FRAMEWORK FOR DOCUMENT KNOWLEDGE EXTRACTION

• US 20130246435A1
• Filed: 03/14/2012
• Published: 09/19/2013
• Est. Priority Date: 03/14/2012
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Yan, Jun, Ji, Lei, Li, Yi, Liu, Ning, Chen, Zheng, Wild, Edward W.
• Application Number:
  US13/419,690
• Publication Number:
  US 20130246435A1
• Time in Patent Office: Days
• US Class Current: 707/741
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification

Abstract

A knowledge extraction framework may iteratively enrich an ontology that is used to classify structured knowledge obtained from web pages based on structured knowledge previously acquired from other web pages. The framework may enable a user to define the ontology for extracting structured knowledge from a plurality of web pages. The framework applies the ontology using a supervised extraction algorithm to extract seed information from a set of web pages. The framework further applies an unsupervised extraction algorithm to extract the structured knowledge from an additional set of web pages. The framework subsequently maps the structured knowledge to the ontology based on the seed information to enrich the ontology.

Claims

1. A computer-implemented method, comprising:

defining an ontology for extracting structured knowledge from a plurality of web pages;

applying the ontology using a supervised extraction algorithm to obtain seed information from a set of web pages;

applying an unsupervised extraction algorithm to extract the structured knowledge from an additional set of web pages; and

mapping the structured knowledge to the ontology based at least on the seed information to produce an enriched ontology.

2. The computer-implemented method of claim 1, further comprising annotating the additional set of web pages with the structured knowledge using the enriched ontology.

3. The computer-implemented method of claim 1, wherein the mapping further comprises:

determining a set of one or more overlapping seed entities included in the seed information that overlaps with one or more extracted entities included in the structured knowledge;

retrieving at least one attribute of each overlapping seed entity and each of extracted entities included in the structured knowledge; and

mapping attributes of the extracted entities to the ontology by classifying attribute values of the extracted entities to the ontology using an attribute name and an attribute value of the each overlapping seed entity.

4. The computer-implemented method of claim 3, further comprising receiving a manually defined rule, and wherein the mapping includes classifying the attribute values to the ontology based at least on the manually defined rule.

5. The computer-implemented method of claim 4, wherein the manually defined rule is a string matching rule, a regular expression, or an attribute type taxonomy for classifying an attribute.

6. The computer-implemented method of claim 5, wherein the manually defined rule is the attribute type taxonomy, and the attribute type taxonomy includes definitions for numerical attributes, enumerable attributes, and free text attributes.

7. The computer-implemented method of claim 3, further comprising automatically generating a pattern rule via an analysis of at least the attributes of the extracted entities, and wherein the mapping includes classifying the attribute values to the ontology based at least on the pattern rule.

8. The computer-implemented method of claim 3, further comprising:

determining a confidence score for an attribute that is mapped to the ontology; and

discarding mapping of the attribute to the ontology when the confidence score fails to exceed a predetermined threshold.

9. The computer-implemented method of claim 8, wherein the confidence score for the attribute is calculated based at least on extracted entities corresponding to an attribute column that lists values of the attribute.

10. The computer-implemented method of claim 3, further comprising:

building an index that associates a plurality of overlapping seed entities with corresponding attributes; and

enriching the seed information by adding an association between an attribute that is mapped to the ontology and a corresponding entity to the index.

11. The computer-implemented method of claim 3, wherein the determining including terminating sampling of the extracted entities included in the structured knowledge when a predetermined number of the one or more overlapping seed entities is discovered.

12. A computer-readable medium storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising:

defining an ontology for extracting structured knowledge from a plurality of web pages;

applying the ontology using a supervised extraction algorithm to obtain seed entities from a set of web pages;

applying an unsupervised extraction algorithm to obtain extracted entities from an additional set of web pages;

determining a set of overlapping seed entities included in the seed entities that overlaps with the extracted entities;

retrieving at least one attribute of each overlapping seed entity and each of the extracted entities, each attribute including an attribute name and an attribute value; and

mapping attributes of the extracted entities to the ontology to produce an enriched ontology.

13. The computer-readable medium of claim 12, further comprising validating the mapping based at least on at least one of a precision value or a recall value that is obtained from a comparison of the seed entities to the extracted entities or a manual labeling of the extracted entities.

14. The computer-readable medium of claim 12, further comprising annotating the additional set of web pages with ontology node names from the enriched ontology.

15. The computer-readable medium of claim 12, wherein the mapping includes classifying attribute values of the extracted entities to the ontology using the attribute name and attribute value of the each overlapping seed entity.

16. The computer-readable medium of claim 14, further comprising:

receiving a manually defined rule that is a matching rule, a regular expression, or an attribute type taxonomy for classifying an attribute; and

generating a pattern rule via an analysis of at least the attributes of the extracted entities,

and wherein the mapping includes classifying the attributes values to the ontology based at least on at least one of the manually defined rule or the pattern rule.

17. The computer-readable medium of claim 12, further comprising:

determining a confidence score for an attribute that is mapped to the ontology, the confidence score being calculated using extracted entities corresponding to an attribute column that lists values of the attribute; and

discarding mapping of the attribute to the ontology when the confidence score fails to exceed a predetermined threshold.

18. A computing device, comprising:

one or more processors; and

a memory that includes a plurality of computer-executable components of a knowledge extraction framework, the plurality of computer-executable components comprising;

a supervised learning module that applies a predefined ontology using a supervised extraction algorithm to extract seed information from a set of web pages;

an unsupervised learning module that applies an unsupervised extraction algorithm to extract structured knowledge from an additional set of web pages;

a mapping module that maps the structured knowledge to the ontology based at least on the seed information to enrich the ontology; and

an annotation module that annotates the additional set of web pages based at least on the structured knowledge.

19. The computing device of claim 18, wherein the mapping module maps the structured knowledge to the ontology by:

determining a set of one or more overlapping seed entities included in the seed information that overlaps with one or more extracted entities included in the structured knowledge;

retrieving at least one attribute of each overlapping seed entity and each of extracted entities included in the structured knowledge, each attribute including an attribute name and an attribute value; and

mapping attributes of the extracted entities to the ontology by classifying attribute values of the extracted entities to the ontology using the attribute name and attribute value of the each overlapping seed entity.

20. The computing device of claim 19, wherein the mapping module is to further:

receive a manually defined rule that is a string matching rule, a regular expression, or an attribute type taxonomy for classifying an attribute; and

generate a pattern rule via an analysis of at least the attributes of the extracted entities,

and wherein the mapping includes classifying the attributes values to the ontology based at least on at least one of the manually defined rule or the pattern rule.