AU2021104731A4 - Business Aligned Knowledge Management System from Unstructured data using Convolutional Neural Network - Google Patents
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Abstract
Business Aligned Knowledge Management System from Unstructured data
using Convolutional Neural Network
ABSTRACT
The present invention relates to a system and method for business aligned knowledge
management system from unstructured data using convolutional neural network to
solve business problems in an organization using convolutional neural network for
achieving business objectives. The objective of present invention is to solve the
anomalies presented in the prior art techniques and using advanced technique for
providing business aligned knowledge from unstructured data to solve problem
statements. The disclosure presents a business aligned knowledge management
system which comprises a central server which is backed by two main components
BAKMS extraction engine and BAKMS execution engine that are based on
convolutional neural network models which are competent enough to learn
themselves and provides an automatic and self-learned system for providing business
aligned knowledge. The proposed invention comprises a server storing the data
related to the business of an organization, test cases and past data to train
convolutional neural network model and schema definition. The said data is stored in
the database. The server involved in the present invention is convolutional neural
network (CNN) enabled server. The present invention discloses a computer
implemented method for business aligned knowledge management system from
unstructured data using convolutional neural network, the method comprises:
retrieving the unstructured data from the data source; BAKMS extraction engine
performing the steps of: converting the identified data into structured data based on
predefined schema; analyzing and processing the structured data using basic classifier
based on convolutional neural network to classify the same as business aligned
knowledge; BAKMS execution engine performing the steps of : defining the business
goal and problem statement of business of the organization; identify the type of
knowledge needed for solving problem statement i.e., explicit knowledge or tacit
knowledge; segregate the knowledge into explicit knowledge and tacit knowledge
using advanced classifier which is based on convolutional neural network; identify the
process and tools to solve problem statement; applying the identified type of
knowledge to identified process/tools to solve problem statement.
1
retrieving the unstructured data from the data source (201)
converting the identified data into structured data based on
predefined schema by BAKMS extraction engine (202)
analyzing and processing the structured data using basic classifier to
classify the same as business aligned knowledge (203)
defining the business goal and problem statement of business of the
organization by BAKMS execution engine (204)
4
identify the type of knowledge for solving problem statement i.e.,
explicit knowledge or tacit knowledge by BAKMS execution engine
(205)
segregate the knowledge into explicit knowledge and tacit knowledge
using advanced classifier (206)
identify the process and tools to solve problem statement (207)
applying the identified type of knowledge to identified process/tools to
solve problem statement (208)
Figure 2 - Flow-diagram of the method for business aligned knowledge management system
2
Description
retrieving the unstructured data from the data source (201)
converting the identified data into structured data based on predefined schema by BAKMS extraction engine (202)
analyzing and processing the structured data using basic classifier to classify the same as business aligned knowledge (203)
defining the business goal and problem statement of business of the organization by BAKMS execution engine (204)
4 identify the type of knowledge for solving problem statement i.e., explicit knowledge or tacit knowledge by BAKMS execution engine (205)
segregate the knowledge into explicit knowledge and tacit knowledge using advanced classifier (206)
identify the process and tools to solve problem statement (207)
applying the identified type of knowledge to identified process/tools to solve problem statement (208)
Figure 2 - Flow-diagram of the method for business aligned knowledge management system
Business Aligned Knowledge Management System from Unstructured data using Convolutional Neural Network
[0001] The present invention relates to the technical field of knowledge management system in an organization. The field of the invention is to provide a business aligned knowledge management system form unstructured data in an organization automatically.
[0002] More particularly, this present invention relates to the field of automatically extracting the business aligned knowledge from unstructured data from a data source to solve business problems in an organization using convolutional neural network for achieving business objectives.
[0003] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in-and-of themselves may also be inventions.
[0004] Knowledge is somewhat a familiarity awareness or understanding of something such as facts, skills or objects. knowledge can be acquired in many different ways and from many sources, including but not limited to perception, reason, memory, testimony, scientific enquiry, education and practice. The term knowledge is a very wide term and meaning of which can be understood as theoretical or practical understanding of any subject. The knowledge can be of explicit knowledge or implicit knowledge. Knowledge plays an important role in problem solving strategies for running or growing an organization. Knowledge plays a vital role and getting or acquiring the right knowledge for a business organization is an important task which we called as knowledge management. Knowledge management enables individuals to stimulate innovation and the cultural changes needed to evolve the organization and meet changing business needs. With faster access to information and resources across the organization, knowledge workers can act quickly. Knowledge management in an organization is necessary to speed up access to business needed information, improvise decision making process, promote innovation and cultural change, improves the efficiency of an organization for meeting the business goals and customer satisfaction. Now, getting the suitable knowledge from the available data sources is one of an important task needed for business management. Knowledge can be available from any kind of database as an unstructured data and structured data. Further, knowledge itself is also of two types i.e., explicit knowledge and tacit knowledge. Hence, extracting the suitable/right knowledge from the available data sources is one of the important task in an organization for solving business problem and achieving business goals. Now, acquiring or extracting the knowledge from the unstructured data is very complex task and applying the said data business problems statement is also an important task. Thus, automation of extraction of such kind of business aligned knowledge plays a key role in an organization.
[0005] Now, with the advancement in technology, knowledge management system is also evolved while using technology. Since, the technology has been evolved or advent, improving the efficiency of knowledge management system for business aligned knowledge is also an important task and need to be performed automatically in a better way. Knowledge management system comprises whole process of extracting, managing and disseminating information and knowledge. Knowledge management system specifies practices tools and process for managing business knowledge. Knowledge management system involves the process and mechanism of knowledge management for creating a form of repository that an organization tap upon from time to time for achieving business goals and solving problem statement in an organization. Thus, there is a need of the process for extracting the knowledge from unstructured data automatically using advanced technology. One such kind of technology for providing automation to any kind of technical filed is convolutional neural network.
[0006] Convolutional neural network is a kind of artificial intelligence or machine intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. As the use of machine learning models like convolutional neural network are increasing in every field for improving the effectiveness and correctness of the work to be done. Convolutional neural network is a kind of machine learning model which takes raw data as input, give weightage to various kinds of data and be able to differentiate it from other. The pre-processing required in convolutional neural network is much lower as compared to other traditional models. The architecture of a Convolutional neural network is analogous to that of the connectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex. Individual neurons respond to stimuli only in a restricted region of the visual field known as the Receptive Field. A collection of such fields overlaps to cover the entire visual area. A convolutional neural network uses various kinds of classifiers to classify the data available.
[0007] Hence, the use of convolutional neural network makes any technology related system automated and more efficient. Thus, developing a business aligned knowledge management system from available unstructured data using convolutional neural network makes system more efficient and valuable in an organization. Hence, there is a need of such a system that can automate the process of extracting business aligned knowledge from unstructured data. There is various prior art that aim to resolve the said issue which are discussed below:
[0008] US20020169737 Al - A knowledge management systems, and more particularly, to a computer-based knowledge management system. In the present description, a knowledge management is described which provides a visual representation of an organization wherein the individuals in the organization are linked to one another through knowledge artefacts. By use of the knowledge artefacts to link individuals, the knowledge management system of the present invention provides for an improved method for visualization of the knowledge exchanges that take place in an organization.
[0009] US6604114 BI - A system for processing a user query for information and organizes, analyzes and presents in a graphic representation, the relevant data for the user, allowing the user to immediately and intrinsically infer the existence of relationships and trends that would normally not have been apparent otherwise. This method supports decision making to an improved level and is capable of presenting data relationships across multiple planes and accessing dissimilar data sets. The ability to then access the underlying data.
[0010] US7127440 B2 - A method for establishing a community of practice including a plurality of users, one or more experts, and one or more community of practice managers. A need for a community of practice is identified. The roles and responsibilities of participants in the community of practice are identified. One or more goals are identified for the community of practice based on the identified need. A plurality of the participants in the community of practice collaborate to achieve the identified goals.
[0011] CN101281622 A - A knowledge management system and a method for implementing management software using the KMS are provided. In the present method, a network configuration module is used for obtaining a domain name of a network connecting the knowledge management system and network configuration data of a plurality of peripheral hosts in the network so as to connect the management host with the peripheral hosts. A software licensing module is then used for obtaining the software licensing data of a plurality of application programs required by the management software to verify the legitimacy of the application programs. A software configuration module is used for obtaining configuration data of the application programs to enable the management software to make use of the resource of the application programs.; Therefore, the implementation process of the management software is simplified and the flexibility for modifying and updating configuration parameters is enhanced.
[0012] W02002017197 A9 - A knowledge management system including a user configurable portal page for a web browser or the like; said page comprising. (a) an assembly zone for assembly by said user of user-selected links; (b) a selection zone comprising a plurality of link zones; each link zone providing access to a plurality of links which each offer access to a predetermined grouping of knowledge-providing links.
[0013] US6820071 BI - A knowledge management system includes clients in communication with a server. The server includes a knowledge matrix that includes a knowledge worker grid, a process grid, a process cycle grid, a data grid, and a data cycle grid. The knowledge matrix identifies process items and data items associated with a knowledge worker operating the client. The server accesses status information stored in the knowledge matrix relating to the identified process items and data items.
[0014] CN104200324 A - A business knowledge management-based configuration management method. The business knowledge management based configuration management method comprises the steps of: defining business requirements, a business process and a business model through data information stored in a knowledge base module, filtering, analyzing and matching through a rule engine to obtain business data, and uploading to obtain a business configuration item; inputting the business configuration item and an IT resource configuration item into a relational database module to obtain a relationship between the business configuration item and the IT resource configuration item; modeling based on the business data, the business configuration item and the IT resource configuration item to obtain the business model of the knowledge base module; analyzing and calling the business configuration item and the business model of the knowledge base module through the rule engine, and combining with the execution of an IT business model and a resource configuration item of a configuration management database module to obtain a configuration data management system of the business management and system running data. The business knowledge management-based configuration management method improves availability and stability of an IT system.
[0015] US6484155 B1 - A knowledge management system that supports inquiries of distributed knowledge resources. Those inquiries may be in the form of questions or problem statements presented by a user. Interaction between a user and the knowledge resources is mediated by a collection of cooperative intelligent agents. The cooperative intelligent agents incorporate generalized automated negotiation and distributed inference (i.e., problem solving) processes. Using those processes in a hierarchical architecture, the invention analyzes input problem statements and organizes the problem statements as sets of tasks. In pursuit of each task, the invention solicits accessible knowledge repositories, represented by knowledge agents, for relevant knowledge, and then analyzes and integrates responses from those knowledge repositories. The invention may then provide the responses to a human user or a using process.
[0016] US7865457 B2 - A knowledge management system allocating expert resources, method of allocating expert resources and program product therefor. Information requests are provided over networked devices, e.g., over voice and data networks. Data on experts may be stored in an expert database and data on requesters stored in a requestor database. A pairing unit identifies an appropriate expert matched to each request and requestor. The pairing unit includes a request/requestor characterization unit collecting request/requestor attributes, a matching unit matching request/requestor attributes with an expert, and a routing unit routing each incoming request to a selected matching expert.
[0017] Hence, there are various prior art that aims to develop a knowledge management system in various way. The objective of all these knowledge management systems is to develop more efficient system with the use of the technology. The aim here to present this invention is to develop more advanced system with the current technology to make more efficient and automatic system. Further, since, the knowledge is not readily available in ready-to-use form, there is a need to extract the knowledge from any data source useful for a business in an organization. Hence, business aligned knowledge management system form unstructured data using convolutional neural network is the requirement in today's business organization.
[0018] Besides this, there are various prior arts in the state of the art that claims to resolve the problem of providing knowledge management system for an organization but the approach adopted for solving the same need to be further refined. Hence, there is a need to provide an automatic business aligned knowledge management system for with the use of convolutional neural network that aims to extracts the knowledge from the available data source or from unstructured data useful for achieving business goals. The aim of the present invention is to use convolutional neural network that makes less intervention and involvement of the human resources. The use of convolutional neural network provides more advanced system for providing business aligned knowledge management system from unstructured data in an organization.
[0019] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markus groups used in the appended claims.
[0020] As used in the description herein and throughout the claims that follow, the meaning of "a," "an," and "the" includes plural reference unless the context clearly dictate otherwise. Also, as used in the description herein, the meaning of "in" includes "in" and "on" unless the context clearly dictates otherwise.
[0021] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context.
[0022] The use of any and all examples, or exemplary language (e.g. "such as") provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
[0023] The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art.
[0024] Before the present systems and methods, are described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only and is not intended to limit the scope of the present application. This summary is provided to introduce concepts related to business aligned knowledge management system using convolutional neural network and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[0025] The present invention mainly solves the technical problems existing in the prior art. In response to these problems, the present invention discloses an business aligned knowledge management system using convolutional neural network which extract business aligned knowledge from unstructured data automatically from the available data source using the methodology presented here. The given solution provides the complete and automatic business aligned knowledge management system that efficiently the available data source to collect/extract business aligned knowledge through convolutional neural network comprising various classifier to classify or filter the data according to the defined business goals and problem statement of the business. Here, the business aligned knowledge management system is abbreviated as BAKMS system for ease of understanding. The proposed invention comprises BAKMS extraction engine, and BAKMS execution engine that are based on convolutional neural network which are competent enough to learn themselves and provides an automatic and self-learned system for providing business aligned knowledge management system.
[0026] The proposed invention comprises a server storing the data related to the business of an organization in an unstructured format from any data source. The said data is stored in the database storing data related to the business, product and reports. The server involved in the present invention is convolutional neural network (CNN) enabled server. The proposed invention is based on convolutional neural network which comprised of two classifier namely basic classifier and advanced classifier. The first one work at the time of extracting the knowledge or information from the unstructured data and the second one works at the time of using the same knowledge for solving business problem statement. The proposed invention works in two phases. The first phase comprised of BAKMS extraction engine which extracts or captures the business-related knowledge or information from the unstructured data. The unstructured data from any data source is first converted into structured data by defining the schema of the business. The basic classifier based on convolutional neural network works in this first phase. The basic classifier extracts all the relevant information related to the business and their products. The said classifier is trained using the test cases and past data related to the business aligned knowledge. Thereafter, the second phase of the proposed invention works. The second phase of the proposed invention is performed by the BAKMS execution engine. The BAKMS execution engine first determine/identify the business goal and problem statement of the organization. Then the BAKMS engine identity the type of knowledge to address the problem statement i.e., explicit knowledge or tacit knowledge. In this phase, the advanced classifier based on convolutional neural network becomes active and identify the type of knowledge and segregate the explicit knowledge and tacit knowledge from the output of the basic classifier. The advanced classifier is also trained using the test cases and past data related to business having both kind of knowledge. The BAKMS engine then determine the tools and process needed for applying the identified knowledge to solve the problem statement. In this way, the whole process is completely automatic and identify the business aligned knowledge form the unstructured data from the data source automatically using classifiers based on convolutional neural networks. The proposed process or system automatically identify the relevant knowledge form the unstructured data and apply the said data to problem statement with the identified tools and process to solve the problem statement and fulfils the business goal of an organization.
[0027] The present invention comprises a central server of the business organization which is based on the convolutional neural network and backed by the database related to the unstructured data from the data source, test cases and past data related to explicit knowledge and tacit knowledge related to business and their products of an organization. The convolutional neural network is intelligent enough and is automatic machine learned using the data available on the central server. The BAKMS extraction engine and BAKMS execution engine may be implemented in the form of but not limited to hardware component, software modules, program modules, computer instructions or the like. The component of present BAKMS system i.e., BAKMS extraction engine and BAKMS execution engine both resides on the central server of the business organization. The business goals and problem statement of the business of an organization is defined by the administration or executives of an organization. The central server is also equipped with the schema of the business-related organization and the server is also competent enough to identity the suitable schema in the given scenario.
[0028] An aspect of the present disclosure relates to a computer implemented method for business aligned knowledge management system from unstructured data using convolutional neural network, the method comprises: retrieving the unstructured data from the data source; BAKMS extraction engine performing the steps of: converting the identified data into structured data based on predefined schema; analyzing and processing the structured data using basic classifier based on convolutional neural network to classify the same as business aligned knowledge; BAKMS execution engine performing the steps of defining the business goal and problem statement of business of the organization; identify the type of knowledge needed for solving problem statement i.e., explicit knowledge or tacit knowledge; segregate the knowledge into explicit knowledge and tacit knowledge using advanced classifier which is based on convolutional neural network; identify the process and tools to solve problem statement; applying the identified type of knowledge to identified process/tools to solve problem statement.
[0029] Another aspect of the present disclosure relates to a computer implemented system for business aligned knowledge management from unstructured data using convolutional neural network, the method comprises: retrieving the unstructured data from the data source; BAKMS extraction engine performing the steps of: converting the identified data into structured data based on predefined schema; analyzing and processing the structured data using basic classifier based on convolutional neural network to classify the same as business aligned knowledge; BAKMS execution engine performing the steps of : defining the business goal and problem statement of business of the organization; identify the type of knowledge needed for solving problem statement i.e., explicit knowledge or tacit knowledge; segregate the knowledge into explicit knowledge and tacit knowledge using advanced classifier which is based on convolutional neural network; identify the process and tools to solve problem statement; applying the identified type of knowledge to identified process/tools to solve problem statement.
[0030] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
[0031] A primary object of the present invention is to provide a method for business aligned knowledge management from unstructured data using convolutional neural network related to the business of the organization.
[0032] Yet another object of the present invention is to provide a system for business aligned knowledge management from unstructured data using convolutional neural network related to the business of the organization. The said system provides complete and automatic knowledge management system for an organization from identifying the knowledge and solving the problem statement using identified knowledge from unstructured data using convolutional neural network-based classifiers.
[0033] To clarify various aspects of some example embodiments of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It is appreciated that these drawings depict only illustrated embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail through the use of the accompanying drawings.
[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments belong. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing, suitable methods and materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.
[0035] In order that the advantages of the present invention will be easily understood, a detail description of the invention is discussed below in conjunction with the appended drawings, which, however, should not be considered to limit the scope of the invention to the accompanying drawings, in which:
[0036] Figure 1 shows block-diagram of the business aligned knowledge management system incorporating all the embodiment of the system of the present invention.
[0037] Figure 2 shows a flow-diagram of computer implemented method for business aligned knowledge management system using convolutional neural network in accordance with the present invention.
[0038] The present invention relates to a computer implemented method for business aligned knowledge management system from unstructured data using convolutional neural network related to the business of an organization.
[0039] Although the present disclosure has been described with the purpose of business aligned knowledge management system from unstructured data using convolutional neural network, it should be appreciated that the same has been done merely to illustrate the invention in an exemplary manner and to highlight any other purpose or function for which explained structures or configurations could be used and is covered within the scope of the present disclosure.
[0040] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words and other forms thereof are intended to 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 forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary systems and methods are now described. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.
[0041] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.
[0042] Figure 1 show a block-diagram of the business aligned knowledge management system from unstructured data using convolutional neural network as per the embodiments of the present invention. According to the present invention, the said system comprises a central server (103) having the computation facility wherein the central server is equipped with convolutional neural network model. The server machine (103) is backed with the database (102) comprising of data related to the products and the business of an organization, test cases and past data used to train the convolutional neural network, and schema definition. The communication network (101) is responsible for transmitting and receiving various data across various embodiments of the present invention. The communication network involved in the present invention may be but not limited to Wide area Network (WAN), local area Network (LAN), WiFi, Bluetooth or the combination thereof. Further, there are n number of computing devices (108) used to perform or provide input to the system. The central server is equipped with two major component which makes the said system automatic and machine learned. The BAKMS extraction engine (104) which is responsible for extracting the knowledge related to the business of an organization using the basic classifier (105) based on convolutional neural network layer. Further, the second component, BAKMS execution engine further identify the business goals and problem statement of the organization, type of knowledge needed, process and tools needed to solve problem statement and applying the identified knowledge with identified tools an processes to achieve business goals.
[0043] Figure 2 shows the flow-diagram of computer implemented method for business aligned knowledge management system from unstructured data using convolutional neural network model. The computer implemented method first retrieves the unstructured data from the data source at step 201; BAKMS extraction engine (104) performing the steps of: converting the identified data into structured data based on predefined schema (202); analyzing and processing the structured data using basic classifier (105) based on convolutional neural network to classify the same as business aligned knowledge at step 203; BAKMS execution engine (106) performing the steps of : defining the business goal and problem statement of business of the organization at step 204; identify the type of knowledge needed for solving problem statement i.e., explicit knowledge or tacit knowledge at step 205; segregate the knowledge into explicit knowledge and tacit knowledge using advanced classifier (107) which is based on convolutional neural network at step 206; identify the process and tools to solve problem statement at step 207; applying the identified type of knowledge to identified process/tools to solve problem statement (at step 208).
[0044] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.
[0045] Although implementations for invention have been described in a language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for the invention.
Claims (5)
1. A computer implemented method for business aligned knowledge management system from unstructured data using convolutional neural network, the computer implemented method performed by the computing device(s) having a processor, input/output device and a memory comprising steps of: retrieving the unstructured data from the data source (201); BAKMS extraction engine (104) performing the steps of: converting the identified data into structured data based on predefined schema (202); analyzing and processing the structured data using basic classifier (105) based on convolutional neural network to classify the same as business aligned knowledge (203); BAKMS execution engine (106) performing the steps of: defining the business goal and problem statement of business of the organization (204); identify the type of knowledge needed for solving problem statement i.e., explicit knowledge or tacit knowledge (205); segregate the knowledge into explicit knowledge and tacit knowledge using advanced classifier (107) which is based on convolutional neural network (206); identify the process and tools to solve problem statement (207); applying the identified type of knowledge to identified process/tools to solve problem statement (208).
2. The computer implemented method as claimed in claim 1, wherein the data is retrieved from any kind of data source related to the business of an organization.
3. The computer implemented method as claimed in claim 1, wherein the communication network may be based on the WiFi, Bluetooth, Local Area Network, Wide Area Network or the combination thereof.
4. The computer implemented method as claimed in claim 1, wherein basic classifier (105) and advanced classifier (107) are based on convolutional neural network (CNN).
5. A system for business aligned knowledge management system from unstructured data using convolutional neural network, the system is performed by the computing device(s) (108) having a processor, input/output device and a memory, the system comprising: a communication network (101) to transmit/receive data from other embodiments of the system; database (102) to store data related to the business of the organization, test cases along with past data and schema definition; server (103) for performing function of BAKMS extraction engine (104) and BAKMS execution engine (106) for performing the steps of: retrieving the unstructured data from the data source (201); BAKMS extraction engine (104) performing the steps of: converting the identified data into structured data based on predefined schema (202); analyzing and processing the structured data using basic classifier (105) based on convolutional neural network to classify the same as business aligned knowledge (203); BAKMS execution engine (106) performing the steps of: defining the business goal and problem statement of business of the organization (204); identify the type of knowledge needed for solving problem statement i.e., explicit knowledge or tacit knowledge (205); segregate the knowledge into explicit knowledge and tacit knowledge using advanced classifier (107) which is based on convolutional neural network (206); identify the process and tools to solve problem statement (207); applying the identified type of knowledge to identified process/tools to solve problem statement (208).
Database (102) 2021104731
Computing device(s) (108)
Central server (103) Communication network (101)
BAKMS BAKMS execution extraction engine (106) engine (104) Advanced classifier (107)
Basic classifier (105)
Figure 1: Block diagram of business aligned knowledge management system
retrieving the unstructured data from the data source (201) 2021104731
converting the identified data into structured data based on predefined schema by BAKMS extraction engine (202)
analyzing and processing the structured data using basic classifier to classify the same as business aligned knowledge (203)
defining the business goal and problem statement of business of the organization by BAKMS execution engine (204)
identify the type of knowledge for solving problem statement i.e., explicit knowledge or tacit knowledge by BAKMS execution engine (205)
segregate the knowledge into explicit knowledge and tacit knowledge using advanced classifier (206)
identify the process and tools to solve problem statement (207)
applying the identified type of knowledge to identified process/tools to solve problem statement (208)
Figure 2 – Flow-diagram of the method for business aligned knowledge management system
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114282011A (en) * | 2022-03-01 | 2022-04-05 | 支付宝(杭州)信息技术有限公司 | Knowledge graph construction method and device, and graph calculation method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114282011A (en) * | 2022-03-01 | 2022-04-05 | 支付宝(杭州)信息技术有限公司 | Knowledge graph construction method and device, and graph calculation method and device |
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