Method and Apparatus of Knowledge Base Building

• US 20110060734A1
• Filed: 04/27/2010
• Published: 03/10/2011
• Est. Priority Date: 04/29/2009
• Status: Abandoned Application

• Current Assignee: Alibaba Group Holding Ltd.
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: Chen, Wei, Qin, Jisheng, Zhang, Qin, Hou, Lei
• Application Number:
  US12/863,683
• Publication Number:
  US 20110060734A1
• Time in Patent Office: Days
• US Class Current: 707/723
• CPC Class Codes: G06F 16/3338 Query expansion
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

The present disclosure provides a method and apparatus of knowledge base building to automatically construct a knowledge base. Furthermore, the disclosed techniques can be used to improve the accuracy of that knowledge base. In one aspect, a method acquires a sentence from a webpage using a basic data processing layer of a computing apparatus. The acquired sentence is parsed into words using a data mining layer of the computing apparatus. One or more representative words in a first category of a knowledge base are matched with the words parsed from the acquired sentence. When there is a match between one of the representative words and one of the words parsed from the acquired sentence, a string of words adjacent the matched word in the acquired sentence is added to the first category as a first entry. When matching the words parsed from the acquired sentence with a second entry of a second category of the knowledge base, it is determined whether or not an established correlation exists between the first category and the second category. When it is determined that an established correlation exists between the first category and the second category, a correlation between the first entry of the first category and the second entry of the second category is established. The present disclosure also discloses methods for searching information and computing apparatuses that implement the methods.

Claims

1. A method of knowledge base building using a computing apparatus, the method comprising:

acquiring a sentence from a webpage using a basic data processing layer of the computing apparatus;

parsing the acquired sentence into words using a data mining layer of the computing apparatus;

matching one or more representative words in a first category of a knowledge base with the words parsed from the acquired sentence;

when there is a match between one of the representative words and one of the words parsed from the acquired sentence, adding a string of words adjacent the matched word in the acquired sentence to the first category as a first entry;

when matching the words parsed from the acquired sentence with a second entry of a second category of the knowledge base, determining whether or not an established correlation exists between the first category and the second category; and

when it is determined that an established correlation exists between the first category and the second category, establishing a correlation between the first entry of the first category and the second entry of the second category.

2. The method as recited in claim 1, wherein acquiring a sentence from a webpage comprises dividing the acquired sentence into multiple shorter sentences based on punctuation marks in the acquired sentence, and wherein parsing the acquired sentence comprises parsing the acquired sentece or parsing the multiple shorter sentences.

3. The method as recited in claim 1, further comprising:

the basic data processing layer counting a number of appearances of individual sentences; and

the data mining layer establishing a weighted value of the first entry of the first category based on a number of appearances of any sentence having the first entry and one or more of the representative words adjacent the first entry.

4. The method as recited in claim 1, wherein the data mining layer employs a parsing system that includes the one or more representative words to divide the acquired sentence.

5. The method as recited in claim 1, wherein the knowledge base includes a common word system and a substantive word system, wherein the common word system and the substantive word system respectively include different categories, wherein the representative words include category-corresponding index words of the substantive word system and category-corresponding seed words of the common word system, and wherein when the string of words adjacent the matched word in the acquired sentence is added to the first category as the first entry, the string of words is added to the common word system or the substantive word system that includes the first category.

6. The method as recited in claim 5, wherein when the first category is one of the categories included in the common word system, the method further comprises:

setting the first entry as the seed word corresponding to the first category.

7. The method as recited in claim 1, wherein establishing a correlation between the first entry of the first category and the second entry of the second category comprises:

obtaining a frequency of appearance of sentences of the first entry and the second entry; and

establishing the correlation between the first and second entry when the frequency of appearance of sentences of the first entry and the second entry exceeds a predetermined threshold value.

8. The method as recited in claim 1, further comprising:

the data mining layer generating a respective result file according to each category and respective entries under each category; and

an integration layer of the computing apparatus integrating multiple result files into a single result file.

9. The method as recited in claim 8, further comprising:

counting a number of appearances of individual sentences;

establishing a weighted value of the first entry of the first category based on a number of appearances of any sentence having one or more of the representative words and the first entry;

comparing weighted values of individual entries under different categories; and

filtering entry-corresponding categories.

10. The method as recited in claim 1, further comprising:

acquiring a table from the webpage; and

attributing a word that appears in the table in a pair with the first entry multiple times as a property of the first entry.

11. The method as recited in claim 1, wherein acquiring a sentence from a webpage comprises acquiring from the webpage a sentence that contains special symbols.

12. A method of information searching, the method comprising:

Identifying, in a knowledge base, a label based on one or more keywords in a webpage and entries related to the one or more keywords, the label matching a search term inputted by a user;

locating the webpage that corresponds to the label; and

providing to the user the webpage or a link to the webpage.

13. The method as recited in claim 12, wherein the knowledge base is constructed by:

acquiring a sentence from one of a plurality of webpages using a basic data processing layer of a computing apparatus;

parsing the acquired sentence into words using a data mining layer of the computing apparatus;

matching one or more representative words in a first category of the knowledge base with the words parsed from the acquired sentence;

when there is a match between one of the representative words and one of the words parsed from the acquired sentence, adding a string of words adjacent the matched word in the acquired sentence to the first category as a first entry;

when matching the words parsed from the acquired sentence with a second entry of a second category of the knowledge base, determining whether or not an established correlation exists between the first category and the second category; and

when it is determined that an established correlation exists between the first category and the second category, establishing a correlation between the first entry of the first category and the second entry of the second category.

14. A method of information searching, the method comprising:

parsing a search term inputted by a user using entries of a knowledge base;

matching words parsed from the search term with the entries of the knowledge base;

identifying those entries of the knowledge base that are related to an entry having a match with a word parsed from the search term;

updating the search term with those entries of the knowledge base that are related to the entry having a match with a word parsed from the search term; and

conducting a search based on the updated search term.

15. The method as recited in claim 14, wherein the knowledge base is constructed by:

acquiring a sentence from a webpage using a basic data processing layer of a computing apparatus;

parsing the acquired sentence into words using a data mining layer of the computing apparatus;

matching one or more representative words in a first category of the knowledge base with the words parsed from the acquired sentence;

when there is a match between one of the representative words and one of the words parsed from the acquired sentence, adding a string of words adjacent the matched word in the acquired sentence to the first category as a first entry;

when matching the words parsed from the acquired sentence with a second entry of a second category of the knowledge base, determining whether or not an established correlation exists between the first category and the second category; and

when it is determined that an established correlation exists between the first category and the second category, establishing a correlation between the first entry of the first category and the second entry of the second category.

16. A computing apparatus that constructs a knowledge base, the computing apparatus comprising:

a basic data processing module that acquires one or more sentences from a webpage; and

a data mining module that parses the one or more sentences acquired from the webpage, the data mining module further;

matching one or more representative words in a first category of the knowledge base with the words parsed from the acquired sentence;

when there is a match between one of the representative words and one of the words parsed from the acquired sentence, adding a string of words adjacent the matched word in the acquired sentence to the first category as a first entry;

when matching the words parsed from the acquired sentence with a second entry of a second category of the knowledge base, determining whether or not an established correlation exists between the first category and the second category; and

when it is determined that an established correlation exists between the first category and the second category, establishing a correlation between the first entry of the first category and the second entry of the second category.

17. A search engine, comprising:

a first query module that identifies a label corresponding to a search term inputted by a user;

a second query module that identifies a webpage corresponding to the label;

an interface module that provides to the user the webpage or a link to the webpage; and

a label generation module that generates labels corresponding to the webpage based on one or more keywords of the webpage and entries of a knowledge base that are related to the one or more keywords.

18. A search engine, comprising:

a parsing module that parses a user-inputted search term into words based on entries of a knowledge base;

a matching module that matches words parsed from the search term with the entries of the knowledge base;

a query module that identifies those entries of the knowledge base that are related to an entry having a match with a word parsed from the search term;

an update module that updates the search term with those entries of the knowledge base that are related to the entry having a match with a word parsed from the search term; and

a search module that conducts a search based on the updated search term.