Patent Application
20220092352
The present disclosure relates to business process models, and more specifically, to label generation for an element of a business process model.
In practice, business processes can be described in various representations for different users. For example, textual process descriptions, such as memos, manuals and requirement descriptions, are well-suited for non-technical users, while business process modeling using languages such as Event-driven Process Chain (EPC) and Business Process Modeling and Notation (BPMN) are usually used for technical users. Business process models are representations or illustrations of an organization's business processes, which are critical for effective business process management. For example, modeling business processes can help to better understand the processes and to identify and prevent problems. In large-scale organizations, different representations of business process are usually kept in parallel to make the processes understandable by all users. However, due to the evolving nature of business process management, it is often necessary to transform between different representations.
Multiple challenges arise during transformation between different representations. For example, a textual process description is usually not limited to a predetermined format, that is, it may include one or more paragraphs each including one or more sentences. By contrast, a business process model typically may include a sequence of elements, such as an activity or an event, and each of the elements may be tagged with a label, which is a short natural language text describing the element. Thus, when generating a business process model automatically out of a textual process description, it is necessary to generate a label for an element of the business process model based on the corresponding text segment in the textual process description.
Disclosed herein are embodiments of a method, system and computer program product for generating a label for an element of a business process model.
According to an embodiment of the present invention, a computer-implemented method is provided. The method comprises obtaining at least one portion of a text segment that describes an element of a business process model. The method further comprises applying a question-answering (QA) machine learning model to the at least one portion of the text segment to obtain a set of answers to a set of predetermined questions. The method further comprises generating a label for the element by combining the set of answers according to a format associated with the set of predetermined questions.
According to another embodiment of the present invention, a computing system is provided. The computing system comprises a processor and a computer-readable memory unit coupled to the processor. The memory unit comprising instructions that, when executed by the processor, perform actions of obtaining at least one portion of a text segment that describes an element of a business process model. The memory unit further comprising instructions that, when executed by the processor, perform actions of applying a question-answering (QA) machine learning model to the at least one portion of the text segment to obtain a set of answers to a set of predetermined questions. The memory unit further comprising instructions that, when executed by the processor, perform actions of generating a label for the element by combining the set of answers according to a format associated with the set of predetermined questions.
According to further embodiment of the present invention, a computer program product is provided. The computer program product comprises a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the computer to perform actions of obtaining at least one portion of a text segment that describes an element of a business process model. The program instructions are executable by a computer to further cause the computer to perform actions of applying a question-answering (QA) machine learning model to the at least one portion of the text segment to obtain a set of answers to a set of predetermined questions. The program instructions are executable by a computer to further cause the computer to perform actions of generating a label for the element by combining the set of answers according to a format associated with the set of predetermined questions.
Through the more detailed description of some embodiments of the present disclosure in the accompanying drawings, the above and other objects, features and advantages of the present disclosure will become more apparent, wherein the same reference generally refers to the same components in the embodiments of the present disclosure.
Some embodiments will be described in more detail with reference to the accompanying drawings, in which the embodiments of the present disclosure have been illustrated. However, the present disclosure can be implemented in various manners, and thus should not be construed to be limited to the embodiments disclosed herein.
It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.
Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.
Referring now to
In cloud computing node 10 there is a computer system/server 12 or a portable electronic device such as a communication device, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server 12 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.
Computer system/server 12 may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server 12 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
As shown in
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer system/server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server 12, and it includes both volatile and non-volatile media, removable and non-removable media.
System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32. Computer system/server 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus 18 by one or more data media interfaces. As will be further depicted and described below, memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
Program/utility 40, having a set (at least one) of program modules 42, may be stored in memory 28 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments of the invention as described herein.
Computer system/server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer system/server 12; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 12 to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces 22. Still yet, computer system/server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 20. As depicted, network adapter 20 communicates with the other components of computer system/server 12 via bus 18. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server 12. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.
Referring now to
Referring now to
Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; RISC (Reduced Instruction Set Computer) architecture based servers 62; servers 63; blade servers 64; storage devices 65; and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.
Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and label generating 96.
As mentioned above, when generating a business process model automatically out of a textual process description, it is necessary to generate a label for an element of the business process model based on the corresponding text segment in the textual process description. However, when the business process model is displayed in a graphical representation (such as a flow diagram), the maximum length of a label for an element of the business process model displayed in the graphical representation is often limited (e.g., to 20 words), which makes it challenging to describe an element accurately. Moreover, the label for an element of the business process model should retain the key information (e.g., the target system and/or the action) contained in the corresponding text segment and not introduce irrelevant or wrong information.
With reference now to
As shown, the business process model 400 includes elements 410-460. Elements 410 and 460 are events that represent things that happen instantaneously, while elements 420-450 are activities that represent units of work that have a duration. It should be noted that the business process model 400 may include any other kinds of elements, such as gateways (not shown). The start event element 410 is shown as a circle with a thin border and the end event element 460 is shown as a circle with a thick border. The elements 420-450 are represented by rounded rectangles, each with a label on it. The label for element 420 is “Gatekeeper receives email notification”, the label for element 430 is “Gatekeeper reviews support request & attachments”, the label for element 440 is “Gatekeeper validates & uploads pricing” and the label for element 450 is “Gatekeeper removes contract watermark”. Note that elements 410 and 460 may also be given labels (not shown) to show, e.g., when should a new instance of the process be started and what conditions hold when an instance of the process completes, respectively. As discussed above, there may be a maximum length limitation for the labels, which may be, for example, 516-20 words.
Elements 420-450 may be expanded to show more information regarding the elements. For example, after clicking on the element 420, a block 470 will be displayed. The block 470 may show the label “Gatekeeper receives email notification” on the top, as well as multiple tabs showing other information regarding the activity of the element 420, such as details, problems, policies, documentation, attachments and comments. The “Documentation” tab may contain a text segment 480 that describes the element 420. According to an embodiment of the invention, the text segment 480 is taken from a textual process description (such as a memo, a manual, a log, etc.). According to an embodiment of the invention, the business process model 400 was generated automatically out of the textual process description and the label for the element 420 was generated based on the text segment 480.
The text segment 480 is as follows:
Once a proposal has been accepted by the customer, and Sales has a completed contract package, but before the contract or contract addendum is uploaded for customer signature, the Seller will submit an Engagement Support Request (SR) for the addition of new sites to existing contracts. The Gate mailbox will receive a Support Request Email Notification for the Gatekeeper to review.
Although the text segment 480 gives a more detailed description of the element 420, but it is too long and may contain noisy information that is insignificant and irrelevant with the key information of the activity. Conventional Natural Language Processing tools such as generative deep learning models and extractive deep learning models are generally not applicable in generating a label for the element 420 based on the text segment 480. For example, by applying a trained generative deep learning model called “gigaword_nocopy_acc_51.33_ppl_12.74_e20.pt” in the Opennmt-py script to the text segment 480, a label generated for the element 420 is “SR will submit SR for”, which is not grammatically correct and does not capture the key information of the activity. As another example, the label generated by extractive deep learning models (such as, Opennmt-giga, Opennmt-transformer and Textrank) may be a complete sentence in the text segment 480, which may exceed the maximum length limitation, or the extractive deep learning models may even fail to generate a label.
Now, with reference to
With reference now to
As shown at 510, the method 500 may include obtaining at least one portion of a text segment that describes an element of a business process model. For example, the text segment may contain one or more semantic components (such as one or more sentences, or one or more paragraphs) in a textual process description. According to an embodiment of the invention, obtaining the at least one portion of the text segment may comprise at least one of: removing one or more sentences in the text segment that are at one or more lower levels of a hierarchy structure of the text segment; and removing one or more sentences in the text segment that contain predefined keywords (such as “tool” and “note”). The hierarchy structure may be extracted based on layout information and/or semantic information of the text segment. The one or more sentences that are at one or more lower levels of the hierarchy structure of the text segment and/or the one or more sentences that contain predefined keywords may contain noisy information. Therefore, by removing the one or more sentences from the text segment, useful and important information will remain, which may facilitate the generation of a more accurate label. The pre-processing of the text segment will be described below with respect to
As shown at 520, a question-answering (QA) machine learning model may be applied to the at least one portion of the text segment to obtain a set of answers to a set of predetermined questions. Note that any appropriate QA machine learning model that takes a passage of text and a corresponding question as inputs and gives an answer as output may be used here. According to an embodiment of the invention, the QA machine learning model may have been trained using a training dataset comprising pairs of inputs and corresponding outputs. Each of the output may be a portion of a label of an element of a business process model, and each of the input may be extracted from a text segment that describes the element by at least one of: removing one or more sentences in the text segment that are at one or more lower levels of a hierarchy structure of the text segment; and removing one or more sentences in the text segment that contain predefined keywords. Thereby, when building a training dataset, the input may also be pre-processed to remove noisy information, which may improve the performance of the machine learning model. The structure of the machine learning model will be described below with respect to
According to an embodiment of the invention, each of the set of answers may be a span of texts selected from the at least one portion of the text segment. According to an embodiment of the invention, the QA machine learning model may be applied to the at least one portion of the text segment to obtain a first answer to a first question of the set of predetermined questions, and then the QA machine learning model may be applied to the at least one portion of the text segment to obtain a second answer to a second question of the set of predetermined questions, wherein a component of the second question is based on the first answer. According to another embodiment of the invention, the application of the QA machine learning model to the at least one portion of the text segment may be performed in parallel for at least some of the set of questions. By leveraging a QA machine learning model, the method 500 may extract key information of the text segment (such as the subject, the action, and the like).
According to an embodiment of the invention, at least one answer may be obtained for each of the set of predetermined questions, and each of the at least one answer may have a score that indicates a probability that the obtained answer is ground truth.
As shown at 530, a label for the element may be generated by combining the set of answers according to a format associated with the set of predetermined questions. According to an embodiment of the invention, the format may be designed such that the combined set of answers conforms to a predetermined labeling style. Thereby, the method 500 may provide greater flexibility in customizing the label. Examples of labeling styles will be described below with respect to
According to an embodiment of the invention, as mentioned above, at least one answer may be obtained for each of the set of predetermined questions, and each of the at least one answer may have a score that indicates a probability that the obtained answer is ground truth. The label for the element may be generated based at least on the scores of the answers. According to an embodiment of the invention, answers to respective ones of the set of predetermined questions may be cascaded according to the format associated with the set of predetermined questions to generate a plurality of candidate labels for the element, and one of the plurality of candidate labels may be selected as the label for the element based at least on the scores of the answers. According to an embodiment of the invention, the plurality of candidate labels may be grouped into one or more clusters by semantic clustering, and the label for the element may be selected from one of the one or more clusters that has the largest number of candidate labels. Semantic clustering of the candidate labels will be described below with respect to
It should be noted that the processing of the method 500 according to embodiments of this disclosure could be implemented by computer system/server 12 of
With reference to
A text segment 610 (such as the text segment 480) that describes an element (such as the element 420 in
The data pre-processing module 620 may obtain at least one portion of the text segment 610. According to an embodiment of the invention, the data pre-processing module 620 may send the text segment 610 to the hierarchy structure extractor 650, and the hierarchy structure extractor 650 may extract a hierarchy structure of the text segment based on layout information and/or semantic information of the text segment. The layout information may include but is not limited to at least one of font type, font size, text color, text indent, associated number, etc. The semantic information may be analyzed using existing tools, such as unsupervised methods to recognizing discourse relations, or discourse segmentation methods (such as TextTiling). By referring to
1. Launch a Session.//Sentence 702
1.1 Log in system A.//Sentence 704
1.2 Log in http://www.website1.com/ with ID and password. //Sentence 706
(Please ensure that the SOCKS client is enabled). //Sentence 708
Note: To register a new user ID, please click http://www.website2.com/. //Sentence 710
1.3 After connecting to the server, please install SOCKS Client to launch the session.
//Sentence 712
Link: (http://www.website3.com/). //Sentence 714
It should be noted that the text shown above after “//” in each line is an identifier of the sentence in that line, rather than a part of the sentence itself. The corresponding hierarchy structure 700 may comprise three levels, wherein sentence 702 is at the first/top level, sentences 704, 706 and 712 are at the second/middle level and sentences 708, 710 and 714 are at the third/lowest level. Note that, if Sentence A is at a level higher than Sentence B, it is more likely that Sentence A relates to the key information of the text segment. The hierarchy structure 700 may be generated by the hierarchy structure extractor 650 based on layout information and/or semantic information of the text segment.
According to an embodiment of the invention, the hierarchy structure extractor 650 may analyze the layout information of the text segment T1. According to an embodiment of the invention, the hierarchy structure extractor 650 may identify that sentence 702 is started with a number “1” and sentences 704, 706 and 712 are started with “1.1”, “1.2” and “1.3”, respectively. In addition, sentences 708, 710 and 714 are indented by three spaces. Based on the associated numbers and the text indent, the hierarchy structure extractor 650 may determine that sentences 708, 710 and 714 are at a lower level than sentences 704, 706 and 712 and sentence 702 is at a higher level than sentences 704, 706 and 712, and thereby derive the hierarchy structure 700. According to an embodiment of the invention, the text segment may include other layout information in addition to or instead of the associated numbers and the text indent. For example, sentence 702 may be represented in 12-point, Arial, boldface type, sentences 704, 706 and 712 may be represented in 11-point, Times New Roman font, and sentences 708, 710 and 714 may be represented in 10-point, Times New Roman, italic type. In such cases, the hierarchy structure extractor 650 may extract the hierarchy structure 700 based at least on the font information.
According to an embodiment of the invention, the hierarchy structure extractor 650 may analyze the semantic information of the above text segment T1 using existing tools, such as unsupervised methods to recognizing discourse relations and discourse segmentation methods (such as TextTiling), to extract the hierarchy structure 700. Semantic analysis may be useful particularly when the layout information is insufficient, for example, when the text segment is taken from a file that contains plain text (e.g., a txt document). It should be noted that the hierarchy structure extractor 650 may extract the hierarchy structure 700 in any other appropriate way, e.g., based on a combination of the layout information and semantic information, or based on a hierarchy description, etc.
Now referring back to
According to an embodiment of the invention, the data pre-processing module 620 may remove one or more sentences in the text segment that contain predefined keywords. The predefined keywords may include “note”, “link”, “tool”, and the like. The sentences including such predefined keywords may represent a notice or auxiliary information that is less important and thus can be removed. Taking the text segment T1 as an example, the data pre-processing module 620 may identify that sentence 710 contains the keyword “note” and sentence 714 contains the keyword “link”, and thus remove sentences 710 and 714.
After the data pre-processing module 620 obtains at least one portion of a text segment that describes an element of a business process model, it may provide the at least one portion of the text segment to the label generation module 630. The label generation module 630 may apply a question-answering (QA) machine learning model 640 to the at least one portion of the text segment to obtain a set of answers to a set of predetermined questions. According to an embodiment of the invention, each of the set of answers may be a span of texts selected from the at least one portion of the text segment. The QA machine learning model 640 may be any appropriate machine learning model that takes a passage of text and a corresponding question as inputs and gives an answer (or more than one answer) as output, such as Match-LSTM (Long Short-Term Memory), Attentive Reader, and the like. By referring to
The question may include four question words W1-W4 802-808. For example, the question may be “What is the system”. Each of the question words 802-808 goes through an embedding layer 810 that maps each question word into its word embedding and a BiLSTM (Bidirectional LSTM) layer 820 that extracts context feature of the question to obtain a vector representation of the question, which is denoted by q=(q1, q2, q3, q4).
The pre-processed text segment may include four sentences S1-S4 862-868, and each sentence may include one or more words. For example, the sentence S3 866 may include five words X1-X4 830-838, such as “register a new user id”. In the sentence level encoding stage, each sentence is processed in a similar way as the question. Specifically, each of the words 830-838 goes through an embedding layer 840 that maps the word into its word embedding and a BiLSTM layer 850 that extracts context feature of the sentence S3 866 to obtain a vector representation of the sentence S3 866. The sentences S1 862, S2 864 and S4 868 may be processed similarly to obtain vector representations thereof. Then, in the document level encoding stage, each of the vector representations of sentences S1-S4 862-868 goes through an embedding layer 870 and a BiLSTM layer 880 to obtain a vector representation of the pre-processed text segment p=(p1, p2, . . . , p1), where 1 is the total number of words in the pre-processed text segment. Then, the vector representation of the question q and the vector representation of the pre-processed text segment p (only p1, p2, q1, and q2 are illustrated for simplicity) are processed by a bilinear decoder 892 and a bilinear decoder 894 to obtain a start position 896 and an end position 898 in the pre-processed text segment, respectively. For example, the bilinear decoder 892 and the bilinear decoder 894 may be bilinear classifiers. By extracting a span of texts from the pre-processed text segment based on the start position 896 and the end position 898, an answer to the question may be obtained and provided to the label generation module 630. It will be appreciated that the QA machine learning model 800 may have a structure different from that shown in
Now referring back to
For example, assuming that the pre-processed text segment received from the data pre-processing module 620 is the text segment 480 in
According to an embodiment of the invention, the application of the QA machine learning model 640 to the pre-processed text segment may be performed in parallel for at least some of the set of questions. For example, the first question in the set of predetermined questions may be: “What is the system?” The second question in the set of predetermined questions may be: “What is the operation?” Since the first and second questions do not dependent on each other, obtaining answers to the two questions may be performed in parallel using two instances of the QA machine learning model 640.
According to an embodiment of the invention, the QA machine learning model 640 may have been trained using a training dataset comprising pairs of inputs and corresponding outputs. Each of output is a portion of a label of an element of a business process model, and each of the input is extracted from a text segment that describes the element by at least one of: removing one or more sentences in the text segment that are at one or more lower levels of a hierarchy structure of the text segment; and removing one or more sentences in the text segment that contain predefined keywords. That is to say, the training text segment may be processed in advance by the data pre-processing module 620 to remove noisy information.
Consider using the text segment T1 as described above to build a training dataset. The text segment T1 describes an element of a business process model, and the label for the element may be pre-determined as “Launch a session” (e.g., by a technician) Similar to the process described above with respect to
According to an embodiment of the invention, the format associated with the set of predetermined questions is designed such that the combined set of answers conforms to a predetermined labeling style.
In the second class of labeling styles, the action is captured as a noun. Three different styles 920-940 in
Referring back to
For each answer to the first question, the second question will be determined by replacing the component “***” with the answer to the first question. For example, for the answer “Gate,” the second question is determined as “What will Gate do?” and the label generation module 630 may obtain the following answers from the QA machine learning model 640:
For each of the other answers “Support Request” and “Sales,” the second question may be determined, and at least one answer may be obtained for the second question in a similar way.
According to an embodiment of the invention, the label generation module 630 may cascade answers to respective ones of the set of predetermined questions according to the format associated with the set of predetermined questions to generate a plurality of candidate labels for the element, and one of the plurality of candidate labels may be selected as the label for the element 670 based at least on the scores of the answers. For example, considering the above example where there are two predetermined questions, if three answers are obtained for the first question and four answers are obtained for the second question, a total number of 12 candidate labels may be generated by cascading answers to respective ones of the two questions, and each candidate label may be assigned a score based on the scores of the answers constituting it. The score for a candidate label may be calculated by multiplying the scores of each answer constituting the label, by calculating a weighted sum of the answers constituting the label, etc. Continuing the above example, the label generation module 630 may generate the following three candidate labels among others:
Label 1: “Gate receives a Support Request Email Notification;”
Label 2: “Gate reviews;”
Label 3: “Gate has a completed contract package.”
The score for Label 1 may be calculated as: “0.19373165×0.03707864=0.007183306106956”, the score for Label 2 may be calculated as: “0.19373165×0.01873733=0.0036300138574945”, and the score for Label 3 may be calculated as “0.19373165×0.01585925=0.0030724386702625”. The label generation module 630 may select a label from the candidate labels as the label for the element 670. According to an embodiment of the invention, the label generation module 630 may select the candidate label with the highest score, such as Label 1 in this example. The label generation module 630 may select the label for the element 670 based on one or more other criteria. According to an embodiment of the invention, the label generation module 630 may discard a candidate label that exceeds a predetermined length limit, in order to meet the requirement of a business process modeling language (such as EPC and BPMN). For example, the label generation module 630 may select the candidate label that has the highest score among the candidate labels that do not exceed a predetermined length limit (such as 10 or 20 words).
According to an embodiment of the invention, the label generation module 630 may select the label for the element 670 in combination with the semantic clustering module 660. According to an embodiment of the invention, the label generation module 630 may send the plurality of labels to the semantic clustering module 660, and the semantic clustering module 660 may group the plurality of candidate labels into one or more clusters by semantic clustering. The semantic clustering method may include, for example, K-Means, Chameleon, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), etc.
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The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
The descriptions of the various embodiments of the present invention have been presented for purposes of illustration but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
