first extraction rule set, wherein the first goods webpages have the template format and the first goods webpages are selected from the goods webpages;
(c) generating a first classified goods information result through a product information classification model, wherein the product information classification model is generated based on the first product information and the entity-relationship of the template of goods information;
wherein the step of generating the first classified goods information result through the product information classification model comprises:
making a word segmentation according to the first product information to generate a word segmentation result; and
extracting a relation result according to the entity-relationship of the template of goods information and the word segmentation result to generate the first classified goods information result, wherein the first classified goods information result indicates the entity-relationship of the first product information; and

(d) converting the first classified goods information result into a plurality of semantic triples to generate a knowledge graph.

9. The knowledge graph generating method of claim 8 , wherein the template format is a webpage table or a Cascading Style Sheet (CSS).

10. The knowledge graph generating method of claim 8 , wherein the first extraction rule set is related to at least one category of the template format.

11. The knowledge graph generating method of claim 8 , wherein the entity-relationship at least contains an entity, a relation and at least one attribute corresponding to the relation, of the template of goods information.

12. The knowledge graph generating method of claim 8 , further comprising:
extracting a piece of second product information of a second goods webpage according to the first extraction rule set, wherein the second goods webpage has the template format;
generating a second classified goods information result through the product information classification model, wherein the second classified goods information result indicates the entity-relationship of the second product information; and
updating the knowledge graph according to the second classified goods information result.

13. The knowledge graph generating method of claim 8 , further comprising:
annotating an entity-relationship of a second product in a second template of webpage according to a second operating signal and generating a second extraction rule set, wherein the second template of webpage is one of the plurality of goods webpages and the second template of webpage has a second template format; and
generating an extraction rule model according to the first extraction rule set and the second extraction rule set.

1

Assignees

• Inst Information Ind

Inventors

• Kuo Hsin-Yi
• Yang You-Cyuan
• Huang Wen-Fa
• Wang Wen-Nan
• Chen Ping-I

Classifications

• G06F16/906

  Clustering; Classification · CPC title

• G06F16/9024 Primary

  Graphs; Linked lists (G06F16/9027 takes precedence) · CPC title

• G06F40/30

  Semantic analysis · CPC title

• G06F16/951

  Indexing; Web crawling techniques · CPC title

• G06F40/169

  Annotation, e.g. comment data or footnotes · CPC title