Frame-slot architecture for data conversion

• US 8,190,985 B2
• Filed: 05/19/2009
• Issued: 05/29/2012
• Est. Priority Date: 06/13/2005
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Green, Edward A., Markey, Kevin L., Sharp, Alee
• Primary Examiner(s): HUYNH, CONG LAC T
• Application Number:
  US12/468,713
• Publication Number:
  US 20100077011A1
• Time in Patent Office: 1,106 Days
• Field of Search: 715/200, 715/249, 715/239
• US Class Current: 715/200
• CPC Class Codes: G06F 40/103 Formatting, i.e. changing of presentation of documents automatic justification G06F40/189; automatic line break hyphenation G06F40/191
• CPC Class Codes: G06F 40/151 Transformation
• CPC Class Codes: G06F 40/205 Parsing

Abstract

A machine based tool and associated logic and methodology are used in converting data from an input form to a target form using context dependent conversion rules. In particular, a frame-slot architecture is utilized where a frame represents an intersection between a contextual cue recognized by the machine tool, associated content and related constraint information to specific to that conversion environment, whereas a slot represents an included chunk of information. An exemplary conversion system (400) includes a parser (402) for use in parsing and converting an input stream (403) from a source (404) to provide an output stream (411) in a form for use by a target system (412). To accomplish the desired conversion, the parser (402) uses information from a public schema (406), a private schema (408) and a grammar (410). The public schema (406), private schema (408) and grammar (410) may include conversion rules applicable to less than the whole of a subject matter area including the input stream (403).

Claims

1. A method for use in operating a machine-based tool for converting data from a first form to a second form, comprising the steps of:

establishing, based on external knowledge of a subject matter area independent of analysis of a particular data set to be converted, a number of schema, each including one or more conversion rules for use in converting data within a corresponding context of said subject matter area;

identifying a set of data to be converted from said first form to said second form;

determining a particular context of said set of data;

based on said context, accessing an associated first schema of said number of schema; and

using an included conversion rule of said first schema in a process for converting said set of data from said first form to said second form;

wherein said step of establishing comprises identifying a private schema, including conversion rules specific to an entity or group of entities less than the public as a whole, that establishes a structure for understanding at least a portion of the subject matter area.

2. A method as set forth in claim 1, wherein said step of establishing comprises identifying a public schema, including conversion rules generally applicable to said subject matter area independent of any entity or group of entities associated with said set of data, that establishes a structure for understanding at least a portion of the subject matter area.

3. A method as set forth in claim 1, wherein said step of identifying comprises parsing a data stream to obtain an attribute phrase including information potentially defining a semantic object, an attribute of said object and an attribute value of said attribute.

4. A method as set forth in claim 3, wherein said step of determining comprises associating said semantic object with said particular context.

5. A method as set forth in claim 3, wherein said step of using comprises executing logic to interpret said information so as to identify said object, attribute or attribute value.

6. A method as set forth in claim 3, wherein said step of using comprises performing a comparison of said object, attribute or attribute value to a corresponding set of objects, attributes, or attribute values defined by said first schema.

7. A method as set forth in claim 6, wherein said step of using comprises using said comparison to convert said set of data from said first form to said second form.

8. A method as set forth in claim 6, wherein said step of using comprises using said comparison to identify an anomaly regarding said set of data.

9. A method as set forth in claim 3, wherein said step of using comprises identifying legal attribute values for said attribute.

10. A method as set forth in claim 1, wherein said step of establishing is implemented in a start-up mode for configuration of logic of said machine-based tool so as to convert data based on contextual cues inferred from an understanding of said subject matter area.

11. A method as set forth in claim 10, wherein said first schema is operative to enable proper conversion of a set of data which was not specifically addressed in said configuration.

12. A machine-based method for use in a conversion of data from a first form associated with a data source to a second form for use by a target data system, comprising the steps of:

receiving content associated with said data source;

obtaining first information from said content for use in said conversion;

first using said first information to obtain second information from a location, external to said content, for use in said conversion; and

second using said first information and said second information in said conversion of said content from said first form to said second form

wherein said step of second using is executed by a machine having access to a first semantic metadata model including a classification structure for a subject matter under consideration and associated conversion rules, and a number of sets of frame rules, separate from said semantic metadata model, for use in disambiguating terms based on specific subject matter contexts within said subject matter under consideration, and said step of second using comprises selecting one of said sets of frame rules.

13. A method as set forth in claim 12, wherein said content is structured data and said step of receiving comprises accessing structured data storage.

14. A method as set forth in claim 12, wherein said content is substantially unstructured.

15. A method as set forth in claim 12, wherein said step of obtaining comprises analyzing said content to determine a subject matter context of said content.

16. A method as set forth in claim 12, wherein said content comprises payload data including a meaning of said content and metadata describing said data, and said step of obtaining comprises using said metadata to derive said first information.

17. A method as set forth in claim 12, wherein said first information identifies a subject matter context of said content and said step of first using comprises accessing conversion rules specific to said subject matter context.

18. A method as set forth in claim 17, wherein said content includes an attribute or value susceptible of at least two possible meanings and said conversion rules are operative to select one of said at least two meanings based on said subject matter context.

19. A method as set forth in claim 17, wherein said rules are operative for identifying an impermissible attribute or attribute value based on said subject matter context.

20. A method as set forth in claim 12, wherein:

said first information comprises ambiguous data that is ambiguous in that it is susceptible, when considered in isolation, of at least two different conversions, and said first information further identifies a subject matter context of said ambiguous data;

said second information includes a rule for disambiguating said ambiguous data based on said subject matter context; and

said step of second using comprises applying said rule to said ambiguous data to select one of said at least two different conversions.