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WO2024169529A1 - Procédé de construction de base de connaissances, procédé et appareil de récupération de données, et dispositif en nuage - Google Patents

Procédé de construction de base de connaissances, procédé et appareil de récupération de données, et dispositif en nuage Download PDF

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Publication number
WO2024169529A1
WO2024169529A1 PCT/CN2024/073350 CN2024073350W WO2024169529A1 WO 2024169529 A1 WO2024169529 A1 WO 2024169529A1 CN 2024073350 W CN2024073350 W CN 2024073350W WO 2024169529 A1 WO2024169529 A1 WO 2024169529A1
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key
data
text
knowledge base
original data
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PCT/CN2024/073350
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English (en)
Chinese (zh)
Inventor
李鹤
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杭州阿里云飞天信息技术有限公司
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Publication of WO2024169529A1 publication Critical patent/WO2024169529A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/31—Indexing; Data structures therefor; Storage structures
    • G06F16/316—Indexing structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3344—Query execution using natural language analysis

Definitions

  • the present application relates to the field of computer technology, and in particular to a knowledge base construction method, a data retrieval method, an apparatus and a cloud device.
  • the question-and-answer knowledge base is applied in intelligent customer service scenarios, in which the corresponding answer text is retrieved from the question-and-answer knowledge base based on the user's inquiry text and the user is replied.
  • a question-answering knowledge base is constructed by taking part of the text in the original text data as an index and taking the original text as the value of the index.
  • the question-answering knowledge base obtained by this construction method is not suitable for the retrieval of answer data of different modalities, which limits the application scope of the question-answering knowledge base in intelligent customer service scenarios.
  • a first aspect of an embodiment of the present application provides a method for constructing a knowledge base, comprising:
  • a second aspect of an embodiment of the present application provides a data retrieval method, comprising:
  • question-answering knowledge base answer data corresponding to the query data is retrieved, the modality of the answer data is one of text, image or video, and the question-answering knowledge base is constructed according to the knowledge base construction method of any one of the first aspects;
  • a third aspect of the present application embodiment provides a data retrieval method, which is applied to a terminal device.
  • the data retrieval method includes:
  • Receive answer data of the inquiry data sent by the server, the answer data is determined according to the data retrieval method of the second aspect.
  • a fourth aspect of the present application provides a knowledge base construction device, including:
  • An acquisition module used for acquiring multiple pieces of original data, where the modalities of the multiple pieces of original data are at least two of text, image or video;
  • a determination module for determining key text of the original data among the plurality of original data, determining a key of a key-value pair according to the key text, and determining a value of the key-value pair according to the original data, wherein the key text is used to describe the content of the original data;
  • the construction module is used to construct a question-answering knowledge base based on the key-value pairs corresponding to multiple pieces of original data.
  • the fifth aspect of an embodiment of the present application provides a cloud device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the knowledge base construction method of the first aspect, the data retrieval method of the second aspect and/or the third aspect are implemented.
  • the sixth aspect of the embodiments of the present application provides a computer-readable storage medium, in which computer execution instructions are stored.
  • the computer execution instructions are executed by a processor, they are used to implement the knowledge base construction method of the first aspect, the data retrieval method of the second aspect and/or the third aspect.
  • a seventh aspect of an embodiment of the present application provides a computer program product, the program product comprising: a computer program, the computer program is stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the knowledge base construction method of the first aspect, the data retrieval method of the second aspect and/or the third aspect.
  • the embodiment of the present application is applied in an intelligent customer service scenario, by obtaining multiple pieces of original data, the modalities of the multiple pieces of original data are at least two of text, image or video; for the original data in the multiple pieces of original data, the key text of the original data is determined, and the key of the key-value pair is determined according to the key text, and the value of the key-value pair is determined according to the original data, and the key text is used to describe the content of the original data; according to the key-value pairs corresponding to the multiple pieces of original data, a question-value pairs corresponding to the multiple pieces of original data, a question-and-answer knowledge base is constructed, and a question-and-answer knowledge base that supports answer retrieval in different modalities can be constructed.
  • FIG1 is a diagram of an application scenario provided by an exemplary embodiment of the present application.
  • FIG2 is a flowchart of a method for constructing a knowledge base provided by an exemplary embodiment of the present application
  • FIG3 is a schematic diagram of a question-answering knowledge base provided by an exemplary embodiment of the present application.
  • FIG4 is a schematic diagram of another question-answering knowledge base provided by an exemplary embodiment of the present application.
  • FIG5 is a flowchart of a data retrieval method provided by an exemplary embodiment of the present application.
  • FIG6 is a flowchart of another data retrieval method provided by an exemplary embodiment of the present application.
  • FIG7 is a structural block diagram of a knowledge base construction device provided by an exemplary embodiment of the present application.
  • FIG8 is a schematic diagram of the structure of a cloud device provided by an exemplary embodiment of the present application.
  • Intelligent customer service is an automatic question-and-answer product.
  • the index (key in the key-value pair) construction method is to directly build a query index for a certain field.
  • this method is not suitable because the multi-source knowledge of intelligent customer service is usually in different modalities, and each modality includes different types of data. As the business develops, the amount of raw data will also increase. Therefore, how to design a question-and-answer knowledge base that can support the retrieval of answer data of different modalities and different types, and at the same time, the method of building the knowledge base is scalable and can be applied to new raw data is particularly important.
  • the knowledge base construction method includes: obtaining multiple pieces of original data, the modalities of the multiple pieces of original data are at least two of text, image or video; for the original data in the multiple pieces of original data, determining the key text of the original data, and determining the key of the key-value pair according to the key text, and determining the value of the key-value pair according to the original data, the key text is used to describe the content of the original data; constructing a question-and-answer knowledge base according to the key-value pairs corresponding to the multiple pieces of original data.
  • the present application can construct a question-and-answer knowledge base that supports answer retrieval in different modalities, and the construction method is extensible. When there is new original data, this method can be used to obtain key-value pairs for the new original data and incorporate them into the question-and-answer knowledge base.
  • the overall knowledge base construction method can be realized by means of a cloud computing system.
  • the server of the knowledge base construction method can be a cloud server, so as to run various neural network models with the advantage of cloud resources; relative to the cloud, the knowledge base construction method can also be applied to conventional servers or server arrays and other server-side devices, which are not limited here.
  • an application scenario diagram provided by the present application includes raw data of different modalities, such as text, images, or videos.
  • the text data comes from different types of text, such as conversations, comments, and graphs. These raw data are processed to build a question-and-answer knowledge base. After the question-and-answer knowledge base is put online for use, upon receiving the user's inquiry text, the corresponding answer data can be found in the question-and-answer knowledge base, and the answer data can be returned to the user to reply to the user's inquiry text.
  • FIG2 is a flowchart of a method for constructing a knowledge base provided by an exemplary embodiment of the present application.
  • the method for constructing the knowledge base used in the server, as shown in FIG2, specifically includes the following steps:
  • the modalities of the multiple pieces of original data are at least two of text, image or video.
  • the database includes multiple pieces of raw data, which come from different data sources, and the data of the data source is text mode, image mode or video mode.
  • the data source of the text mode includes: different types of data sources, such as user comment data source, person-to-person dialogue data source, product attribute data source, product knowledge graph data source, product details page data source and product homepage data source.
  • the user comment data source includes: user comment text on the quality of a certain product, the express delivery speed of the product or the service of the merchant.
  • the person-to-person dialogue data source includes: the dialogue text between the user and the merchant's manual customer service;
  • the product attribute data source includes: text descriptions of product attributes (such as category, size, color, etc.).
  • the product knowledge graph data source includes: nodes represent products, and the connections between nodes represent the knowledge graph of the connections between products.
  • the product details page data source contains text content on the image, which is used to describe the product. These text introduction contents can be identified by OCR (optical character recognition) technology and used as raw data.
  • OCR optical character recognition
  • the data source of the product homepage is the text content contained in the image, which is also used to describe the product. The text content can also be recognized by OCR technology and used as original data.
  • the image may include an image of a product object, or may include an image of text, such as the product details page, product homepage, etc.
  • the video may include a product introduction video, a product usage video, or a product installation video.
  • a piece of raw data such as a comment text is a piece of raw data
  • a person-to-person conversation is a piece of raw data
  • a product attribute is a piece of raw data
  • a knowledge graph is a piece of raw data
  • all the text recognized in an image is a piece of raw data
  • an image is a piece of raw data
  • a video is a piece of raw data.
  • the source of the original data is not limited.
  • At least one key text can be determined corresponding to a piece of raw data.
  • the key text is used to describe the content of the raw data. Determining the key text of the raw data includes: converting the raw data into natural language text; extracting the key text from the natural language text to obtain the key text, and the key text includes: at least one of the content theme, core viewpoint, keyword, and key entity of the raw data.
  • converting the original data into natural language text includes: processing the original data through a prompt (a natural language model) template to obtain natural language text.
  • the prompt template is pre-trained and can convert the original data into natural language text. If the original data is an image or video (including multiple frames of images), the image or video can be input into a pre-trained multimodal recognition model for processing, and a natural language text describing the corresponding image or video can be obtained.
  • key text is extracted from the natural language text to obtain the key text. It includes: inputting natural language text into a unified model to extract key text and obtain key text.
  • the unified model includes multiple pre-trained sub-models, which can realize the unification of multiple NLP (natural language) tasks, such as matching sub-models, classification sub-models or sorting sub-models.
  • the matching sub-model is used to realize the matching task
  • the classification sub-model is used to realize the classification task
  • the sorting sub-model is used to realize the sorting task.
  • the natural language text is input into the unified model, and at least one key text can be obtained through the processing of each sub-model.
  • the unified model can extract the core contents of the original data, such as content themes, core ideas, keywords, key entities, etc.
  • content themes such as whether the original data is about goods or quality
  • core ideas such as whether the original data is positive data or negative data
  • keywords such as keywords in the original data that describe the content theme attributes
  • key entities such as location, time, product name or product category, etc.
  • the key text can be used as the key of the key-value pair
  • the natural language text or original data can be used as the value of the key-value pair to obtain the key-value pair, thereby constructing a question-answering knowledge base.
  • one piece of original data corresponds to one piece of natural language text
  • one piece of natural language text can correspond to one key, which is one key text in the key text of the original data or a combination of multiple key texts.
  • the original data is X
  • the natural text corresponding to the original data is N
  • the key text corresponding to the original data includes topic A, core view B, keyword C, keyword D, key entity E and key entity F.
  • the key-value pairs included in the constructed question-answering knowledge base are shown in FIG3 .
  • the value of the key-value pair is determined based on the original data, including: segmenting the original data based on the key text to obtain data segments corresponding to the key text, the mode of the data segments is the same as the mode of the original data; and determining the value of the key-value pair based on the data segments.
  • the original data is text
  • the original data is segmented based on the key text to obtain data segments corresponding to the key text, including: using machine reading comprehension technology (MRC, Machine Reading Comprehension) to extract data segments describing the key text from the original data.
  • MRC Machine Reading Comprehension
  • the key text and the corresponding raw data are input into the understanding model based on machine reading comprehension technology, and the data fragments describing the key text are output.
  • the understanding model can remove the redundant noise data in the raw data, thereby improving the overall quality of the data fragments.
  • a key text has a corresponding data fragment in the raw data.
  • the original data is "the parameters of this projector are body weight 1kg, zoom multiple is fixed focus, and light source type is LED light source”
  • the key texts obtained are: projector, weight, zoom multiple, fixed focus, light source type, LED light source.
  • the data segment corresponding to "projector” can be the entire original data
  • the data segment corresponding to weight can be "body weight 1kg”
  • the data segment corresponding to zoom multiple and/or fixed focus is zoom multiple is fixed focus
  • the data segment corresponding to light source type and/or LED light source is light source type is LED light source.
  • the entire image can be used as the value of the key-value pair, or the original data can be segmented, and the local image obtained by segmentation is the data fragment as the value of the key-value pair.
  • the segmentation method can be through image recognition or image processing technology.
  • the object contained in an image is a set of tableware. If the key text is “spoon”, the partial image containing the spoon in the image can be used as the data segment of the “spoon”.
  • the entire video can be used as the value of the key-value pair, or a portion of the frame image in the video can be used as the value of the key-value pair, or a local image in the image in the video can be used as the value of the key-value pair.
  • the specific segmentation method of the image and video is not limited.
  • determining the key of the key-value pair according to the key text includes: expanding the key text to obtain a first inquiry text; and determining the first inquiry text as the key of the key-value pair.
  • one or more key texts can be expanded to obtain a first inquiry text.
  • the corresponding key texts include topic A, core view B, keyword C, keyword D, key entity E, and key entity F.
  • topic A can be expanded to obtain the corresponding first inquiry text
  • topic A and core view B can be expanded to obtain the corresponding first inquiry text.
  • one original data can correspond to multiple first inquiry texts.
  • the first inquiry text is determined as the key of the key-value pair, which is the index.
  • the key text may be expanded by inputting the key text into a pre-trained expansion model, so that the key text is expanded into a natural language inquiry text.
  • the expanded first inquiry text may be "How much does the projector weigh?" It can be understood that the expanded first inquiry text includes the key text.
  • the method further includes: encoding the first query text to obtain a coding vector; and determining the coding vector as a key in the key-value pair.
  • the first inquiry text is encoded using an encoder of a pre-trained BERT language model, the first inquiry text is input into the encoder for encoding, and the output is the encoding vector of the first inquiry text.
  • it also includes: performing data mining on the original data to obtain target data; generating a second query text of the target data based on the target data; determining the target data as the value of the key-value pair and determining the second query text as the key of the key-value pair to construct a question-and-answer knowledge base.
  • the data mined from the original data can be combined into key-value pairs and added to the question-answer knowledge base constructed as described above, thereby increasing the amount of data in the question-answer knowledge base.
  • user comment data is mined, and the target data obtained is comment data, and multiple comment data constitute the comment knowledge base.
  • Product attribute data is mined, and the target data obtained is product data, and multiple product data constitute the product knowledge base.
  • Other data is mined, and the target data obtained is general data, and multiple general data constitute the general knowledge base.
  • the key of the target data is determined, and the target data is used as the value, so that the key-value pair can be obtained and added to the question-answer knowledge base constructed in the above manner.
  • the key of the question-answering knowledge base may be a text or a coded vector, but the value corresponding to the key may be a text, an image or a video.
  • the encoding vector b is the encoding vector of the query text a
  • the corresponding value of the two is the text g.
  • the encoding vector d is the encoding vector of the query text c
  • the corresponding value of the two is the image h.
  • the encoding vector f is the encoding vector of the query text e
  • the corresponding value of the two is the video k.
  • the method for constructing a question-and-answer knowledge base can solve the problem of unified retrieval of answer data of different modalities. At the same time, it has strong scalability and can better solve the problem that the method for constructing a question-and-answer knowledge base in related technologies is not flexible enough.
  • This application can quickly construct a question-and-answer knowledge base of 100 million levels, thereby improving the retrieval coverage of online inquiry data.
  • FIG5 is a flowchart of a data retrieval method provided by an exemplary embodiment of the present application, which is applied to a server. Specifically, the following steps are included:
  • the query data is one of text, image or video. If it is an image or video, the image or video can be converted into a query text, and the query text can describe the content of the image or video.
  • the mode of the answer data is one of text, image or video
  • the question-answer knowledge base is constructed according to the above-mentioned knowledge base construction method.
  • answer data corresponding to the query data is retrieved, including: determining that the value in a key-value pair of a key determined based on the query data is the answer data; and/or encoding the query data to obtain a query encoding vector; in the question-and-answer knowledge base, determining a target encoding vector whose similarity with the encoding vector is greater than a threshold; in the question-and-answer knowledge base, determining that the value of the key-value pair with the target encoding vector as the key is the answer data.
  • determining that the value in the key-value pair of the key determined based on the query data is the answer data includes: if the query data sent by the terminal device is text, the corresponding value can be retrieved as the answer data in the question and answer knowledge base using the query data as the key; if the corresponding value cannot be retrieved, the query data is encoded to obtain a coding vector, and the corresponding value is retrieved as the answer data in the question and answer knowledge base using the coding vector as the key.
  • the query data is an image or video
  • the image or video can be extracted and processed to obtain the corresponding query text, and then the query text is used as a key or the encoding vector of the query text is used as a key to retrieve the corresponding value as the answer data.
  • the server can retrieve answer data for the query data based on the knowledge base to provide high-quality answers to the user.
  • FIG6 is a flowchart of another data retrieval method provided by an exemplary embodiment of the present application. Applied to a terminal device, the method specifically includes the following steps:
  • the query data is one of text, image or video.
  • the answer data is determined according to the above-mentioned data retrieval method.
  • a knowledge base construction device 70 in addition to providing a method for constructing a knowledge base, a knowledge base construction device 70 is also provided.
  • the knowledge base construction device 70 includes:
  • An acquisition module 71 is used to acquire multiple pieces of original data, where the modalities of the multiple pieces of original data are at least two of text, image or video;
  • a determination module 72 for determining key text of the original data among the plurality of original data, and determining a key of a key-value pair according to the key text, and determining a value of the key-value pair according to the original data, wherein the key text is used to describe the content of the original data;
  • the construction module 73 is used to construct a question-answer knowledge base according to the key-value pairs corresponding to the multiple original data.
  • the determination module 72 is specifically used to: convert the original data into natural language text; extract key text from the natural language text to obtain key text, and the key text includes: at least one of the content theme, core ideas, keywords, and key entities of the original data.
  • the determination module 72 when extracting key text from natural language text to obtain key text, is specifically used to: input the natural language text into a unified model to extract key text to obtain key text.
  • the determination module 72 is specifically used to: segment the original data based on the key text to obtain data segments corresponding to the key text, and the mode of the data segments is the same as the mode of the original data; determine the value of the key-value pair based on the data segments.
  • the original data is text
  • the determination module 72 segments the original data based on the key text to obtain data segments corresponding to the key text, specifically for: using machine reading comprehension technology to extract data segments describing the key text from the original data.
  • the determination module 72 is specifically used to: expand the key text to obtain a first query text; and determine that the first query text is a key of the key-value pair.
  • the determination module 72 is further used to encode the first query text to obtain an encoding vector; and determine the encoding vector as a key in the key-value pair.
  • the determination module 72 is also used to perform data mining on the original data to obtain target data; generate a second query text for the target data based on the target data; determine the target data as the value of the key-value pair and determine the second query text as the key of the key-value pair to construct a question-and-answer knowledge base.
  • a data retrieval device (not shown) is also provided, and the data retrieval device includes:
  • a receiving module used to receive inquiry data sent by a terminal device, where the inquiry data is one of text, image or video;
  • a retrieval module is used to retrieve answer data corresponding to the query data in the question-answer knowledge base, where the mode of the answer data is one of text, image or video.
  • the question-answer knowledge base is constructed according to the above-mentioned knowledge base construction method;
  • the sending module is used to send answer data to the terminal device.
  • the retrieval module is specifically used to: determine that a value in a key-value pair of a key determined based on the query data is answer data;
  • encode the query data to obtain a query encoding vector; in the question-answering knowledge base, determine a target encoding vector whose similarity with the encoding vector is greater than a threshold; in the question-answering knowledge base, determine that the value of the key-value pair with the target encoding vector as the key is the answer data.
  • the data retrieval device includes:
  • a sending module used for sending query data to the server
  • the receiving module is used to receive answer data of the inquiry data sent by the server, and the answer data is determined by the above-mentioned data retrieval method.
  • a knowledge base construction device which is applied to an intelligent customer service scenario, by obtaining multiple pieces of original data, the modalities of the multiple pieces of original data are at least two of text, image or video; for the original data in the multiple pieces of original data, the key text of the original data is determined, and the key of the key-value pair is determined according to the key text, and the value of the key-value pair is determined according to the original data, and the key text is used to describe the content of the original data; according to the key-value pairs corresponding to the multiple pieces of original data, a question-value pairs corresponding to the multiple pieces of original data, a question-and-answer knowledge base is constructed, and a question-and-answer knowledge base that supports answer retrieval in different modalities can be constructed.
  • FIG8 is a schematic diagram of the structure of a cloud device 80 provided by an exemplary embodiment of the present application.
  • the cloud device 80 is used to run the above-mentioned knowledge base construction method or image processing method.
  • the cloud device includes: a memory 84 and a processor 85 .
  • the memory 84 is used to store computer programs and can be configured to store various other information to support operations on the cloud device.
  • the memory 84 can be an object storage service (OSS).
  • OSS object storage service
  • the memory 84 can be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable programmable read-only memory
  • PROM programmable read-only memory
  • ROM read-only memory
  • magnetic memory magnetic memory
  • flash memory magnetic disk or optical disk.
  • the processor 85 is coupled to the memory 84 and is used to execute the computer program in the memory 84 to: obtain multiple pieces of original data, where the modalities of the multiple pieces of original data are at least two of text, image, or video; determine key text of the original data for the original data in the multiple pieces of original data, determine the key of the key-value pair according to the key text, and determine the value of the key-value pair according to the original data, where the key text is used to describe the content of the original data; and determine the value of the key-value pair according to the multiple pieces of original data. According to the corresponding key-value pairs, a question-answer knowledge base is constructed.
  • the processor 85 is specifically used to: convert the original data into natural language text; extract the key text from the natural language text to obtain the key text, and the key text includes: at least one of the content theme, core ideas, keywords, and key entities of the original data.
  • the processor 85 extracts key text from the natural language text to obtain the key text, it is specifically used to: input the natural language text into a unified model to extract the key text to obtain the key text.
  • the processor 85 when determining the value of the key-value pair based on the original data, is specifically used to: segment the original data based on the key text to obtain data segments corresponding to the key text, the mode of the data segments is the same as the mode of the original data; and determine the value of the key-value pair based on the data segments.
  • the original data is text
  • the processor 85 segments the original data based on the key text to obtain data segments corresponding to the key text, it is specifically used to: use machine reading comprehension technology to extract data segments describing the key text from the original data.
  • the processor 85 is further used to: encode the first query text to obtain a coding vector; and determine the coding vector as a key in the key-value pair.
  • the processor 85 is also used to perform data mining on the original data to obtain target data; generate a second query text of the target data based on the target data; determine the target data as the value of the key-value pair and determine the second query text as the key of the key-value pair to construct a question and answer knowledge base.
  • the processor 85 is coupled to the memory 84, and is used to execute the computer program in the memory 84, so as to: receive query data sent by a terminal device, the query data being one of text, image or video; retrieve answer data corresponding to the query data in a question and answer knowledge base, the modality of the answer data being one of text, image or video, and the question and answer knowledge base is constructed according to any of the above-mentioned knowledge base construction methods; and send the answer data to the terminal device.
  • the processor 85 retrieves answer data corresponding to the query data in the question and answer knowledge base, it is specifically used to: determine that the value in the key-value pair of the key determined based on the query data is the answer data; and/or, encode the query data to obtain a query encoding vector; determine in the question and answer knowledge base a target encoding vector whose similarity with the encoding vector is greater than a threshold.
  • the processor 85 is coupled to the memory 84 and is used to execute the computer program in the memory 84 to: send query data to the server; receive answer data of the query data sent by the server, and the answer data is determined according to the above-mentioned data retrieval method.
  • the cloud device also includes other components such as a firewall 81, a load balancer 82, a communication component 86, and a power supply component 83.
  • Fig. 8 only schematically shows some components, which does not mean that the cloud device only includes the components shown in Fig. 8 .
  • the cloud device provided in the embodiment of the present application can obtain a compressed visual network model, which occupies a smaller memory and has a faster computing efficiency without affecting the recognition accuracy.
  • the embodiment of the present application also provides a computer-readable storage medium storing a computer program.
  • the processor is caused to implement the steps in the above-mentioned method.
  • an embodiment of the present application also provides a computer program product, including a computer program/instruction.
  • the processor is caused to implement the steps in the method shown above.
  • the communication component of Figure 8 above is configured to facilitate wired or wireless communication between the device where the communication component is located and other devices.
  • the device where the communication component is located can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G/LTE, 5G and other mobile communication networks, or a combination thereof.
  • the communication component receives a broadcast signal or broadcast-related text from an external broadcast management system via a broadcast channel.
  • the communication component also includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared information association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared information association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • the power supply assembly of Figure 8 provides power to various components of the device in which the power supply assembly is located.
  • the power supply assembly may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device in which the power supply assembly is located.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.
  • the above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium.
  • the above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute some steps of the methods of each embodiment of the present application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Artificial Intelligence (AREA)
  • Human Computer Interaction (AREA)
  • Software Systems (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

La présente invention concerne un procédé de construction de base de connaissances, un procédé et un appareil de récupération de données, et un dispositif en nuage. Le procédé de construction de base de connaissances consiste à : obtenir une pluralité d'éléments de données d'origine, les modalités de la pluralité d'éléments de données d'origine étant au moins deux parmi : un texte, une image ou une vidéo ; pour des données d'origine dans la pluralité d'éléments de données d'origine, déterminer un texte de clé des données d'origine, déterminer la clé d'une paire clé-valeur selon le texte de clé, et déterminer la valeur de la paire clé-valeur en fonction des données d'origine, le texte de clé étant utilisé pour décrire le contenu des données d'origine ; selon les paires clé-valeur correspondant à la pluralité d'éléments de données d'origine, construire une base de connaissances de questions et de réponses, ce qui permet de construire une base de connaissances de questions et de réponses prenant en charge différentes modalités de récupération de réponses.
PCT/CN2024/073350 2023-02-13 2024-01-19 Procédé de construction de base de connaissances, procédé et appareil de récupération de données, et dispositif en nuage WO2024169529A1 (fr)

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CN116340479A (zh) * 2023-02-13 2023-06-27 阿里巴巴（中国）有限公司 知识库的构建方法、数据检索方法、装置和云设备

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CN111177532A (zh) * 2019-12-02 2020-05-19 平安资产管理有限责任公司 一种垂直搜索方法、装置、计算机系统及可读存储介质
CN113077893A (zh) * 2021-04-23 2021-07-06 上海理工大学 一种智能辅具适配决策系统及方法
CN116340479A (zh) * 2023-02-13 2023-06-27 阿里巴巴（中国）有限公司 知识库的构建方法、数据检索方法、装置和云设备

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111177532A (zh) * 2019-12-02 2020-05-19 平安资产管理有限责任公司 一种垂直搜索方法、装置、计算机系统及可读存储介质
CN113077893A (zh) * 2021-04-23 2021-07-06 上海理工大学 一种智能辅具适配决策系统及方法
CN116340479A (zh) * 2023-02-13 2023-06-27 阿里巴巴（中国）有限公司 知识库的构建方法、数据检索方法、装置和云设备

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