WO2000026839A1 - Advanced model for automatic extraction of skill and knowledge information from an electronic document - Google Patents
Advanced model for automatic extraction of skill and knowledge information from an electronic document Download PDFInfo
- Publication number
- WO2000026839A1 WO2000026839A1 PCT/US1999/026083 US9926083W WO0026839A1 WO 2000026839 A1 WO2000026839 A1 WO 2000026839A1 US 9926083 W US9926083 W US 9926083W WO 0026839 A1 WO0026839 A1 WO 0026839A1
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- WO
- WIPO (PCT)
- Prior art keywords
- skill
- electronic document
- information
- knowledge
- knowledge information
- Prior art date
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q99/00—Subject matter not provided for in other groups of this subclass
Definitions
- This invention relates to the field of computer analysis of electronic documents.
- Information to be sorted and stored in a computer database may reside in
- employee for a specific job may have a specific job description.
- the project manager must sift through several documents which contain the
- project manager may have to read the documents several times and may have to review
- a computerized system which can analyze and extract pertinent information
- these documents may be prepared in a variety of different file formats, such as Microsoft Word 97, Rich Text Format, PDF, WordPerfect, ASCII files, and
- HTML HyperText Markup Language
- the present invention is an apparatus, method, and computer-readable medium
- semantic network engine (216) for determining a skill level for the skill information
- knowledge section processor (702) uses a non-monotonic reasoning principle to
- the content analysis and semantic network engine (216) further comprises a
- document (104) comprises the steps of: identifying skill and knowledge information in
- the method further comprises the step of storing the skill information and qualitative
- Figure 1 is a block diagram of a preferred embodiment of a system 100 in
- FIG. 2 is a block diagram of a preferred embodiment of an extraction server
- Figure 3 is a flow chart of a preferred embodiment of the steps performed by
- Figure 4 is a block diagram of a preferred embodiment of a thesaurus. 221
- Figure 5 is a block diagram of a preferred embodiment of a semantic network
- Figure 6 is a flow chart of a preferred embodiment of the steps performed by
- Figure 7 is a block diagram of a preferred embodiment of a system 700 in
- Figure 8 is a flow chart of a preferred embodiment of the steps performed by
- Figure 9 is a screen shot of a user interface of a preferred embodiment of a
- target database 110 display for skill information.
- a host computer 102 using the method
- unstructured text As used herein "unstructured text"
- Examples of documents containing unstructured text include, but are not limited to, a
- the host computer 102 is
- a conventional computer having a keyboard and mouse for input (not shown), and a
- the electronic document 104 may be prepared in any electronic file
- the electronic document 104 is processed by host computer 102 using the
- host computer 102 uses extraction server 108 to extract data from external source 102 .
- extraction server 108 uses extraction server 108 to extract data from external source 102 .
- word groups are used to mean any text that may be derived from document 104
- the extraction server 108 identifies the document type of the
- the structure and operation of the extraction server 108 is
- the target database 110 comprises predefined tables with predefined columns
- a predefined table and predefined columns correspond to a
- document 104 is a resume
- predefined For example, if document 104 is a resume, then a predefined
- document 104 is a patent document, then a predefined table for a document type called
- pattern document may have predefined columns such as “inventors”, “company”,
- present invention is not limited to a particular document type or a predefined table, but
- the process of extraction performed by the extraction server 108 preferably
- extraction server 108 assumes a reasonable default value. That default value is
- the present invention advantageously allows a user to extract skill
- the present invention analyzes an electronic copy of a text document
- target database comprising predefined tables and columns associated with a particular
- the target database can then be retrieved and manipulated by other computer program
- the electronic document 104 may be any electronic
- the electronic document 104 may be an electronic form of a hard copy of a
- OCR OCR
- Microsoft Word file 204 an ASCII text file 206 or
- target database 110 information in target database 110 are also preferably stored in memory 106.
- the extraction server 108 comprises a document preprocessor
- heuristics engine 212 coupled to the document pre-processor 210, a morpho logical
- analysis engine 214 coupled to the heuristics engine 212, a content analysis and
- semantic network engine 216 coupled to the document preprocessor 210, and a database interface 222 coupled to the content analysis and semantic network engine
- 216 preferably comprises section processors 218 and a semantic network 220.
- the document pre-processor 210 retrieves the electronic document 104 from
- memory 106 and performs the initial analysis of the electronic document 104.
- the document pre-processor 210 performs the
- the document pre-processor 210 identifies the file format of the electronic
- the document pre-processor 210 filters out (304) any unnecessary and
- processor 210 then stores (306) formatting information for the document 104 such as,
- the document pre-processor 210 then performs paragraph identification
- Paragraph characteristics include, but are not limited to, the number of
- the document pre-processor 210 groups the paragraphs into sections.
- the heuristic engine 212 applies a set of heuristics, that is a set of rules, to the
- the set of heuristics which are applied to the electronic document 104 are associated
- the morphological analysis engine 214 is used for target language analysis and
- LinguistiX 2.0 application programming interface API
- the LmguistiX 2.0 API is a language neutral
- LinguistiX API can analyze documents in
- LinguistiX API are external to and separate from the document pre-processor
- the Heuristics Engine 212 uses the following features provided by
- LinguistiX API tokenization, lexical analysis, tagging, and noun-phrase extraction.
- LinguistiX tokenization includes the ability to recognize multi-word
- the lexical analysis feature identifies the grammatical
- the tagging feature identifies the
- LinguistiX phrase extraction technology enables
- semantic network 220 to identify the multi-word noun phrases.
- the extraction server 108 may discover that a
- the database interface 222 is a set of APIs that provide a mechanism for
- the extraction server 108 can
- database interface 222 provides the following mechanisms: a method
- the content analyzer and semantic network engine 216 analyzes the content of
- the electronic document 104 extracts words and word groups from the document 104, extracts words and word groups from the document 104, and
- section processors 218 which extract information from a particular section
- the semantic network 220 uses a thesaurus
- the thesaurus 221 is shown.
- the thesaurus 221 is a vocabulary database for the extraction
- the server 108 and is organized by skills.
- the thesaurus 221 groups all related terms 402
- a "concept” or “skill” 404 comprises a
- skills 404 connect all the different names for the same skill 404 that are
- each skill 404 has a unique skill identifier (ConceptlD).
- Concept ID the concept ID
- terml 402 A may consist of 'MS VC++'
- term2 402B may consist of
- 'Microsoft Visual C++' and term3 402C may consist of 'MS Visual C++'. All these
- document 104 uses any of the words or word groups 'MS VC++', 'Microsoft Visual
- the thesaurus 221 allows the extraction server 108 to
- term4, term5 and term ⁇ are respectively 'JDK 1.1', 'Symantec cafe',
- the electronic document 104 uses any of the words or word groups 'JDK 1.1',
- the thesaurus 221 allows the extraction server 108 to
- the thesaurus 221 may also comprise other information such as the attributes
- Attributes provide additional information that
- thesaurus 221 also comprises relationships among skills 404. Preferably, these
- subsumption refers to relationships that include related skills, co-occurring skills
- thesaurus 221 are not limited to the examples given herein but may contain any
- thesaurus facilitates the access to concept relationships and to
- FIG. 5 a block diagram of a preferred embodiment of a
- semantic network 220 is shown.
- the semantic network 220 provides a way of
- the semantic network 220 is of higher level knowledge-concepts and categories.
- the semantic network 220 is of higher level knowledge-concepts and categories.
- the semantic network 220 is configured to:
- a category 504 is the highest level in the semantic network 222. Broad
- categories 504 may be created according to a specific industry which fully subsume
- the semantic network 220 categorizes
- Knowledge-concepts 502 comprises
- Each knowledge-concept 502 is
- the semantic network 220 categorizes all terms 402 into skills 404. As
- the entire semantic network 220 separate from the thesaurus 221, comprises
- a single knowledge-concept 502 can comprise several skills 404 and a
- knowledge-concepts 502 may comprises a category 504 and several categories may
- the skill 404 'Visual C++' may also belong to the knowledge-concept 502
- Programming Environment may also be linked to other skills 404 such as 'Visual
- the semantic network 220 uses subsumption as the basis for the hierarchical
- An object may also be subsumed by more than one higher level object.
- the skill 404 'JDBC may be subsumed by at least two knowledge-concepts
- sections are then analyzed (604) and information is extracted from the sections.
- the extracted information is stored (606) in a predefined structure in the target database
- the present invention advantageously extracts
- the present invention provides a powerful semantic network and
- the semantic network can stored information relating to any field, industry or
- the section processors 218 extract information from sections of interest in an
- network engine 216 comprises a section processor 218 for extracting words or word
- Section processors 218 are configured to operate on a specific document type
- type may comprise a cover letter section processor for extracting information from a
- a contact information section processor for extracting contact information
- a skills and experience section processor for extracting the skills
- an education section processor for extracting educational
- section processor for extracting any articles or documents published by a candidate.
- Each section processor 218 analyzes a particular section in the electronic document
- section processor 218 applies a set of heuristics to the particular section of interest in
- present invention comprising a skills and knowledge information extractor 702.
- the skills and knowledge information extractor 702 allows a
- a "career profile” refers to any qualitative and quantitative
- such information includes, but is not limited to, how long a candidate worked
- “skill” or “skill information” refers to the skills 404 in the thesaurus 221 and
- semantic network 220 which relate to those terms, and "knowledge” or “knowledge
- a candidate may have used the terms "Microsoft Visual C++" or "MS
- the present invention is able to determine that the candidate has "skill" in C++
- the skill and knowledge information extractor 702 uses a non-monotonic
- non- monotonic reasoning refers to the use of default assumptions which are made about the
- extractor 702 is best illustrated using an example.
- the present invention finds a skill, X, in a candidate's
- X is refined. Additional knowledge that may be used to refine the skill level includes,
- X is found in the Objective Section of a resume R, a positive numerical value, or
- this weightage value is computed for all
- associated skills are the skills related
- W(Y) may also be added to the skill level.
- W(LU) which is subtracted from the skill level.
- SkillLevel(X') SkillLevel(X) + W(O) + ⁇ W(P j ) + W(K) + W(Y) - W(LU)
- the weightage functions are computed using the total number of skill levels
- extractor 702 assumes that a person has an average skill level for a particular skill such as C++. If the candidate's resume states that the candidate took a course in C++, that
- knowledge information extractor 702 then maps the skill value to a scale for
- the present invention allows a user to
- scale may map the final skill value to a scale comprising numbers such as 1 to 5 or 1 to
- a scale may map the final skill value to a scale comprising numbers and adjectives
- the qualitative scale may be determined by the
- the categories, knowledge, skills and terms are preferably set up in a relational
- resume is evaluated (802) for a particular skill.
- Window 902 displays the particular skills analyzed from a candidate's
- the highlighted portion of window 902 indicates that the candidate has some
- present invention advantageously allows a user to extract, determine, and display from
- the present invention is designed as a set of Object Oriented Libraries and
- the present invention may be implemented to run
- Database tables may be used to define how information is represented in a relational or
- any relational table is preferably represented as an object class.
- object class any relational table
- Table 1 holds the documents that are to be extracted. It holds the following information:
- Table 2 holds information about the scheduled extraction tasks.
- Table 3 holds the personal information like name of the person, contact address, current employer, resume summary etc.
- the XtractionXpert automatically extracts the following information from the resume:
- Table 16 provides information regarding the relationships between categories and knowledge information.
- Table 17 provides knowledge information for semantic network 220.
- Table 18 provides information relating to skills.
- Table 19 provides information on relationships between skills and knowledge.
- Table 20 provides information on terms.
- Table 21 stores information about different languages to which the terms belong.
Abstract
Description
Claims
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
GB0113250A GB2359168A (en) | 1998-11-04 | 2001-05-31 | Advanced model for automatic extraction of skill and knowledge information from an electronic document |
Applications Claiming Priority (6)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US10706398P | 1998-11-04 | 1998-11-04 | |
US60/107,063 | 1998-11-04 | ||
PCT/US1998/027664 WO1999034307A1 (en) | 1997-12-29 | 1998-12-28 | Extraction server for unstructured documents |
USPCT/US98/27664 | 1998-12-28 | ||
US38021999A | 1999-08-27 | 1999-08-27 | |
US09/380,219 | 1999-08-27 |
Publications (3)
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WO2000026839A1 true WO2000026839A1 (en) | 2000-05-11 |
WO2000026839A8 WO2000026839A8 (en) | 2000-10-12 |
WO2000026839A9 WO2000026839A9 (en) | 2001-08-02 |
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PCT/US1999/026083 WO2000026839A1 (en) | 1998-11-04 | 1999-11-03 | Advanced model for automatic extraction of skill and knowledge information from an electronic document |
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WO (1) | WO2000026839A1 (en) |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2005024692A1 (en) * | 2003-09-03 | 2005-03-17 | Yahoo! Inc. | Automatically identifying required job criteria |
EP1706845A2 (en) * | 2003-12-02 | 2006-10-04 | Unisys Corporation | Improved cargo handling security handling system and method |
EP1920364A2 (en) * | 2005-07-27 | 2008-05-14 | John Harney | System and method for providing profile matching with an unstructured document |
US8021163B2 (en) * | 2006-10-31 | 2011-09-20 | Hewlett-Packard Development Company, L.P. | Skill-set identification |
US9779390B1 (en) | 2008-04-21 | 2017-10-03 | Monster Worldwide, Inc. | Apparatuses, methods and systems for advancement path benchmarking |
US9959525B2 (en) | 2005-05-23 | 2018-05-01 | Monster Worldwide, Inc. | Intelligent job matching system and method |
US9996523B1 (en) | 2016-12-28 | 2018-06-12 | Google Llc | System for real-time autosuggestion of related objects |
US10181116B1 (en) | 2006-01-09 | 2019-01-15 | Monster Worldwide, Inc. | Apparatuses, systems and methods for data entry correlation |
US10387839B2 (en) | 2006-03-31 | 2019-08-20 | Monster Worldwide, Inc. | Apparatuses, methods and systems for automated online data submission |
US10607273B2 (en) | 2016-12-28 | 2020-03-31 | Google Llc | System for determining and displaying relevant explanations for recommended content |
US10997560B2 (en) | 2016-12-23 | 2021-05-04 | Google Llc | Systems and methods to improve job posting structure and presentation |
CN113240400A (en) * | 2021-06-02 | 2021-08-10 | 北京金山数字娱乐科技有限公司 | Candidate determination method and device based on knowledge graph |
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Cited By (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2005024692A1 (en) * | 2003-09-03 | 2005-03-17 | Yahoo! Inc. | Automatically identifying required job criteria |
EP1706845A2 (en) * | 2003-12-02 | 2006-10-04 | Unisys Corporation | Improved cargo handling security handling system and method |
EP1706845A4 (en) * | 2003-12-02 | 2008-08-06 | Unisys Corp | Improved cargo handling security handling system and method |
US9959525B2 (en) | 2005-05-23 | 2018-05-01 | Monster Worldwide, Inc. | Intelligent job matching system and method |
EP1920364A4 (en) * | 2005-07-27 | 2010-10-13 | John Harney | System and method for providing profile matching with an unstructured document |
EP1920364A2 (en) * | 2005-07-27 | 2008-05-14 | John Harney | System and method for providing profile matching with an unstructured document |
US10181116B1 (en) | 2006-01-09 | 2019-01-15 | Monster Worldwide, Inc. | Apparatuses, systems and methods for data entry correlation |
US10387839B2 (en) | 2006-03-31 | 2019-08-20 | Monster Worldwide, Inc. | Apparatuses, methods and systems for automated online data submission |
US8021163B2 (en) * | 2006-10-31 | 2011-09-20 | Hewlett-Packard Development Company, L.P. | Skill-set identification |
US9779390B1 (en) | 2008-04-21 | 2017-10-03 | Monster Worldwide, Inc. | Apparatuses, methods and systems for advancement path benchmarking |
US9830575B1 (en) | 2008-04-21 | 2017-11-28 | Monster Worldwide, Inc. | Apparatuses, methods and systems for advancement path taxonomy |
US10387837B1 (en) | 2008-04-21 | 2019-08-20 | Monster Worldwide, Inc. | Apparatuses, methods and systems for career path advancement structuring |
US10997560B2 (en) | 2016-12-23 | 2021-05-04 | Google Llc | Systems and methods to improve job posting structure and presentation |
US9996523B1 (en) | 2016-12-28 | 2018-06-12 | Google Llc | System for real-time autosuggestion of related objects |
US10607273B2 (en) | 2016-12-28 | 2020-03-31 | Google Llc | System for determining and displaying relevant explanations for recommended content |
CN113240400A (en) * | 2021-06-02 | 2021-08-10 | 北京金山数字娱乐科技有限公司 | Candidate determination method and device based on knowledge graph |
Also Published As
Publication number | Publication date |
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WO2000026839A8 (en) | 2000-10-12 |
WO2000026839A9 (en) | 2001-08-02 |
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