Disclosure of Invention
The embodiment of the application provides an enterprise knowledge management method, an enterprise knowledge management system, electronic equipment and a storage medium, which at least solve the problem that the existing enterprise knowledge management method can not combine two parts together to carry out knowledge management.
In a first aspect, an embodiment of the present application provides an enterprise knowledge management method, including a structured data processing step of obtaining structured data in enterprise knowledge, performing a first definition on content to be mapped in the structured data, generating a knowledge graph according to the first definition, an unstructured data processing step of obtaining unstructured data in the enterprise knowledge, performing a second definition on content to be mapped in the unstructured data, supplementing the content to be mapped in the unstructured data into the knowledge graph according to the second definition, setting a priority of the structured data and the unstructured data, determining a coverage relationship according to the priority when new content is entered in the knowledge graph, and constructing a visual interface, and displaying the knowledge graph on the visual interface.
Preferably, the structured data processing step further comprises analyzing the service field content in the structured data and defining entities, relationship categories and attributes in the service field content.
Preferably, the unstructured data processing step further comprises the steps of analyzing the unstructured data through a natural language processing technology, constructing an extractor according to the second definition, extracting the entity, the relation category and the attribute in the unstructured data through the extractor, and supplementing the entity, the relation category and the attribute in the unstructured data into the knowledge graph.
Preferably, the method further comprises a knowledge graph dynamic supplementing step of supplementing the knowledge graph according to a preset period, supplementing the structured data in an incremental operation mode and supplementing the unstructured data in a full-scale operation mode.
The embodiment of the application provides an enterprise knowledge management system which is suitable for the enterprise knowledge management method, and comprises a structured data processing module, a priority policy setting module, a visual interface construction module and a visual interface construction module, wherein the structured data processing module is used for acquiring structured data in enterprise knowledge and carrying out a first definition on content to be mapped in the structured data, generating a knowledge map according to the first definition on the content to be mapped in the structured data, the unstructured data processing module is used for acquiring unstructured data in the enterprise knowledge and carrying out a second definition on the content to be mapped in the unstructured data, supplementing the content to be mapped in the unstructured data into the knowledge map according to the second definition, the priority policy setting module is used for setting priorities of the structured data and the unstructured data, and when new content enters in the knowledge map, determining a coverage relation according to the priorities, and the visual interface is constructed and displayed on the visual interface.
In some of these embodiments, the structured data processing module further comprises analyzing the business field content in the structured data and defining entities, relationship categories, and attributes in the business field content.
In some embodiments, the unstructured data processing module further includes analyzing the unstructured data through natural language processing technology, constructing an extractor according to the second definition, extracting entities, relationship types and attributes in the unstructured data through the extractor, and supplementing the extracted entities, relationship types and attributes in the unstructured data into the knowledge graph.
In some embodiments, the system further comprises a knowledge graph dynamic supplementing module, wherein the knowledge graph is supplemented according to a preset period, the structured data is supplemented in an incremental operation mode, and the unstructured data is supplemented in a full-scale operation mode.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, where the processor implements an enterprise knowledge management method as described in the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements an enterprise knowledge management method as described in the first aspect above.
The application can be applied to the technical field of knowledge maps. Compared with the related art, the method and the device adopt the mode of an industry map library, fully utilize the semantic expression capability of rich maps, and are more suitable for storing and managing the basic knowledge with rich types, different standards and various sources of enterprises. The method comprises the steps of carrying out graph-based knowledge management in a visual mode, simplifying knowledge management operation and threshold, defining the flow and rules of map schema and unstructured data acquisition schema fusion operation of structured data acquisition, enabling knowledge acquired by multiple channels to be effectively fused in mechanism, increasing logic definition and enhancing knowledge floor property.
Detailed Description
The present application will be described and illustrated with reference to the accompanying drawings and examples in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. All other embodiments, which can be made by a person of ordinary skill in the art based on the embodiments provided by the present application without making any inventive effort, are intended to fall within the scope of the present application.
It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the described embodiments of the application can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," and similar referents in the context of the application are not to be construed as limiting the quantity, but rather as singular or plural. The terms "comprises," "comprising," "includes," "including," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the application organizes and manages the knowledge in the enterprise in the form of a map and defines the entity, relationship and event concerned by the service.
Embodiments of the present invention will be described in detail below with reference to the attached drawings:
fig. 1 is a flowchart of an enterprise knowledge management method of the present invention, please refer to fig. 1, the enterprise knowledge management method of the present invention includes the following steps:
S1, obtaining structured data in enterprise knowledge, carrying out first definition on content to be mapped in the structured data, and generating a knowledge graph on the content to be mapped in the structured data according to the first definition.
Optionally, analyzing the service field content in the structured data, and defining the entity, the relationship category and the attribute in the service field content.
In a specific implementation, for the structured data, by analyzing the content of the service field contained in the database table, the entity, the relation category and the attribute which can be possessed by the partial map are defined, and the map schema defined from the structured data is imported.
In the specific implementation, because the structured graph construction is a complex engineering logic, in order to ensure the high consistency of the structured data graph generation work output result and the management knowledge in the knowledge base, the schema defined for the structured data in the knowledge base only supports the increment synchronization, and does not support the deletion and modification operations.
S2, obtaining unstructured data in the enterprise knowledge, carrying out second definition on the content to be mapped in the unstructured data, and supplementing the content to be mapped in the unstructured data into the knowledge map according to the second definition.
Optionally, the unstructured data is analyzed through a natural language processing technology, an extractor is constructed according to the second definition, the entity, the relation category and the attribute in the unstructured data are extracted through the extractor, and the extracted entity, relation category and attribute in the unstructured data are supplemented into the knowledge graph.
In specific implementation, for unstructured data, an entity/relationship extractor is constructed through analysis of text data and comprehensive analysis of the capability of an entity recognition algorithm, so that the supplement of map entity, relationship category and attribute definition is completed, and map schema defined by unstructured data is synchronized in a knowledge map.
In a specific implementation, when synchronizing the schema defined by unstructured data, if the schema is a brand new schema, the identifier information is added into the original schema definition if the schema is a schema already related in the structured data.
In the specific implementation, for the map part with the source defined by the unstructured data, manual addition, deletion and modification operations are supported, a user can add new entities or modify the associated identifier of the original entities on the page according to the continuous enrichment and optimization of the identifier, and for the schema with the source of the structured and unstructured data, only the modification identifier is supported, but not the deletion operation is supported.
And S3, setting the priority of the structured data and the unstructured data, and determining an overlay relationship according to the priority when new content is entered in the knowledge graph.
In specific implementation, the setting of the data priority policy may select a structured priority, or may select an unstructured priority, and may cover data with a high priority and a low priority.
And S4, supplementing the knowledge graph according to a preset period, supplementing the structured data in an incremental operation mode, and supplementing the unstructured data in a full-scale operation mode.
In the specific implementation, the knowledge extraction task supports timing and periodical execution, the structured data adopts an incremental operation mode to finish data addition, the unstructured data adopts a full-scale operation mode, and a full-coverage strategy is adopted to update.
And S5, constructing a visual interface, and visualizing and displaying the knowledge graph on the visual interface.
FIG. 2 is a block diagram of an enterprise knowledge management system in accordance with the present invention, please refer to FIG. 2, which includes:
the structured data processing module 1 is used for obtaining structured data in enterprise knowledge, carrying out first definition on content to be mapped in the structured data, and generating a knowledge graph on the content to be mapped in the structured data according to the first definition.
Optionally, analyzing the service field content in the structured data, and defining the entity, the relationship category and the attribute in the service field content.
In a specific implementation, for the structured data, by analyzing the content of the service field contained in the database table, the entity, the relation category and the attribute which can be possessed by the partial map are defined, and the map schema defined from the structured data is imported.
In the specific implementation, because the structured graph construction is a complex engineering logic, in order to ensure the high consistency of the structured data graph generation work output result and the management knowledge in the knowledge base, the schema defined for the structured data in the knowledge base only supports the increment synchronization, and does not support the deletion and modification operations.
And the unstructured data processing module 2 is used for acquiring unstructured data in the enterprise knowledge, carrying out a second definition on the content to be mapped in the unstructured data, and supplementing the content to be mapped in the unstructured data into the knowledge map according to the second definition.
Optionally, the unstructured data is analyzed through a natural language processing technology, an extractor is constructed according to the second definition, the entity, the relation category and the attribute in the unstructured data are extracted through the extractor, and the extracted entity, relation category and attribute in the unstructured data are supplemented into the knowledge graph.
In specific implementation, for unstructured data, an entity/relationship extractor is constructed through analysis of text data and comprehensive analysis of the capability of an entity recognition algorithm, so that the supplement of map entity, relationship category and attribute definition is completed, and map schema defined by unstructured data is synchronized in a knowledge map.
In a specific implementation, when synchronizing the schema defined by unstructured data, if the schema is a brand new schema, the identifier information is added into the original schema definition if the schema is a schema already related in the structured data.
In the specific implementation, for the map part with the source defined by the unstructured data, manual addition, deletion and modification operations are supported, a user can add new entities or modify the associated identifier of the original entities on the page according to the continuous enrichment and optimization of the identifier, and for the schema with the source of the structured and unstructured data, only the modification identifier is supported, but not the deletion operation is supported.
And the priority policy setting module 3 is used for setting the priorities of the structured data and the unstructured data, and determining the coverage relation according to the priorities when new content is entered in the knowledge graph.
In specific implementation, the setting of the data priority policy may select a structured priority, or may select an unstructured priority, and may cover data with a high priority and a low priority.
And the knowledge graph dynamic supplementing module 4 supplements the knowledge graph according to a preset period, supplements the structured data in an incremental operation mode and supplements the unstructured data in a full-scale operation mode.
In the specific implementation, the knowledge extraction task supports timing and periodical execution, the structured data adopts an incremental operation mode to finish data addition, the unstructured data adopts a full-scale operation mode, and a full-coverage strategy is adopted to update.
And the visual interface construction module 5 is used for constructing a visual interface, visualizing and displaying the knowledge graph on the visual interface.
In addition, an enterprise knowledge management method described in connection with FIG. 1 may be implemented by an electronic device. Fig. 3 is a frame diagram of the electronic device of the present invention.
The electronic device may comprise a processor 61 and a memory 62 storing computer program instructions.
In particular, the processor 61 may include a Central Processing Unit (CPU), or an Application SPECIFIC INTEGRATED Circuit (ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present application.
Memory 62 may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory 62 may comprise a hard disk drive (HARD DISK DRIVE, abbreviated HDD), a floppy disk drive, a Solid state drive (Solid STATE DRIVE, abbreviated SSD), flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (Universal Serial Bus, abbreviated USB) drive, or a combination of two or more of these. The memory 62 may include removable or non-removable (or fixed) media, where appropriate. The memory 62 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 62 is a Non-Volatile (Non-Volatile) memory. In particular embodiments, memory 62 includes Read-Only Memory (ROM) and random access Memory (Random Access Memory, RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, abbreviated PROM), an erasable PROM (Erasable Programmable Read-Only Memory, abbreviated EPROM), an electrically erasable PROM (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, abbreviated EEPROM), an electrically rewritable ROM (ELECTRICALLY ALTERABLE READ-Only Memory, abbreviated EAROM), or a FLASH Memory (FLASH), or a combination of two or more of these. The RAM may be a Static Random-Access Memory (SRAM) or a dynamic Random-Access Memory (Dynamic Random Access Memory DRAM), where the DRAM may be a fast page mode dynamic Random-Access Memory (Fast Page Mode Dynamic Random Access Memory, FPMDRAM), an extended data output dynamic Random-Access Memory (Extended Date Out Dynamic Random Access Memory, EDODRAM), a synchronous dynamic Random-Access Memory (Synchronous Dynamic Random-Access Memory, SDRAM), or the like, as appropriate.
Memory 62 may be used to store or cache various data files that need to be processed and/or communicated, as well as possible computer program instructions for execution by processor 61.
The processor 61 implements any of the enterprise knowledge management methods of the above embodiments by reading and executing computer program instructions stored in the memory 62.
In some of these embodiments, the electronic device may also include a communication interface 63 and a bus 60. As shown in fig. 3, the processor 61, the memory 62, and the communication interface 63 are connected to each other through the bus 60 and perform communication with each other.
The communication port 63 may enable data communication with other components such as an external device, an image/data acquisition device, a database, an external storage, an image/data processing workstation, etc.
Bus 60 includes hardware, software, or both, that couple components of the electronic device to one another. The Bus 60 includes, but is not limited to, at least one of a Data Bus (Data Bus), an Address Bus (Address Bus), a Control Bus (Control Bus), an Expansion Bus (Expansion Bus), and a Local Bus (Local Bus). By way of example, and not limitation, bus 60 may include a graphics acceleration interface (ACCELERATED GRAPHICS Port, abbreviated as AGP) or other graphics Bus, an enhanced industry standard architecture (Extended Industry Standard Architecture, abbreviated as EISA) Bus, a Front Side Bus (Front Side Bus, abbreviated as FSB), a HyperTransport (abbreviated as HT) interconnect, an industry standard architecture (Industry Standard Architecture, abbreviated as ISA) Bus, a wireless bandwidth (InfiniBand) interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a micro channel architecture (Micro Channel Architecture, abbreviated as MCA) Bus, a peripheral component interconnect (PERIPHERAL COMPONENT INTERCONNECT, abbreviated as PCI) Bus, a PCI-Express (PCI-X) Bus, a serial advanced technology attachment (SERIAL ADVANCED Technology Attachment, abbreviated as SATA) Bus, a video electronics standards Association local (Video Electronics Standards Association Local Bus, abbreviated as VLB) Bus, or other suitable Bus, or a combination of two or more of these. Bus 60 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.
The electronic device can execute the enterprise knowledge management method in the embodiment of the application.
In addition, in combination with the enterprise knowledge management method in the above embodiment, the embodiment of the present application may be implemented by providing a computer readable storage medium. The computer readable storage medium has stored thereon computer program instructions which when executed by a processor implement any of the enterprise knowledge management methods of the above embodiments.
The storage medium includes various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ReadOnly Memory, abbreviated as ROM), a random access memory (Random Access Memory, abbreviated as RAM), a magnetic disk, or an optical disk.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.