CN117807252A - Knowledge graph-based data processing method, device and system and storage medium - Google Patents
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Abstract
The application relates to a data processing method, a device, a system and a storage medium based on a knowledge graph, which comprise responding to acquired sentence data, analyzing the sentence data to obtain a plurality of phrases; assigning a direction vector to each phrase; and connecting the obtained multiple direction vectors end to end in sequence to obtain a pointing vector, searching in a knowledge graph by using the pointing vector to obtain a search result, wherein the surrounding areas of the pointing vector are all included in a first search range, and the search result is obtained according to the content in the first search range. According to the data processing method, device and system based on the knowledge graph and the storage medium, the retrieval and the high matching of the content are realized based on the three-dimensional space operation, and the more accurate content can be obtained.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a data processing method, device, system and storage medium based on a knowledge graph.
Background
The knowledge graph is a structured semantic knowledge base for describing concepts and interrelationships thereof in a physical world in a symbol form, and the basic constituent units of the knowledge graph are entity-relation-entity triples and entity and related attribute-value pairs thereof, and the entities are mutually connected through the relation to form a net-shaped knowledge structure.
In the search field, the knowledge graph has wide application prospect, for example, in the current artificial intelligence field, the trained artificial intelligence can give accurate answers according to questions proposed by questioners, and the implementation of the process can be realized by depending on the knowledge graph. Because in the process of obtaining an accurate answer, an accurate search is required in the existing contents. Prior to such accurate searching, the commonly used searching method is based on association searching, and the searching method sorts the searched content according to the association degree, so that the questioner needs to further screen the content.
With the wide-range use of artificial intelligence, the accuracy and energy consumption of the retrieval process are also attracting attention, and the higher accuracy can reduce the use cost of artificial intelligence. The knowledge graph can open up the data island to realize the networking of the content, so that the content in the rich vertical field can be provided, but further research is needed on how to realize the rapid orientation of the content.
Disclosure of Invention
In order to improve the matching degree of the problem and the result in the retrieval process, the application provides a data processing method, device, system and storage medium based on a knowledge graph, and the method, device and system are used for realizing the high matching of the retrieval and the content based on three-dimensional space operation, so that more accurate content can be obtained.
The above object of the present application is achieved by the following technical solutions:
in a first aspect, the present application provides a data processing method based on a knowledge graph, including:
responding to the obtained sentence data, and analyzing the sentence data to obtain a plurality of phrases;
assigning a direction vector to each phrase;
connecting the obtained multiple direction vectors end to end according to the sequence to obtain a pointing vector; and
and searching in the knowledge graph by using the pointing vector to obtain a search result, wherein the surrounding areas of the pointing vector are all included in a first search range, and the search result is obtained according to the content in the first search range.
In a possible implementation manner of the first aspect, assigning a direction vector to each phrase includes:
dividing statement data into multiple units to obtain multiple statement units, wherein the length of each statement unit obtained by each division is the same;
analyzing the sentence units obtained by each division to obtain corresponding meanings of the sentence units, wherein each sentence unit corresponds to at least one meaning;
obtaining a semantic group of sentence data according to the division and a meaning corresponding to each sentence unit;
screening semantic groups with the highest occurrence frequency and the same expression meaning or similar expression meaning from the semantic groups, and marking the semantic groups as meaning semantic groups; and
each phrase is assigned a direction vector according to the meaning semantic group.
In a possible implementation manner of the first aspect, when one sentence unit corresponds to a plurality of meanings, a plurality of direction vectors are assigned to the sentence unit, and each direction vector corresponds to one meaning of the sentence unit.
In a possible implementation manner of the first aspect, the synthesis processing is performed on the plurality of direction vectors assigned to the sentence unit, and the plurality of direction vectors assigned to the sentence unit are combined into one direction vector.
In a possible implementation manner of the first aspect, retrieving in the knowledge-graph using the pointing vector includes:
taking the connection points of the two directional vectors with connection relations on the sequence as nodes, and bringing the content at the nodes or in the range of the nodes into a first retrieval range;
combining the obtained first search ranges to obtain a second search range, wherein at least one content of the first search range has a direct connection relationship with the content in the other first search range;
obtaining a sub-knowledge graph by using the content in the second retrieval range;
and searching in the sub-knowledge graph by using the pointing vector to obtain a search result.
In a possible implementation manner of the first aspect, retrieving in the sub-knowledge-graph using the pointing vector includes:
calculating the association degree of each content in the sub-knowledge graph and the direction vector, and grouping the content in the sub-knowledge graph according to the association degree to obtain a plurality of content groups;
sorting the content packets according to the association degree, wherein the association degree of the content packets is inversely related to the position in the sequence;
the search result is obtained in at least the preceding content packet of the sequential sequence.
In a possible implementation manner of the first aspect, obtaining the search result in at least a previous content packet of the sequential sequence includes:
obtaining a plurality of content in a first content packet;
sorting the contents according to the relation and constructing a comparison pointing vector according to the sorted contents;
calculating the similarity of the pointing vector and the contrast pointing vector;
when the similarity between the pointing vector and the contrast pointing vector is larger than or equal to a set threshold value, the ordered content is used as a retrieval result;
splitting and reconstructing the contrast pointing vector when the similarity of the pointing vector and the contrast pointing vector is smaller than a set threshold, wherein the similarity of the reconstructed contrast pointing vector and the pointing vector is larger than or equal to the set threshold;
and supplementing the search result by using the newly added part in the reconstructed contrast pointing vector.
In a second aspect, the present application provides a knowledge-graph-based data processing apparatus, including:
the first analysis unit is used for responding to the acquired sentence data and analyzing the sentence data to obtain a plurality of phrases;
a first processing unit for assigning a direction vector to each phrase;
the second processing unit is used for connecting the obtained multiple direction vectors end to end according to the sequence to obtain a pointing vector; and
the first retrieval unit is used for retrieving in the knowledge graph by using the pointing vector to obtain a retrieval result, the surrounding areas of the pointing vector are all included in the first retrieval range, and the retrieval result is obtained according to the content in the first retrieval range.
In a third aspect, the present application provides a knowledge-graph-based data processing system, the system comprising:
one or more memories for storing instructions; and
one or more processors configured to invoke and execute the instructions from the memory, to perform the method as described in the first aspect and any possible implementation of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium comprising:
a program which, when executed by a processor, performs a method as described in the first aspect and any possible implementation of the first aspect.
In a fifth aspect, the present application provides a computer program product comprising program instructions which, when executed by a computing device, perform a method as described in the first aspect and any possible implementation manner of the first aspect.
In a sixth aspect, the present application provides a chip system comprising a processor for implementing the functions involved in the above aspects, e.g. generating, receiving, transmitting, or processing data and/or information involved in the above methods.
The chip system can be composed of chips, and can also comprise chips and other discrete devices.
In one possible design, the system on a chip also includes memory to hold the necessary program instructions and data. The processor and the memory may be decoupled, provided on different devices, respectively, connected by wire or wirelessly, or the processor and the memory may be coupled on the same device.
The beneficial effects of this application are:
according to the knowledge graph-based data processing method, device and system and the storage medium, through accurate splitting of sentence data and accurate matching of the sentence data and the content in a space range, accurate matching of questions and answer contents is achieved, more accurate answer contents can be provided for a questioner in the mode, and answer contents needed by the questioner can be obtained with fewer questioning times.
Drawings
Fig. 1 is a schematic block diagram of a data processing method based on a knowledge graph.
Fig. 2 is a schematic diagram of a process for obtaining a pointing vector provided in the present application.
Fig. 3 is a schematic diagram of a case where the similarity between the pointing vector and the contrast pointing vector is greater than or equal to a set threshold.
Fig. 4 is a schematic diagram of a comparison pointing vector provided in the present application when the similarity between the pointing vector and the comparison pointing vector is greater than or equal to a set threshold after adjustment.
Detailed Description
The technical solutions in the present application are described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, in some examples, the data processing method based on a knowledge graph disclosed in the present application includes the following steps:
s1, responding to the obtained sentence data, and analyzing the sentence data to obtain a plurality of phrases;
s2, giving a direction vector to each phrase;
s3, connecting the obtained multiple direction vectors end to end according to the sequence to obtain a pointing vector; and
and S4, searching in the knowledge graph by using the pointing vector to obtain a search result, wherein the surrounding areas of the pointing vector are all included in a first search range, and the search result is obtained according to the content in the first search range.
The data processing method based on the knowledge graph is applied to a server, the server is deployed locally or at the cloud, a questioner sends questions to the server, for example, in the form of a dialog box, and the server understands the questions and gives answers after receiving the questions.
In step S1, the server receives a sentence data, where the sentence data refers to the problem mentioned in the foregoing, after receiving the problem, the server first parses the sentence data to obtain a plurality of phrases, where the phrases are key parts in the sentence data, and in the sentence data, the association relations of the phrases, for example, including relations, subordinate relations, and the like, are also included.
In step S2, referring to fig. 2, a direction vector is given to each phrase, the direction vector is given according to an association relationship, and is described herein by means of a knowledge graph, wherein the knowledge graph reflects the relationship between entities, and the entities can be regarded as islands one by one, and the relationship is a bridge connecting the islands, and the islands and the bridge form a network. The knowledge graph is transferred into a three-dimensional space here, and for the relationships, a vector is used to represent, the vector having a length and a direction, where the different relationships are distinguished by the direction.
In step S3, the obtained plurality of direction vectors are connected end to end in order to obtain a pointing vector, where the pointing vector may be further interpreted as a polyline in three-dimensional space, where each segment of the polyline has a direction.
In step S4, the search is performed in the knowledge graph by using the directional vector, so as to obtain a search result, wherein the surrounding areas of the directional vector are all included in the first search range, and the search result is obtained according to the content in the first search range.
The method can obtain an accurate first search range in the knowledge graph at the same time, and then the content in the first search range obtains a search result. The processing mode has the advantages that all phrases in the received sentence data can be searched at the same time, and an accurate search range can be obtained by relying on the pointing vector in the search process. In the field of artificial intelligence, the answers of questioners can be accurately searched, because the association relationship between phrases is considered in the searching process.
In some examples, assigning a direction vector to each phrase includes the steps of:
s21, dividing statement data into multiple units to obtain multiple statement units, wherein the length of each statement unit obtained by each division is the same;
s22, analyzing the sentence units obtained by each division to obtain the corresponding meaning of the sentence units, wherein each sentence unit corresponds to at least one meaning;
s23, obtaining a semantic group of sentence data according to the division and a meaning corresponding to each sentence unit;
s24, screening out semantic groups with the highest occurrence number and the same expression meaning or similar expression meaning from the semantic groups, and marking the semantic groups as meaning semantic groups; and
s25, assigning a direction vector to each phrase according to the meaning semantic group.
Specifically, in step S21, the sentence data is first divided into a plurality of units to obtain a plurality of sentence units, and the sentence units obtained by each division have the same length.
It should be understood that sentence data is composed of subjects, predicates, objects, animals, subjects, complements, centers, etc., and is difficult for a machine to directly distinguish, and the sentence data can be understood only after being divided, so that the degree of understanding is directly determined by whether the division is appropriate or not.
For the understanding degree, there are two reference indexes of the accurate matching rate and the execution accuracy rate, the problem of the accurate matching rate is solved by using a multi-division mode, sentence units with different lengths can be obtained by using the multi-division mode, and each sentence unit obtained at the moment has two results which can be understood and cannot be understood, and the results which cannot be understood are discarded.
The results were counted and classified as understood. That is, in step S22, the sentence units obtained by each division are parsed to obtain the corresponding meaning of the sentence units, and each sentence unit corresponds to at least one meaning.
Next, in step S23, a semantic group of sentence data is obtained according to a meaning corresponding to each sentence unit, and then in step S24, the semantic group with the same meaning or similar meaning, which has the largest occurrence number, is screened out and is recorded as a meaning semantic group. Finally, in step S25, a direction vector is assigned to each phrase according to the meaning semantic group.
In this way, the meaning of the statement unit can be obtained in an accurate manner.
When one sentence unit corresponds to a plurality of meanings, a plurality of direction vectors are assigned to the sentence unit, each direction vector corresponding to one meaning of the sentence unit.
In some possible implementations, the plurality of direction vectors assigned to the sentence unit are synthesized, and the plurality of direction vectors assigned to the sentence unit are combined into one direction vector. In this way, similar to the fusion process, it should be noted here that when a plurality of direction vectors are assigned to the sentence unit, the direction vectors should point to one direction or one area, for example, a constraint rule is that the included angle between any two direction vectors is within a required range, and for the direction vectors exceeding the required range, a discard process is required.
In some examples, retrieving in the knowledge-graph using the pointing vector includes the steps of:
s41, taking a connection point of two directional vectors with connection relation on the sequence as a node, and bringing the content at the node or in the node range into a first search range;
s42, merging the obtained first search ranges to obtain a second search range, wherein at least one content of the first search range has a direct connection relationship with the content in the other first search range;
s43, obtaining a sub-knowledge graph by using the content in the second search range;
s44, searching in the sub-knowledge graph by using the pointing vector to obtain a search result.
In step S41 to step S44, the connection point of the two directional vectors with the connection relationship on the sequence is taken as a node, the content at the node or within the node range is included in the first search range, and then the obtained first search ranges are combined to obtain the second search range, wherein at least one content of the first search range has a direct connection relationship with the content in the other first search range.
Thus, a smaller search range, namely a second search range, is obtained, meanwhile, the synthesis of the first search ranges is limited, at least one content of the first search ranges needs to have a direct connection relation with the content in the other first search range, and therefore connection relation among a plurality of first search ranges needs to be ensured instead of independent data islands.
And then obtaining a sub-knowledge graph by using the content in the second retrieval range, wherein the sub-knowledge graph consists of the content in the second retrieval range, and then retrieving in the sub-knowledge graph by using the pointing vector to obtain a retrieval result.
The steps of retrieving in the sub-knowledge graph using the pointing vector are as follows:
s441, calculating the association degree of each content in the sub-knowledge graph and the direction vector, and grouping the content in the sub-knowledge graph according to the association degree to obtain a plurality of content groups;
s442, sorting the content packets according to the association degree, wherein the association degree of the content packets is inversely related to the position in the sequence;
s443, obtaining the search result in at least the previous content packet of the sequence.
Specifically, the association degree between each content in the sub-knowledge graph and the pointing vector is calculated firstly, namely, how many phrases in sentence data each content in the sub-knowledge graph has a relation with the sub-knowledge graph is calculated, and then the contents in the sub-knowledge graph are grouped according to the number of the phrases with the relation.
At this time, the content which has no relation with the word groups in the sentence data can be eliminated, and the word groups which have close connection relation with the word groups in the sentence data can be obtained. The search result is then obtained in at least the preceding content packet of the sequential sequence.
The step of obtaining the search result in at least the preceding content packet of the sequential sequence is as follows:
s4431 obtaining a plurality of contents in the first content packet;
s4432, sorting the contents according to the relation and constructing a comparison pointing vector according to the sorted contents;
s4433, calculating the similarity between the pointing vector and the contrast pointing vector;
s4434, when the similarity between the pointing vector and the contrast pointing vector is greater than or equal to a set threshold, taking the ordered content as a retrieval result;
s4435, splitting and reconstructing the contrast pointing vector when the similarity of the pointing vector and the contrast pointing vector is smaller than a set threshold, wherein the similarity of the reconstructed contrast pointing vector and the pointing vector is larger than or equal to the set threshold;
and S4436, supplementing the search result by using the newly added part in the reconstructed contrast direction vector.
In steps S4431 to S4436, a comparison vector is first constructed according to the contents obtained in the first content group, and then the similarity between the vector and the comparison vector is calculated. There are two processing modes at this time:
firstly, when the similarity between the pointing vector and the contrast pointing vector is larger than or equal to a set threshold value, taking the ordered content as a retrieval result;
secondly, when the similarity between the pointing vector and the contrast pointing vector is smaller than a set threshold, splitting and reconstructing the contrast pointing vector, and supplementing a search result by using a newly added part in the reconstructed contrast pointing vector, wherein the similarity between the reconstructed contrast pointing vector and the pointing vector is larger than or equal to the set threshold.
Referring to fig. 3 and 4, the splitting and reconstructing of the contrast vector divides the contrast vector into multiple segments (similar region and dissimilar region), the divided segments are similar to a part of the vector, and then the empty part between two adjacent segments is complemented by a new vector, where the similarity process is not limited in length.
When supplementing with a new vector, a content is selected in the first content group to be placed in the dissimilar region, then the pointing vector is assigned, and then the contrast pointing vector is reconstructed.
The new vector means that a change is made to the relationship, and the use of the new vector complement means that a new relationship is added. For the two processing modes, when at least one contrast vector can be obtained by using the first processing mode, the second processing mode is not used; when the contrast vector cannot be obtained by using the first processing method, the contrast vector is obtained by using the second processing method.
The application also provides a data processing device based on the knowledge graph, which comprises:
the first analysis unit is used for responding to the acquired sentence data and analyzing the sentence data to obtain a plurality of phrases;
a first processing unit for assigning a direction vector to each phrase;
the second processing unit is used for connecting the obtained multiple direction vectors end to end according to the sequence to obtain a pointing vector; and
the first retrieval unit is used for retrieving in the knowledge graph by using the pointing vector to obtain a retrieval result, the surrounding areas of the pointing vector are all included in the first retrieval range, and the retrieval result is obtained according to the content in the first retrieval range.
Further, the method further comprises the following steps:
the dividing unit is used for dividing the sentence data into multiple units to obtain multiple sentence units, and the length of each sentence unit obtained by each division is the same;
the second analysis unit is used for analyzing the sentence units obtained by each division to obtain the corresponding meaning of the sentence units, and each sentence unit corresponds to at least one meaning;
the third processing unit is used for obtaining a semantic group of the sentence data according to the division and a meaning corresponding to each sentence unit;
the screening unit is used for screening semantic groups with the highest occurrence number and the same or similar expression meaning from the semantic groups, and marking the semantic groups as meaning semantic groups; and
and the giving unit is used for giving a direction vector to each phrase according to the meaning semantic group.
Further, when one sentence unit corresponds to a plurality of meanings, a plurality of direction vectors are assigned to the sentence unit, and each direction vector corresponds to one meaning of the sentence unit.
Further, the plurality of direction vectors given to the sentence unit are synthesized, and the plurality of direction vectors given to the sentence unit are combined into one direction vector.
Further, the method further comprises the following steps:
a first search range construction unit, configured to take a connection point of two directional vectors with a connection relationship on the sequence as a node, and incorporate content at the node or within the node range into the first search range;
the second search range construction unit is used for combining the obtained first search ranges to obtain a second search range, and at least one content of the first search range has a direct connection relationship with the content in the other first search range;
the second retrieval unit is used for obtaining a sub-knowledge graph by using the content in the second retrieval range;
and the third retrieval unit is used for retrieving in the sub-knowledge graph by using the pointing vector to obtain a retrieval result.
Further, the method further comprises the following steps:
the first calculation unit is used for calculating the association degree of each content in the sub-knowledge graph and the direction vector, and grouping the content in the sub-knowledge graph according to the association degree to obtain a plurality of content groups;
the ordering unit is used for ordering the content packets according to the association degree, and the association degree of the content packets is inversely related to the position in the sequence;
and a fourth retrieval unit for obtaining a retrieval result in at least a previous content packet of the sequential sequence.
Further, the method further comprises the following steps:
a fourth processing unit for obtaining a plurality of contents in the first content packet;
the fifth processing unit is used for sequencing the contents according to the relation and constructing a comparison pointing vector according to the sequenced contents;
the second calculating unit is used for calculating the similarity between the pointing vector and the contrast pointing vector;
the sixth processing unit is used for taking the ordered content as a retrieval result when the similarity between the pointing vector and the contrast pointing vector is greater than or equal to a set threshold value;
the seventh processing unit is used for splitting and reconstructing the contrast pointing vector when the similarity of the pointing vector and the contrast pointing vector is smaller than a set threshold, and the reconstructed similarity of the contrast pointing vector and the pointing vector is larger than or equal to the set threshold;
and the result supplementing unit is used for supplementing the search result by using the newly added part in the reconstructed contrast pointing vector.
In one example, the unit in any of the above apparatuses may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (application specific integratedcircuit, ASIC), or one or more digital signal processors (digital signal processor, DSP), or one or more field programmable gate arrays (field programmable gate array, FPGA), or a combination of at least two of these integrated circuit forms.
For another example, when the units in the apparatus may be implemented in the form of a scheduler of processing elements, the processing elements may be general-purpose processors, such as a central processing unit (central processing unit, CPU) or other processor that may invoke the program. For another example, the units may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Various objects such as various messages/information/devices/network elements/systems/devices/actions/operations/processes/concepts may be named in the present application, and it should be understood that these specific names do not constitute limitations on related objects, and that the named names may be changed according to the scenario, context, or usage habit, etc., and understanding of technical meaning of technical terms in the present application should be mainly determined from functions and technical effects that are embodied/performed in the technical solution.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It should also be understood that in various embodiments of the present application, first, second, etc. are merely intended to represent that multiple objects are different. For example, the first time window and the second time window are only intended to represent different time windows. Without any effect on the time window itself, the first, second, etc. mentioned above should not impose any limitation on the embodiments of the present application.
It is also to be understood that in the various embodiments of the application, terms and/or descriptions of the various embodiments are consistent and may be referenced to one another in the absence of a particular explanation or logic conflict, and that the features of the various embodiments may be combined to form new embodiments in accordance with their inherent logic relationships.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a computer-readable storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned computer-readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The application also provides a data processing system based on the knowledge graph, which comprises:
one or more memories for storing instructions; and
one or more processors configured to invoke and execute the instructions from the memory to perform the method as set forth above.
The present application also provides a computer program product comprising instructions which, when executed, cause the data processing system to perform operations corresponding to the data processing system of the above-described method.
The present application also provides a chip system comprising a processor for implementing the functions involved in the above, e.g. generating, receiving, transmitting, or processing data and/or information involved in the above method.
The chip system can be composed of chips, and can also comprise chips and other discrete devices.
The processor referred to in any of the foregoing may be a CPU, microprocessor, ASIC, or integrated circuit that performs one or more of the procedures for controlling the transmission of feedback information described above.
In one possible design, the system on a chip also includes memory to hold the necessary program instructions and data. The processor and the memory may be decoupled, and disposed on different devices, respectively, and connected by wired or wireless means, so as to support the chip system to implement the various functions in the foregoing embodiments. In the alternative, the processor and the memory may be coupled to the same device.
Optionally, the computer instructions are stored in a memory.
Alternatively, the memory may be a storage unit in the chip, such as a register, a cache, etc., and the memory may also be a storage unit in the terminal located outside the chip, such as a ROM or other type of static storage device, a RAM, etc., that may store static information and instructions.
It is to be understood that the memory in this application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory.
The nonvolatile memory may be a ROM, a Programmable ROM (PROM), an Erasable Programmable ROM (EPROM), an electrically erasable programmable EPROM (EEPROM), or a flash memory.
The volatile memory may be RAM, which acts as external cache. There are many different types of RAM, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), and direct memory bus RAM.
The embodiments of the present invention are all preferred embodiments of the present application, and are not intended to limit the scope of the present application in this way, therefore: all equivalent changes in structure, shape and principle of this application should be covered in the protection scope of this application.
Claims (10)
1. The data processing method based on the knowledge graph is characterized by comprising the following steps of:
responding to the obtained sentence data, and analyzing the sentence data to obtain a plurality of phrases;
assigning a direction vector to each phrase;
connecting the obtained multiple direction vectors end to end according to the sequence to obtain a pointing vector; and
and searching in the knowledge graph by using the pointing vector to obtain a search result, wherein the surrounding areas of the pointing vector are all included in a first search range, and the search result is obtained according to the content in the first search range.
2. The knowledge-graph based data processing method of claim 1, wherein assigning a direction vector to each phrase comprises:
dividing statement data into multiple units to obtain multiple statement units, wherein the length of each statement unit obtained by each division is the same;
analyzing the sentence units obtained by each division to obtain corresponding meanings of the sentence units, wherein each sentence unit corresponds to at least one meaning;
obtaining a semantic group of sentence data according to the division and a meaning corresponding to each sentence unit;
screening semantic groups with the highest occurrence frequency and the same expression meaning or similar expression meaning from the semantic groups, and marking the semantic groups as meaning semantic groups; and
each phrase is assigned a direction vector according to the meaning semantic group.
3. The knowledge-graph-based data processing method according to claim 2, wherein when one sentence unit corresponds to a plurality of meanings, a plurality of direction vectors are assigned to the sentence unit, each direction vector corresponding to one meaning of the sentence unit.
4. The knowledge-graph-based data processing method according to claim 3, wherein the plurality of direction vectors assigned to the sentence unit are synthesized, and the plurality of direction vectors assigned to the sentence unit are combined into one direction vector.
5. The knowledge-graph based data processing method according to any one of claims 1 to 4, wherein retrieving in the knowledge-graph using the orientation vector comprises:
taking the connection points of the two directional vectors with connection relations on the sequence as nodes, and bringing the content at the nodes or in the range of the nodes into a first retrieval range;
combining the obtained first search ranges to obtain a second search range, wherein at least one content of the first search range has a direct connection relationship with the content in the other first search range;
obtaining a sub-knowledge graph by using the content in the second retrieval range;
and searching in the sub-knowledge graph by using the pointing vector to obtain a search result.
6. The knowledge-graph based data processing method of claim 5, wherein retrieving in the sub-knowledge-graph using the pointing vector comprises:
calculating the association degree of each content in the sub-knowledge graph and the direction vector, and grouping the content in the sub-knowledge graph according to the association degree to obtain a plurality of content groups;
sorting the content packets according to the association degree, wherein the association degree of the content packets is inversely related to the position in the sequence;
the search result is obtained in at least the preceding content packet of the sequential sequence.
7. The knowledge-graph based data processing method of claim 6, wherein obtaining a search result in at least a previous content packet of the sequential sequence comprises:
obtaining a plurality of content in a first content packet;
sorting the contents according to the relation and constructing a comparison pointing vector according to the sorted contents;
calculating the similarity of the pointing vector and the contrast pointing vector;
when the similarity between the pointing vector and the contrast pointing vector is larger than or equal to a set threshold value, the ordered content is used as a retrieval result;
splitting and reconstructing the contrast pointing vector when the similarity of the pointing vector and the contrast pointing vector is smaller than a set threshold, wherein the similarity of the reconstructed contrast pointing vector and the pointing vector is larger than or equal to the set threshold;
and supplementing the search result by using the newly added part in the reconstructed contrast pointing vector.
8. A knowledge-graph-based data processing apparatus, comprising:
the first analysis unit is used for responding to the acquired sentence data and analyzing the sentence data to obtain a plurality of phrases;
a first processing unit for assigning a direction vector to each phrase;
the second processing unit is used for connecting the obtained multiple direction vectors end to end according to the sequence to obtain a pointing vector; and
the first retrieval unit is used for retrieving in the knowledge graph by using the pointing vector to obtain a retrieval result, the surrounding areas of the pointing vector are all included in the first retrieval range, and the retrieval result is obtained according to the content in the first retrieval range.
9. A knowledge-graph-based data processing system, the system comprising:
one or more memories for storing instructions; and
one or more processors to invoke and execute the instructions from the memory to perform the method of any of claims 1 to 7.
10. A computer-readable storage medium, the computer-readable storage medium comprising:
program which, when executed by a processor, performs a method according to any one of claims 1 to 7.
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