Source: http://www.google.com/patents/US8065336?dq=KOI-18
Timestamp: 2015-04-27 01:38:25
Document Index: 618612721

Matched Legal Cases: ['Application No. 03257974', 'Application No. 03257974', 'Application No. 03257974', 'Application No. 05027181', 'Application No. 200580013453', 'Application No. 200310123963', 'Application No. 200510132687', 'Application No. 2005', 'Application No. 200580013453', 'Application No. 10', 'Application No. 200310123963', 'Application No. 200310123963', 'Application No. 200510132687', 'Application No. 03257974', 'Application No. 05739051', 'Application No. 200310123963']

Patent US8065336 - Data semanticizer - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsA computer-implemented method of defining a set of annotation elements to map a concept to electronic data as input data; generating a mapping rule, according to the set of annotation elements defined and a sample of the input data; mapping the concept to the input data by applying the mapping rule to...http://www.google.com/patents/US8065336?utm_source=gb-gplus-sharePatent US8065336 - Data semanticizerAdvanced Patent SearchPublication numberUS8065336 B2Publication typeGrantApplication numberUS 11/014,904Publication dateNov 22, 2011Filing dateDec 20, 2004Priority dateDec 20, 2004Also published asCN1794234A, CN100495395C, EP1672537A2, EP1672537A3, EP1672537B1, US20060136194Publication number014904, 11014904, US 8065336 B2, US 8065336B2, US-B2-8065336, US8065336 B2, US8065336B2InventorsPatrick Joseph Armstrong, Nada Hashmi, Sung Youn Lee, Ryusuke Masuoka, Zhexuan SongOriginal AssigneeFujitsu LimitedExport CitationBiBTeX, EndNote, RefManPatent Citations (59), Non-Patent Citations (88), Referenced by (4), Classifications (7), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetData semanticizer
US 8065336 B2Abstract
defining a set of annotation elements to map a concept to electronic data as input data, wherein the set of annotation elements include:
a selected ontology corresponding to a domain of the input data,
a selected ontology concept from the selected ontology as the concept to map,
a mapping of a word or word phrase in a sample input data to the selected ontology concept from the selected ontology; and
a pattern of a word or word phrase relative to a structure of the sample input data, for mapping to the selected ontology concept from the selected ontology;
generating a mapping rule, according to the defined set of annotation elements;
2. The method of claim 1, wherein the generating of the mapping rule comprises:
3. The method of 2, wherein the suggesting of the sample mapping of the selected ontology concept from the selected ontology to the to the word or word phrase in the sample input data comprises same perceptibly distinguishing the word or word phrase in the sample input data as the selected ontology concept.
providing the semantic instance, as an abstraction of the input data, usable within a task computing environment.
displaying the input data, wherein the set of annotation elements defined to map the concept to the input data further comprises perceptibly distinguishing the selected ontology concepts of the selected ontology mapped to the sample of the input data displayed, and the perceptibly distinguishing comprises visually distinguishing same ontology concepts on a display screen via coloring, fonts, font sizing, underlining, bolding, italicizing, numbering, displaying icons, or any combination thereof.
7. The method of claim 1, wherein the input data is structured, semi-structured, unstructured, or any combination thereof.
8. The method of claim 1, wherein location information, a regular expression, or any combination thereof determine the pattern of the mapped word or word phrase relative to the structure of the sample input data.
9. The method of claim 8, wherein templates of the location information and the regular expression depending on the input data are used to determine the pattern of the mapped word or word phrase relative to the structure of the sample input data.
10. The method of claim 1, wherein a plurality of mapping rules are generated and the method further comprises:
12. The method of claim 1, wherein templates are used to determine the pattern of the mapped word or word phrase relative to the structure of the sample input data.
approximating a structure of the sample input data based upon the mapping of the ontology concept;
capturing a structure of the input electronic data according to the approximating of the structure of the sample input data; and
generating semantic instances for the input electronic data based upon the captured structure of the data and/or the mapping of the concept to the input data.
optimizing the generated data structure capture rule according to the user input by modifying the selected ontology, the sample input data, the selected ontology concept, the mapping of the selected ontology concept to the sample input data, or any combination thereof.
15. The method of claim 13, wherein the capturing the structure of the sample input data further comprises:
16. The method of claim 1, wherein the input data is a single input file or multiple input files, and the generating of the semantic instance comprises generating a single output file containing multiple semantic instances, or generating multiple output files with each output file containing one or more semantic instances from the input data.
17. The method of claim 16, wherein the output files are according to the Resource Description Framework/Web Ontology Language and/or Relational Database format.
generating a semantic instance for the input data according to the mapping of the concept to the input data, thereby providing a user controlled data semanticization service for other input data.
19. The method of claim 18, wherein a plurality of concepts are mapped to the input data and a plurality of user controlled semantic instances are generated, and the method further comprises generating a list of the generated user controlled semantic instances based upon the input data.
22. The apparatus of claim 21, wherein the generating of the mapping rule comprises:
23. The apparatus of claim 21, wherein the apparatus controlling process by the programmed computer processor further comprises:
24. The apparatus of claim 21, wherein the apparatus controlling process by the programmed computer processor further comprises:
25. The computing apparatus according to claim 21, wherein the programmed computer processor further executes operations comprising:
generating a data structure capture rule based upon the mapping of the selected ontology concept to the sample input data;
suggesting to the user a mapping of the selected ontology concept to other input data, as the data structure capture rule;
optimizing the generated data structure capture rule according to the user input by adjusting the selected ontology, the sample input data, the selected ontology concept, the mapping of the selected ontology concept to the sample input data, or any combinations thereof,
wherein the generating of the semantic instance includes semanticizing the input data based upon applying the generated optimized data structure capture rule to the other input data, if the user accepts the mapping suggestion that maps the selected ontology concept to the other input data and/or based upon the mapping of the concept to the input data.
26. A computing apparatus, comprising:
means for defining a set of annotation elements to map a concept to electronic data as input data by:
selecting an ontology corresponding to a domain of the input data,
selecting an ontology concept from the selected ontology as the concept to map,
mapping a word or word phrase in a sample input data to the selected ontology concept from the selected ontology, and
determining a pattern of a word or word phrase relative to a structure of the sample input data, for mapping to the selected ontology concept from the selected ontology;
Conventional semantic World Wide Web, or �Web,� technologies involving ontology-based representations of information enable the cooperation of computers and humans and can be used to assist with data sharing and management. Through ontological representation, the modeling of entities and relationships in a domain allows the software and computer to process information as never before [www.sys-con.com/xml/article.cfm?id=577, retrieved on Oct. 22, 2004]. Conventional semantic Web technologies are an extension of the World Wide Web, which rely on searching Web pages and bringing the Web page to the semantic Web page level. Therefore, conventional semantic Web technologies process Web pages, which as tagged documents, such as hypertext markup language (HTML) documents, are considered fully structured documents. Further, the conventional semantic Web technologies are only for presentation, but not for task computing (i.e., computing device to computing device task processing). WEB SCRAPER software is an example of a conventional semantic Web technology bringing Web pages, as structured documents, to the semantic level. However, adding semantics to semi-structured or unstructured data, such as a flat file, is not a trivial task, and traditionally this function has been performed on a case-by-case (per input data) manner, which can be tedious and error-prone. Even when annotation is automated, such automation only targets a specific domain to be annotated.
FIG.1 is a flow chart of a data semanticizer 100 to annotate electronic data 108, in any format, in any domain, with semantics, as implemented in computer software controlling a computer. In FIG. 1, a semanticization flow by the data semanticizer 100 comprises two semanticization operations of rule set generation 102 (shown in the dotted box), and semantic instances generation 104 (shown in the solid double polygon). The rule set generation 102 can be a one time (single) process (but not limited to a single process) and can be performed, for example, by either a domain expert or a system administrator. The domain expert or the system administrator can be human, computer implemented, or any combination thereof. Operation 102 generates a semanticization rule set 110. Once, at operation 102, the rule set 110 is available, at operation 104, semantic instance(s) 118 can be generated based upon the rule set 110. A �semantic instance� 118 is a set of description(s) on an individual item based on a concept(s). An item(s) can be any part of input data 108.
The ontology 116 can be one or more of same and/or different domain ontologies stored on computer readable media according to an electronic information format, such as Web Ontology Language (OWL) file format. Therefore, the data semanticizer 100 is not limited to generating semantic instances 118 corresponding to a single ontology 116, and the data semanticizer 100 can generate semantic instances 118 where different data parts map to a plurality of different ontologies 116. For example, let's consider the input data 108 string �A research fellow at FUJITSU LABORATORIES OF AMERICA (FLA) leads a Task Computing project. He was also involved in LSM, Agent, and other projects during his tenure at FLA. He is also an adjunct professor at UNIVERSITY OF MARYLAND (UM) advising several students.� To annotate such data 108, most likely it will involve ontology concepts defined in an FLA ontology 116 (e.g. projects managing, projects involved properties, etc) and a UM ontology 116 (e.g., advisees, topics properties, etc.).
The generating of the mapping rule 110 to map a concept to the input data 108, or to capture the structure of the input data 108, comprises, at operation 106, suggesting a sample mapping of a concept (i.e., the selected ontology concept from the selected ontology 116) to a word or word phrase in a sample input data 114, as the mapping rule of the input data 108, and selecting a suggested mapping as the mapping rule of the input data 108, or a data structure rule of the input data 108. At operation 112, the mapping rule 110 is applied to the input data 108 to map the concept to the input data 108 to output semantic instances 118. Therefore, �a mapping rule� (semanticization rule set in FIG. 1) 110 is based upon a mapping of a word or word phrase relative to a structure of input data 108. The sample input data 114 can be, for example, a sample number of opened input data files 114 (e.g., 10 files each containing one email from among hundreds of files), or can be one input data file 114 that contains a number of records (e.g., one file containing hundreds of emails from among a plurality of files, where the user works with one email in the one file, but the system suggests all or any subset of email addresses appearing in the rest of the file(s)).
The role of the data semanticizer 100 is to annotate data with semantics to bring data into a higher level of abstraction. Low level data can be easily extracted from higher levels of abstraction, but this is not true for the other direction. An example is comparing structured to unstructured data. Structured data is easy to represent in plain text format. For example, a LATEX document can be easily converted to a format for a display or a printer (LATEX to Device-Independent (DVI) file format to Bitmap). However, converting a Bitmap to a LATEX document would be extremely difficult; this is where the data semanticizer 100 helps, because of the efficient defined set of elements (implemented as a semanticization rule editor) to capture a structure of electronic data as input data, generating a rule according to the set of elements defined to capture the structure of the input data, applying the rule to the input data, and, generating a semantic instance of the input data based upon the rule applied to the input data. With the data semanticizer 100, the procedure of annotating data with semantics can be completed with reduced human interactions. Therefore, a new term, �semanticize,� is introduced to denote adding semantic annotations to data, according to the present invention.
�C� is the concept from the selected ontology 116 corresponding to the class and its property for which the user wants to create an instance.
�W� is the word or word phrase in the sample data 114 that is being conceptualized. The user can specify �W� by, for example, highlighting the word(s) from a displayed sample data 114�for example, a displayed sample document from among a plurality of documents as the input data 108. The �C� and �W� are data structure capture elements that can incorporate user assistance.
�R� is the region of the �W� word or the word phrase relative to the structure of an input data 108 (or a portion of an input data 108), for example, a document. Typically in the present invention, the �R� element is determined relative to the structure of a sample 114 of the data 108 (or a portion a sample 114). Two methods of determining the �R� element to capture a structure of input data is described�location information and regular expressions. The details of these two methods, as data structure capture elements, are described further below. The �R� element is performed by the system (semanticization rule editor 106) as a representation of �C� and �W.� In the present invention, the �R� data structure capture element is based upon an ontology and a data point (for example, a word or word phrase, and/or any other types of data points) mapped to a concept in the ontology, thereby providing a domain or ontology rule-based knowledge system to capture structure of input data. The present invention provides a method of defining a set of annotation elements to map a concept to electronic data.
�K� is the color that uniquely distinguishes one complete �C� concept from another in a displayed sample data 114. For example, assume creation of an instance of a class called Person, in which hasFirstName and hasLastName are properties. When creating a semantic instance of the class Person, the rule editor 106 automatically lists these two properties and groups them as properties of the same class by assigning the same color, in the displayed sample data 114. The present invention is not limited to coloring for distinguishing displayed concepts, and other perceptible distinguishing characteristics/attributes/techniques (e.g., visual and/or audible) can be used, such as (without limitation) visually distinguishing characteristics on a computer display screen via fonts, font sizing, underlining, bolding, italicizing, numbering, displaying icons, etc.
�P� is the priority of the rule. Priority is used to increase efficiency while reducing errors, when, at operation 112, applying a plurality of generated mapping rules 110 of the input data 108. Priority can be used to determine erroneous application of a rule set 110. When high priority rules cannot be applied, semantic instance creation process stops, whereas low priority rules can be safely ignored. For example, when trying to match words from the sample document 114 to an ontology concept from the ontology 116, some of the words may be important than others. For example, if a gene sequence includes a version number, the actual gene sequence can be given a higher priority than the version number, so that if some files omit the version number, the system does not fail to create semantic instances (i.e., mapping out the version number, if necessary).
�O� is the order in which a plurality of generated mapping rules 110 are applied; e.g., O1 is the first rule to be applied, O2 is the second rule to be applied, etc.
Therefore, a set of atomic rules together defines a rule set 110, referred to as a mapping, semanticization, or data structure capture, rule set 110, to map a concept to input data 108, such as documents, email messages, etc., in any format and in any domain. A minimum atomic rule comprises a set of 3 annotation or data structure capture, tuples <C, W, R>, of which �C� and �W� can incorporate user assistance. In the above example, the data structure capture elements <K, P, O>, enhance performance, but are not required. Further, the set of 3-tuples <C, W, R> can be combined in any combination with other data structure capture elements, such as, for example, the <K, P, O> data structure capture elements.
Two examples of methods, including any combinations thereof, for determining the region of word(s)�the �R� element�is described in more detail below. Therefore, the location information can be combined with regular expression as another method of determining the �R� element to capture a structure of input data.
Location Information�Using highlighted location information in the sample data 114, �R� is represented as 4-tuples, <L, S, N, E> (location data structure capture elements) where
essentially capturing �columns� corresponding to words to be conceptualized.
The location elements essentially capture a location in the sample input data 114 corresponding to the word or word phrase, as the �W� element, which is to be conceptualized by being mapped to the selected ontology concept from the ontology 116.
Regular Expressions (Patterns)�Alternatively, regular expressions can be used to deduce a pattern in the input data 108, via the sample data 114, for region of word(s)�the �R� element. In this approach, �R� is a regular expression, which is described in terms of assumptions, inputs, outputs, and the process, as follows�
The data consist of a number of records each with a number of fields. The delimiters between records are easily recognizable. Each field in a record has some defining characteristics, which distinguishes it from the other fields. Input data 108 example:
A list of records containing the data which the user desires to parse. The begin and end indices of a substring from within the data, this is an example of the data which the user desires to extract�the �W� data structure capture element. A tolerance value which defines an acceptable match. Process operations example:
4. After each record has been processed, the total number of matches for a particular regular expression is checked. The regular expression is rejected automatically, if the number of match count does not fall within the tolerance level (the number of records�the tolerance value). In this case, the parse returns to operation 2.
FIG. 2 is a flow chart of semanticizing email text as input electronic data, according to an embodiment of the present invention. More particularly, an example of semanticization by the semanticizer 100 according to the above process operations 1 through 5, using emails (email messages/text), as input data 108, and using the above-described regular expressions for the �R� data structure capture element to determine a region of the �W� data structure capture element, which is a mapping to the �C� data structure capture element, in a sample 114 of the input data 108, is shown with reference to FIG. 2.
In FIG. 2, at operation 150, the input file 108 contains a set of email headers, and �dean@cs.umd.edu� is the example substring��W� data structure capture element�which is mapped (as shown via a displayed highlight) to a selected ontology concept from the ontology 116 (not shown in FIG. 2, but see FIG. 4) and serves as sample data 114 from the input file 108. At operation 152, the pattern generator (also referred to as the semanticization rule editor 106) attempts to approximate the structure of the given input file 108 based on regular expression templates 160. At operation 154, the pattern generator 106 suggests a regular expression 160, to capture the structure of the input file 108, to the user. At operation 156, the user examines the suggestion. At operation 156, the user can either accept or reject the suggestion of the regular expression as the structure rule of the input data 108.
More particularly, in FIG. 2, the left most case in operation 154 shows the string �dean@cs.umd.edu� as a match using the example string �dean@cs.umd.edu� as a regular expression��R� data structure capture element. However, the input file 108 contains exactly one string that matches the regular expression �dean@cs.umd.edu,� (indicated via display screen yellow highlighting) and this regular expression can be ignored, because it generated too few matches. The middle case in operation 154 shows all email addresses as being matched using the regular expression �\w+@\w+.\w+.� This regular expression matched all of email addresses that appeared in the input file 108; however, this expression again can be skipped, because it generated too many matches. The third case in operation 154 shows the matches using the regular expression �From: \S+@\S+,� in which the matches are suggested to the user for inspection. In the FIG. 2 example, the system 100 internally eliminates cases 1 (left) and 2 (middle), according to configurable application design criteria, but the claimed present invention is not limited to such a configuration and the system 100 could be controlled (programmed), for example, to suggest to the user all outputs of the pattern generator 106 including a recommended suggestion.
Semanticization Rule Editor 106: The semanticization rule editor 106 takes samples 114 from a collection of data 108 and its corresponding ontology 116 as input and assists users in defining the semanticization rule set 110 per data collection 108. Typically in the present invention, the rule set 110 is generated with assistance from a domain expert who is familiar with the data collection. In FIG. 4, the computer displayed graphical user interface window 204 is an optional user interface window that can display various representations of operations by the semanticization rule editor 106 (i.e., semanticization rule viewer 204), such as displaying a generated rule expression�the �R� data structure capture element. In FIG. 4, the user interface window 204 displays ontology concepts, including a number thereof, that are mapped to the data displayed in the data viewer user interface window 202. For example, FIG. 4 shows that the COMMENT property of the protein concept (subclass) of the biopax-level1:PhysicalEntity class 208 is mapped once (1) and the ontology concept mapping is also visually indicated by a same color (red color in this example and also connected by a line)�the �K� data capture structure element�in both the semanticization rule editor user interface window 204 and the data viewer user interface window 202.
Several additional components developed by FUJITSU LIMITED, Kawasaki, Japan, assignee of the present application, or others can be added to the ontology viewer tools 200 and the data viewer 202 environments. These include ontology mapping tools, inference engines, and data visualization tools. Ontology mapping tools, such as ONTOLINK [www.mindswap.org/2004/OntoLink, retrieved on Oct. 22, 2004] can be used to specify syntactic and semantic mappings and transformations between concepts defined in different ontologies. Inference engines such as PELLET [www.mindswap.org/2003/pellet/index.shtml, retrieved on Oct. 22, 2004] and RACER [www.cs.concordia.ca/�haarslev/racer/, retrieved on Oct. 22, 2004] can help check for inconsistencies in the ontologies and further classify classes. Data visualization tools, such as JAMBALAYA [www.thechiselgroup.org/jambalaya, retrieved on Oct. 22, 2004] and RACER INTERACTIVE CLIENT ENVIRONMENT (RICE) [www.cs.concordia.ca/�haarslev/racer/, retrieved on Oct. 22, 2004] can be used to present semantic instances 118 (i.e., data content 108 as annotated by the data semanticizer 100) with respect to its ontology 116, providing a visualization of annotated data 118, which can be displayed in the data viewer user interface window 202. In other words, any other third party ontology viewer and data viewer can be used, such as JAMBALAYA and RICE, which are visualization tools, to present annotated data content or a knowledge base with respect to its ontology, but such visualization tools do not have annotation capability.
Therefore, in FIG. 4, the rule pane 204 serves as a container for definitions of associations between ontological concepts 116 and raw data 108, these associations referred to as �mapping rules� 110 (i.e., rule pane 204 implemented as a computer readable medium storing mapping rules and GUI(s) based thereon). A �mapping rule� 110, is a mapping between an ontology representation 116, such as a Web Ontology Language (OWL) property, which is displayed in the ontology viewer 200, and some form of raw data 108, such as strings of text, which is displayed in the data pane 202. In FIG. 4, for example, the semanticization rule editor 106 maps a data point 205, as a sample 114, to a selected ontology class property NAME, as shown in the ontology viewer 200 and the rule viewer 204 (i.e., indicated by the same �K� value, which in this example is highlighted blue for NAME), and for which an �mapping rule� 110 is determined based on �R� data structure capture element by associating the data point 205 (e.g., text) with a rule, via the �Associate Text with Rule� 302. The purpose of the �mapping rule� 110 is to collect samples of data 114 that a smart parser (semanticization rule editor 106) can use to try to discover similar data through suggestions in the remainder of the database 108, as described in more detail below with reference to FIG. 6. Accordingly, the �mapping rule� 110 essentially captures a structure of data 108 based upon a selected domain ontology or the �mapping rule� captures an ontology structure of data 108. According to aspect of the invention, when the smart parser 106 correctly identifies data, the smart parser 106 adds its discoveries back into the original mapping rule definition. Thus, each correct guess by the smart parser 106, theoretically, increases its ability to recognize subsequent similar datum 108. The parser 106 is �smart� because the input file 108 might have no set pattern that can be assumed to parse. In most parsers, the structure of the input file is known and the parser makes use of the known structure to automate the parsing process. Without this prior structure knowledge, it can be quite difficult to automate the parsing process. The parser 106 automates the parsing by trying multiple templates, heuristics, and thresholds, to suggest ontology concept mappings, while typically in the present invention leaving the ultimate decision process to accept the suggestions to be done by humans, and where the suggestions can reflect, or be used to derive, a structure of the input file 108. Once the end user confirms that what the data semanticizer 100, as a �mapping rule� 110 has suggested is correct, the �mapping rule� 110 is stored and can be presented via the rule pane 204. As the data semanticizer 100 collect more rules 110 that are already confirmed by humans as correct, the data semanticizer can utilize these previously confirmed rules in the remainder of data semanticization process (operation 104) if similar patterns appear again. In other words, the tool 106 utilizes what it has learned about the input file 108.
The data pane 202 displays the data 108 from which the user wishes to extract data. Annotated data will be highlighted in different colors depending upon the property with which it is associated, as the �K� data structure capture element. As an example of inputting control commands to the data semanticizer 100, the keypad 206 is used as a handy menu type control panel, which allows the user to quickly execute certain common tasks, such as (without limitation and in any combination thereof) add a rule (i.e., map a data point to a selected ontology concept), remove selection from rules, associate text with rule to generate the �R� data structure capture element, and/or generate an instance. The present invention is not limited to the keypad 206 implementation, and, for example, to map a sample data point to an ontology concept, typically in the present invention any available displayed data selection techniques can be used, such as selecting a region of a displayed sample input data 114 in the data viewer 202 and dropping the grabbed selection into a displayed concept of the ontology 116 in the ontology viewer 200.
In FIG. 5, at operation 268, semantic instances 118 are output. Given the rule set 110 and the data set 108, the data semanticizer 100 generates corresponding semantic instances 118. FIGS. 6-7 are example images of graphical user interfaces of a data semanticizer semanticizing bioinformatics as input electronic data, according to an embodiment of the present invention. More particularly, FIGS. 6-7 show an example of the data semanticizer 100 annotating bioinformatics data using the regular expression method as the �R� data structure capture element. When a user accepts matches suggested by the data semanticizer 100 through the process similar to the processes shown in FIG. 2, a user may elect to populate rules 110 with data in the input file 108. A conveniently displayed selectable menu keypad 206 provides an easy access to frequently used menu items.
In FIG. 6, for each selected ontology class and all of its properties mapped to a data point 108, as shown in the ontology viewer 200 and the rule viewer 204 (i.e., indicated, via a mapping by selecting �Add a Rule� 300, by the same �K� value, which in this example is highlighted orange for COMMENT (Description: . . . ), highlighted yellow for NAME, highlighted red for SEQUENCE, highlighted dark green for SHORT-NAME, and highlighted light green for SYNONYMS), the �mapping rules� are determined based on �R� data structure capture element by associating a data point (e.g., text) with a rule, via the �Associate Text with Rule� 302 (operation 260 in FIG. 5) and providing suggested matches 306 for acceptance, rejection and/or optimization (operations 262, 264 and/or 266 in FIG. 5). In particular, FIG. 6 shows that the parser 106 has just completed for data point 205 discovering similar data 308 for the NAME ontology class property, in a remainder of a sample 114 of a database 108, which is highlighted in yellow upon selecting �Associate Text with Rule� 302 and the parser 106 provides similar data suggestions 308 displayed by red color font.
Upon acceptance of suggestions and a successful completion of an error checking mechanism, a semantic instance can be created, via �Generate an Instance� selection 304, using the following procedure:
1. For each row of the same color �K,� create an instance of the class with property values using �column� information stored.
FIG. 7 shows all properties have been fully populated after selecting generate an instance 304, as indicated by the same �K� value, which in this example is a highlighted orange for COMMENT (Description: . . . ), highlighted yellow for NAME, highlighted red for SEQUENCE, highlighted dark green for SHORT-NAME, and highlighted light green for SYNONYMS. In FIGS. 4, 6 and 7, drawn lines also illustrate the mapping of ontology concepts to data points.
FIG. 8B: One data point is mapped to name property of �city� class of terrorism ontology 116. Again, the output file test2.OWL contains exactly one data point as one semantic instance 118. Here it is illustrated that the tool 100 is just as applicable in other domains (other than bioinformatics domain). The reference for the terrorism ontology is [www.mindswap.org/2003/owl/swint/terrorism, retrieved on Dec. 16, 2004].
FIGS. 8F-8H: Twelve data points are mapped to comment property of �dataSource� class of BIOPAX ontology 116. In addition to showing the capability to generate multiple semantic instances 118 in one output file (test4.OWL), it also shows that the parser 106 captures the input file 108 properly when there is no apparent pattern in the input file 108. In particular, in test4.OWL shown in FIGS. 8F-8H, there are twelve data points in an input file 108. They are, in the order of appearance, MINDSWAP, FLACP, FLACP, FLACP, UMIACS, UMIACS, MINDSWAP, MINDSWAP, MINDSWAP, UMIACS, UMIACS, and UMIACS. The data semanticizer 100 generates a regular expression 110 to capture the twelve data points when there is no pattern in the input file 108.
More particularly, the present invention provides a computer system, as a data semanticizer 100, to assist a user to annotate with semantics a large volume of electronic data in any format, including semi-structured to unstructured electronic data, in any domain. Therefore, the present invention provides an ontological representation of electronic data in any format and any domain. Use of semantic Web technologies to provide interoperability via resource and service abstractions, thereby providing a task computing environment, is successfully demonstrated and described by FUJITSU LIMITED, Kawasaki, Japan, assignee of the present application, in the following publications and/or patent applications (all of which are incorporated herein by reference) by R. Masuoka, Y. Labrou, B. Parsia, and E. Sirin, Ontology�Enabled Pervasive Computing Applications, IEEE Intelligent Systems, vol. 18, no. 5, September/October 2003, pp. 68-72; R. Masuoka, B. Parsia, and Y. Labrou, Task Computing�the Semantic Web meets Pervasive Computing, Proceedings of the 2nd International Semantic Web Conference 2003, Oct. 20-23, 2003, Sundial Resort, Sanibel Island, Fla., USA; Z. Song, Y. Labrou and R. Masuoka, Dynamic Service Discovery and Management in Task Computing, MobiQuitous 2004, Aug. 22-25, 2004, Boston, USA; Ryusuke Masuoka, Yannis Labrou, and Zhexuan Song, Semantic Web and Ubiquitous Computing�Task Computing as an Example�AIS SIGSEMIS Bulletin, Vol. 1 No. 3, October 2004, pp. 21-24; Ryusuke Masuoka and Yannis Labrou, Task Computing�Semantic-web enabled, user-driven, interactive environments, WWW Based Communities For Knowledge Presentation, Sharing, Mining and Protection (The PSMP workshop) within CIC 2003, Jun. 23-26, 2003, Las Vegas, USA; in copending U.S. non-provisional utility patent application Ser. No. 10/733,328 filed on Dec. 12, 2003; and U.S. provisional application Nos. 60/434,432, 60/501,012 and 60/511,741. Task Computing presents to a user the likely compositions of available services based on semantic input and output descriptions and creates an environment, in which non-computing experts can take advantage of available resources and services just as computing experts would. The data semanticizer 100 has a benefit of bringing similar interoperability to application data sets in any format and in any domain.
The BIO-CENTRAL is a website which allows access to a knowledge-base of semantically annotated biological data. It exemplifies the benefits of a semantically described data. The data semanticizer 100 can be used to annotate molecular interaction data from the Biomolecular Interaction Network Database (BIND) [Bader, Betel, and Hogue, �BIND: The Biomolecular Interaction Network Database,� Nucleic Acids, Res, PMID, Vol. 31, No. 1, 2003] with the BIOPAX-LEVEL1 (Biological Pathway Exchange Language) [Bader et al. �Bio-PAX�Biological Pathways Exchange Language, Level 1, Version 1.0 Documentation,� BioPAX Recommendation, [www.biopax.org/Downloads/Level1v1.0/biopax-level.zip, retrieved on Oct. 22, 2004]] ontology. The annotated data 118 are then deposited into the BIO-CENTRAL database.
When the data is annotated with rich semantics, the data can be easily manipulated, transformed, and used in many different ways. However, the work of �pushing� data into a higher level is not trivial. The framework of data semanticizer 100 works as a �pump� and helps users to complete the procedure in a much easier way by defining (implementing in software) a set of annotation elements to capture a structure of electronic data as input data; generating a rule, according to the set of annotation elements defined and a sample of the input data, to capture the structure of the input data; applying the rule to the input data; and generating a semantic instance of the input data based upon the rule applied to the input data.
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