FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION (See Section 10 and Rule 13)
Title of invention:
METHOD AND SYSTEM OF AUTOMATED GENERATION OF A BASELINE CONCEPT MODEL
Applicant
Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India
Preamble to be the description
The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD [001] The disclosure herein generally relates to data management, and, more particularly, to method and system of automated generation of a baseline concept model.
BACKGROUND
[002] Several business problems demand a conceptual model of their domain, i.e., a concept model. Enterprises often need a way to quickly arrive at the concept model that is not a full ontology but one that sufficiently covers core concepts, relations, and attributes without an expert’s or a non-expert’s involvement. Enterprises rely on domain experts’ knowledge in constructing the concept model. But domain expert’s knowledge is implicit, and the domain experts often need to refer to several information resources to put together the concept model useful in a specific problem context. Enterprises often possess diverse information resources such as system user manuals, knowledge guides, information brochures, log files, etc. that the domain expert may refer to create a draft of the concept model which can be refined later.
[003] Even though various information resources may be available to the domain expert along with tools that automate concept model building based on natural language processing (NLP) or machine learning (ML) approaches, most of them either require the expert to build the concept model from scratch or annotate some of the resources as the starting point in building the concept model. It is also possible that availability of domain experts is an issue, and it is not known which information resources to use as a source material in building the concept model. Current technology/solutions assume that a complete ontology is required to address a given problem and that the domain experts are available to provide their input in building the full ontology. Therefore, existing approaches does not provide a way to arrive at the concept model without the domain experts input.
SUMMARY

[004] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one aspect, a processor implemented method of automatically generating of a baseline concept model is provided. The processor implemented method includes at least one of: receiving, via one or more hardware processors, at least one textual information available with an enterprise as a source material; determining, via the one or more hardware processors, a plurality of top ‘n’ keywords by implementing a text ranking on the at least one textual information; creating, via the one or more hardware processors, a pseudo dictionary for each keyword in the plurality of top ‘n’ keywords; extracting, via the one or more hardware processors, at least one triple from the pseudo dictionary by implementing an open information extraction; and generating, via the one or more hardware processors, the baseline concept model based on the at least one triple. The at least one triple correspond to a subject-verb-object. A specific verb from the at least one triple corresponds to a relation, an attribute, a hierarchy, or combination thereof.
[005] In an embodiment, the pseudo dictionary is created by adding a singular form and a plural form of the plurality of top ‘n’ keywords. In an embodiment, the pseudo dictionary is created by obtaining at least one noun chunk or at least one phrase from the at least one textual information and at least one concept. In an embodiment, at least one keyword occurs within or at end of the at least one phrase. In an embodiment, the at least one noun chunk is considered as at least one mention and stored in the pseudo dictionary for each keyword in the plurality of top ‘n’ keywords, whenever at least one noun chunk comprising the at least one keyword is occurred. In an embodiment, the at least one keyword or the at least one phrase that occur in a subject position and an object position to obtain at least one verb as at least one possible candidate for a relation.
[006] In another aspect, there is provided a system for automated generation of a baseline concept model. The system includes a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces,

wherein the one or more hardware processors are configured by the instructions to: receive, at least one textual information available with an enterprise as a source material; determine, a plurality of top ‘n’ keywords by implementing a text ranking on the at least one textual information; create, a pseudo dictionary for each keyword in the plurality of top ‘n’ keywords; extract, at least one triple from the pseudo dictionary by implementing an open information extraction; and generate, the baseline concept model based on the at least one triple. The at least one triple correspond to subject-verb-object. A specific verb from the at least one triple corresponds to a relation, an attribute, a hierarchy, or combination thereof.
[007] In an embodiment, the pseudo dictionary is created by adding a singular form and a plural form of the plurality of top ‘n’ keywords. In an embodiment, the pseudo dictionary is created by obtaining at least one noun chunk or at least one phrase from the at least one textual information and at least one concept. In an embodiment, at least one keyword occurs within or at end of the at least one phrase. In an embodiment, the at least one noun chunk is considered as at least one mention and stored in the pseudo dictionary for each keyword in the plurality of top ‘n’ keywords, whenever at least one noun chunk comprising the at least one keyword is occurred. In an embodiment, the at least one keyword or the at least one phrase that occur in a subject position and an object position to obtain at least one verb as at least one possible candidate for a relation.
[008] In yet another aspect, there are provided one or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes at least one of: receiving, at least one textual information available with an enterprise as a source material; determining, a plurality of top ‘n’ keywords by implementing a text ranking on the at least one textual information; creating, a pseudo dictionary for each keyword in the plurality of top ‘n’ keywords; extracting, at least one triple from the pseudo dictionary by implementing an open information extraction; and generating, a baseline concept model based on the at least one triple. The at least one triple correspond to a subject-verb-object. A specific verb from the at least one triple corresponds to a relation, an attribute, a hierarchy, or combination thereof.

[009] In an embodiment, the pseudo dictionary is created by adding a singular form and a plural form of the plurality of top ‘n’ keywords. In an embodiment, the pseudo dictionary is created by obtaining at least one noun chunk or at least one phrase from the at least one textual information and at least one concept. In an embodiment, at least one keyword occurs within or at end of the at least one phrase. In an embodiment, the at least one noun chunk is considered as at least one mention and stored in the pseudo dictionary for each keyword in the plurality of top ‘n’ keywords, whenever at least one noun chunk comprising the at least one keyword is occurred. In an embodiment, the at least one keyword or the at least one phrase that occur in a subject position and an object position to obtain at least one verb as at least one possible candidate for a relation.
[010] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[011] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
[012] FIG. 1 illustrates a block diagram of an exemplary system for automated generation of a baseline concept model, according to an embodiment of the present disclosure.
[013] FIG. 2 illustrates an exemplary functional block diagram of the system of FIG.1, according to some embodiments of the present disclosure.
[014] FIG. 3 is an exemplary flow diagram illustrating method for automated generation of the baseline concept model, according to an embodiment of the present disclosure.
[015] FIG. 4 is an exemplary line diagram illustrates the automated generation of the baseline concept model, according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[016] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments.
[017] There is a need to arrive at a concept model without an input from the domain experts. Embodiments of the present disclosure provide a method and system to obtain the concept model such as a baseline domain model using a textual information resources available to an enterprise and without experts’ input. The embodiment of the present disclosure utilizes one or more textual information resources available with the enterprise as a source material from which to build the baseline domain model.
[018] Referring now to the drawings, and more particularly to FIGS. 1 through 4, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[019] FIG. 1 illustrates a block diagram of an exemplary system 100 for automated generation of the baseline concept model, according to an embodiment of the present disclosure. In an embodiment, the system 100 includes one or more processor(s) 102, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 104 operatively coupled to the one or more processors 102. The memory 104 includes a database. The one or more processor(s) processor 102, the memory 104, and the I/O interface(s) 106 may be coupled by a system bus such as a system bus 108 or a similar mechanism. The one or more processor(s) 102 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines,

logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more processor(s) 102 is configured to fetch and execute computer-readable instructions stored in the memory 104. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud, and the like.
[020] The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface device(s) 106 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a camera device, and a printer. Further, the I/O interface device(s) 106 may enable the system 100 to communicate with other devices, such as web servers and external databases. The I/O interface device(s) 106 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. In an embodiment, the I/O interface device(s) 106 can include one or more ports for connecting number of devices to one another or to another server.
[021] The memory 104 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory 104 includes a plurality of modules 110 and a repository 112 for storing data processed, received, and generated by the plurality of modules 110. The plurality of modules 110 may include routines, programs, objects, components, data structures, and so on, which perform particular tasks or implement particular abstract data types.
[022] Further, the database stores information pertaining to inputs fed to the system 100 and/or outputs generated by the system (e.g., data/output generated

at each stage of the data processing) 100, specific to the methodology described herein. More specifically, the database stores information being processed at each step of the proposed methodology.
[023] Additionally, the plurality of modules 110 may include programs or coded instructions that supplement applications and functions of the system 100. The repository 112, amongst other things, includes a system database 114 and other data 116. The other data 116 may include data generated as a result of the execution of one or more modules in the plurality of modules 110. Further, the database stores information pertaining to inputs fed to the system 100 and/or outputs generated by the system (e.g., at each stage), specific to the methodology described herein. Herein, the memory for example the memory 104 and the computer program code configured to, with the hardware processor for example the processor 102, causes the system 100 to perform various functions described herein under.
[024] FIG. 2 illustrates an exemplary functional block diagram of the system 100 of FIG.1, according to some embodiments of the present disclosure. The system 100 receives one or more textual information available with an enterprise as a source material. In an embodiment, the source material corresponds to a user manual, knowledge guides, textbooks, regulations. In an embodiment, the at least one textual information corresponds to a PDF, excel files from which the textual content is extracted. The one or more sentences are obtained from the one or more text information using a sentence boundary identification. At least one triple is extracted from the pseudo dictionary by implementing an open information extraction. The at least one triple correspond to a subject-verb-object. For example, from each sentence, the at least one triple is obtained via an open information extraction. In an embodiment, the open information extraction (open IE) model is a machine learning model trained on one or more relation extractors output and therefore much more capable in terms of obtaining high accuracy triples from any text. A one or more top ‘n’ keywords is determined by implementing a text ranking on the one or more textual information. In an embodiment, the one or more top ‘n’ keywords is determined by a user.

[025] A pseudo dictionary is created for each term in the one or more top ‘n’ keywords. The pseudo dictionary is created based on adding a singular form and a plural form of the one or more top ‘n’ keywords. In an embodiment, every triples that include one or more occurrences of key concepts at these positions reveals a possible concept-relation-concept linkage. The pseudo dictionary is created also based on one or more noun chunks or one or more phrases from the one or more textual information and one or more concepts such that one or more keywords occur within or at end of the at least one phrase. In an embodiment, the one or more noun chunks are considered as mentions and stored in the pseudo dictionary for each keyword in the one or more ‘n’ keywords, whenever there exist one or more noun chunks that contain the keyword. For example, the form -> concept: [mention list], where the keyword is the concept and mentions are one or more noun chunks that contain the keyword. In an embodiment, ranked key terms as the one or more concepts and phrases containing key terms as possible mentions.
[026] The baseline concept model is generated based on the at least one triple. In an embodiment, a specific verb from the at least one triple may correspond to a relation, an attribute, a hierarchy, or combination thereof. For example, (a) Relation as relation- Ramesh walks the dog, (b) Relation as attribute-Ramesh has two legs, (c) Relation as hierarchy- Ramesh is a Human. In an embodiment, the baseline concept model is made up of the one or more concepts connected via one or more relations. The at least one noun chunk is considered as at least one mention and stored in the pseudo dictionary for each keyword in the one or more top ‘n’ keywords, whenever at least one noun chunk comprising the at least one keyword is occurred. In an embodiment, whenever the one or more keywords are found in the subject and the object, then the specific verb in the Subject-Verb-Object (at least one triple) is a candidate to become a relation in the baseline concept model. In an embodiment, the one or more keywords need to be found in the subject and the object of the at least one triple for the verb to be considered as the relation. In alternate embodiment, the verb may represent a relation that is not an attribute or hierarchy, or the relation may represent an attribute, or a hierarchy.

[027] For example, for any keyword in ‘n’ keywords, whenever keyword 1 and keyword 2 are contained in subject and object of a triple from the triples obtained in step 2, then a relation is established such that the keyword 1-verb-keyword 2 or concept 1 (represented by keyword 1) is related by a relation (represented by verb) with concept 2 (represented by keyword 2). The baseline domain model is utilized as, e.g., sense making of a domain of the enterprise in terms of (a) key concepts, (b) model blueprinting where concept models from two different systems from the same domain need to be mapped, (c) summarization of textual content on a basis of key concepts, and (d) as a starting point for building a full-fledged ontology of the domain.
[028] FIG. 3 is an exemplary flow diagram illustrating method 300 for automated generation of the baseline concept model, according to an embodiment of the present disclosure. In an embodiment, the system 100 comprises one or more data storage devices or the memory 104 operatively coupled to the one or more hardware processors 102 and is configured to store instructions for execution of steps of the method by the one or more processors 102. The flow diagram depicted is better understood by way of following explanation/description. The steps of the method of the present disclosure will now be explained with reference to the components of the system as depicted in FIGS. 1 and 2.
[029] At step 302, at least one textual information available with an enterprise is received as a source material. At step 304, determining, via the one or more hardware processors, one or more top ‘n’ keywords is determined by implementing a text ranking on the at least one textual information. At step 306, a pseudo dictionary is created for each keyword in the one or more top ‘n’ keywords. The pseudo dictionary is created by adding a singular form and a plural form of the one or more top ‘n’ keywords. The pseudo dictionary is created by obtaining at least one noun chunk or at least one phrase from the at least one textual information and at least one concept. In an embodiment, at least one keyword occurs within or at end of the at least one phrase. At step 308, at least one triple is extracted from the pseudo dictionary by implementing an open information extraction. The at least one triple correspond to a subject-verb-object. At step 310, the baseline concept model

is generated based on the at least one triple. In an embodiment, the specific verb may correspond to the relation, the attribute, the hierarchy, or the combination thereof. The at least one noun chunk is considered as at least one mention and stored in the pseudo dictionary for each keyword in the one or more top ‘n’ keywords, whenever at least one noun chunk comprising the at least one keyword is occurred.
[030] FIG. 4 is an exemplary line diagram illustrates the automated generation of the baseline concept model, according to an embodiment of the present disclosure. For example, the top ‘n’ keywords are obtained by a text ranking from a know your customer (KYC) are at least one of: a customer, a transaction, a bank, a transfer, an individual, a trust. Using the at least one triple extracted from one or more sentences and upon finding the keywords in subject and object the corresponding relation is added to the baseline model. For example, from the sentence ‘Individual customers have accounts at the specified bank’, a relation such as customer->have->bank and customer->have->account may be obtained.
[031] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[032] The embodiment of present disclosure herein addresses unresolved problem of arriving at a concept model without the domain experts’ input. The embodiment of the present disclosure enables generating the baseline domain model without involvement of the domain expert’s using any text pertaining to a problem. The embodiment of present disclosure in which the claimed approach is generic and scalable and that can be applied to any textual information resources of any size. The embodiment of present disclosure utilizes the text ranking approach and without using a seed entity to represent one or more significant terms. The baseline domain model generated using provides a bird’s eye view of one or more core concepts and mentions using a natural language processing.

[033] The domain expert can consider the baseline domain model and extend as required instead of starting from a clean slate or having to submit one or more seed concepts. Automated and configurable generation of the baseline domain model ensures domain modeling activity. Typically, the domain modeling activity is generally time consuming, however, the embodiment of the present disclosure ensures domain modeling activity faster using the generated baseline model. The embodiment of the present disclosure uses a text ranking technique so that the generated baseline domain model is statistically to reveal core concepts and mentions as opposed to expert-driven domain modeling.
[034] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g., hardware means like e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g., using a plurality of CPUs.
[035] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can

comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[036] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular form “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[037] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

[038] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.

We Claim:
1. A processor implemented method (300), comprising:
receiving, via one or more hardware processors, at least one textual information available with an enterprise as a source material (302);
determining, via the one or more hardware processors, a plurality of top ‘n’ keywords by implementing a text ranking on the at least one textual information (304);
creating, via the one or more hardware processors, a pseudo dictionary for each keyword in the plurality of top ‘n’ keywords (306);
extracting, via the one or more hardware processors, at least one triple from the pseudo dictionary by implementing an open information extraction, and wherein the at least one triple corresponds to a subject-verb-object (308); and
generating, via the one or more hardware processors, a baseline concept model based on the at least one triple, and wherein a specific verb from the at least one triple corresponds to a relation, an attribute, a hierarchy, or combination thereof (310).
2. The processor implemented method (300) as claimed in claim 1, wherein the pseudo dictionary is created by adding a singular form and a plural form of the plurality of top ‘n’ keywords.
3. The processor implemented method (300) as claimed in claim 1, wherein the pseudo dictionary is created by obtaining at least one noun chunk or at least one phrase from the at least one textual information and at least one concept, and wherein at least one keyword occurs within or at end of the at least one phrase.
4. The processor implemented method (300) as claimed in claim 1, wherein the at least one noun chunk is considered as at least one mention and stored in the pseudo dictionary for each keyword in the plurality of top ‘n’

keywords, whenever at least one noun chunk comprising the at least one keyword is occurred.
5. The processor implemented method (300) as claimed in claim 1, wherein the at least one keyword or the at least one phrase that occur in a subject position and an object position to obtain at least one verb as at least one possible candidate for a relation.
6. A system (100), comprising:
a memory (104) storing instructions; one or more communication interfaces (106); and
one or more hardware processors (102) coupled to the memory (104) via the one or more communication interfaces (106), wherein the one or more hardware processors (102) are configured by the instructions to:
receive, at least one textual information available with an enterprise as a source material;
determine, a plurality of top ‘n’ keywords by implementing a text ranking on the at least one textual information;
create, a pseudo dictionary for each keyword in the plurality of top ‘n’ keywords;
extract, at least one triple from the pseudo dictionary by implementing an open information extraction, wherein the at least one triple corresponds to subject-verb-object; and
generate, a baseline concept model based on the at least one triple, wherein a specific verb from the at least one triple corresponds to a relation, an attribute, a hierarchy, or combination thereof.
7. The system (100) as claimed in claim 6, wherein the pseudo dictionary is
created by adding a singular form and a plural form of the plurality of top
‘n’ keywords.

8. The system (100) as claimed in claim 6, wherein the pseudo dictionary is created by obtaining at least one noun chunk or at least one phrase from the at least one textual information and at least one concept, and wherein at least one keyword occurs within or at end of the at least one phrase.
9. The system (100) as claimed in claim 6, wherein the at least one noun chunk is considered as at least one mention and stored in the pseudo dictionary for each keyword in the plurality of top ‘n’ keywords, whenever at least one noun chunk comprising the at least one keyword is occurred.
10. The system (100) as claimed in claim 6, wherein the at least one keyword or the at least one phrase that occur in a subject position and an object position to obtain at least one verb as at least one possible candidate for a relation.