Information query method and apparatus, and computer-readable storage medium

Abstract

The present disclosure relates to the technical field of computers, and relates to an information query method and apparatus, and a computer-readable storage medium. The information query method comprises: on the basis of relative time information comprised in first information, generating second information that corresponds to the first information and comprises absolute time information, wherein the relative time information and the absolute time information indicate the same time point; on the basis of time information in a first query request, determining from the second information information related to the first query request; and on the basis of the first information corresponding to the related information, determining a query result.

Description

Information query method, device and computer readable storage medium TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, and particularly relates to an information query method, an information query device, a computer readable storage medium and a computer program product. BACKGROUND

[0002] In many application scenarios of computer technology, it is often necessary to query stored content according to time information. For example, it is a common business scenario to recall and retrieve according to time information contained in conference recording, personal input information and the like.

[0003] In the related art, the information about the time interval mentioned in the query request needs to be accurately retrieved and involved in the query result.

[0004] SUMMARY

[0005] According to some embodiments of the present disclosure, an information query method is provided, including: generating second information including absolute time information corresponding to first information according to relative time information included in the first information, the relative time information and the absolute time information indicating the same time point; determining information related to a first query request from the second information according to time information in the first query request; and determining a query result according to the first information corresponding to the related information.

[0006] In some embodiments, generating the second information including the absolute time information corresponding to the first information according to the relative time information included in the first information includes: determining the absolute time information according to a generation time of the first information and the relative time information.

[0007] In some embodiments, generating the second information including the absolute time information corresponding to the first information according to the relative time information included in the first information includes: generating the second information based on the first information by using a first machine learning model.

[0008] In some embodiments, generating the second information including the absolute time information corresponding to the first information according to the relative time information included in the first information includes: screening time-related content from the first information; determining whether the content includes the relative time information by using a second machine learning model; and generating the second information in response to the content including the relative time information.

[0009] In some embodiments, screening the time-related content from the first information includes: screening content including a specified keyword from the first information by using a word list.

[0010] In some embodiments, determining the related information from the second information according to the time information in the first query request comprises: generating a second query request comprising absolute time information according to the relative time information comprised in the first query request; and determining the related information according to the second query request.

[0011] In some embodiments, determining the related information according to the second query request comprises: determining the related information according to the keyword in the second query request and a feature vector of the second query request.

[0012] In some embodiments, determining the query result according to the first information corresponding to the related information comprises: generating the query result by using a third machine learning model according to the corresponding first information.

[0013] According to some other embodiments of the present disclosure, an information query apparatus is provided, comprising: a generation unit configured to generate second information corresponding to first information and comprising absolute time information according to relative time information comprised in the first information, the relative time information and the absolute time information indicating a same time point; a query unit configured to determine information related to a first query request from the second information according to time information in the first query request; and a determination unit configured to determine a query result according to first information corresponding to the related information.

[0014] In some embodiments, the generation unit determines the absolute time information according to a generation time of the first information and the relative time information.

[0015] In some embodiments, the generation unit generates the second information by using a first machine learning model based on the first information.

[0016] In some embodiments, the generation unit filters out content related to time from the first information; determines whether the content comprises relative time information by using a second machine learning model; and generates the second information in response to the content comprising the relative time information.

[0017] In some embodiments, the generation unit filters out content comprising a specified keyword from the first information by using a keyword list.

[0018] In some embodiments, the query unit generates a second query request comprising absolute time information according to the relative time information comprised in the first query request; and determines the related information according to the second query request.

[0019] In some embodiments, the query unit determines the related information according to the keyword in the second query request and a feature vector of the second query request.

[0020] In some embodiments, the query unit generates the query result by using a third machine learning model according to the corresponding first information.

[0021] According to yet some embodiments of the present disclosure, there is provided an information query apparatus, comprising: a memory; and a processor coupled to the memory, the processor being configured to perform the information query method according to any one of the above embodiments based on instructions stored in the memory.

[0022] According to still some embodiments of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program, the program, when executed by a processor, implementing the information query method according to any one of the above embodiments.

[0023] According to still some embodiments of the present disclosure, there is also provided a computer program product comprising instructions which, when executed by a processor, cause the processor to perform the information query method according to any one of the above embodiments.

[0024] Other features and advantages of the present disclosure will be apparent from the following detailed description of exemplary embodiments of the present disclosure, taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0025] The accompanying drawings, which are included to provide a further understanding of the present disclosure, constitute a part of this application and illustrate exemplary embodiments of the present disclosure and together with the description serve to explain the present disclosure. In the drawings:

[0026] FIG. 1 shows a flow chart of an information query method according to some embodiments of the present disclosure;

[0027] FIG. 2 shows a flow chart of an information query method according to some other embodiments of the present disclosure;

[0028] FIG. 3 shows a flow chart of an information query method according to yet some embodiments of the present disclosure;

[0029] FIG. 4 shows a block diagram of an information query apparatus according to some embodiments of the present disclosure;

[0030] FIG. 5 shows a block diagram of an information query apparatus according to some other embodiments of the present disclosure;

[0031] FIG. 6 shows a block diagram of an information query apparatus according to yet some embodiments of the present disclosure. DETAILED DESCRIPTION

[0032] With reference to the drawings and embodiments of the present disclosure, the technical solutions in the embodiments of the present disclosure will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure. The following description of at least one example embodiment is merely illustrative in nature and is in no way limiting on the disclosure or its application or uses. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present disclosure.

[0033] The relative arrangement, numerical expressions, and numerical values of the components and steps set forth in the embodiments are not limiting on the scope of the present disclosure unless otherwise specifically stated. Meanwhile, it should be understood that the sizes of the various parts shown in the drawings are not drawn in accordance with the actual proportional relationship for the convenience of description. The techniques, methods, and devices known to those skilled in the related art can not be discussed in detail, but should be considered as part of the authorized description under appropriate circumstances. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary, not as a limitation. Therefore, other examples of the example embodiments can have different values. It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0034] As mentioned above, the retrieval based on the time interval mentioned in the query request can only query the content containing absolute time information, and cannot query the content containing relative time information.

[0035] For example, for a meeting held at a certain time point, the meeting record information "Today (20XX-6-18) meeting, the content mentions the xx project technical solution review held last Tuesday, and the development plan is to be completed in the first week of next month, and the test can be submitted" is entered; the user initiates a query request "What technical solution review do we have in the first week of this month" or "What delivery plan do we have next month" for the meeting record information at the end of June.

[0036] It can be seen that such query requests include a relatively vague time range query, i.e. information query based on relative time information, and the related technology is difficult to query accurate content.

[0037] The inventors of the present disclosure found that the above-mentioned related technology has the following problems: the accuracy of the query result is low. In view of this, the present disclosure proposes an information query technical solution, which can improve the accuracy of the query result.

[0038] FIG. 1 shows a flowchart of an information query method according to some embodiments of the present disclosure.

[0039] As shown in FIG. 1, in step 110, according to relative time information included in the first information, second information including absolute time information corresponding to the first information is generated, and the relative time information and the absolute time information indicate the same time point. For example, the first information can include text information, voice information, video information, etc.

[0040] For example, the relative time information can include time information indicating a target time point by a time interval from a specified time point, such as "last week", "next month", "two hours ago", etc.; and the absolute time information can include a determined time point, such as "x month x day", "x o'clock x minute", etc.

[0041] In step 120, according to the time information in the first query request, information related to the first query request is determined from the second information.

[0042] For example, the first information and the second information generated according to the first information can be determined to have a corresponding relationship, and the two pieces of information are stored after being associated.

[0043] In step 130, according to the first information corresponding to the related information, the query result is determined.

[0044] For example, after it is determined in step 120 that a certain second information is the information related to the first query request, the first information corresponding to the second information can be determined according to the corresponding relationship between each first information and each second information stored in advance.

[0045] In the above embodiment, the first information containing relative time information is converted into second information containing absolute time information as the basis for query; and based on the corresponding relationship between the first information and the second information, the first information satisfying the query request is found. In this way, even if the information contains relative time information, the query result can still be accurately obtained, thereby improving the accuracy.

[0046] In the following, the generation method of the second information including absolute time information is exemplarily illustrated through some embodiments.

[0047] In some embodiments, according to the generation time of the first information and the relative time information, the absolute time information is determined. For example, the generation time of the first information (such as the input time of the conference record, the creation time of the file where the first information is located, etc.) can be recorded as the time base point of the relative time information; and the relative time information in the first information is added or subtracted from the time base point to calculate the absolute time information.

[0048] For example, a user enters a meeting record "xx project technical scheme review held last Tuesday, plan to complete development in the first week of next month, can submit test" as the first information on June 18, 20XX. The generation time of the first information is June 18, 20XX, which includes relative time information "last Tuesday" and "the first week of next month". It can be determined that the absolute time information corresponding to "last Tuesday" is June 4, 20XX, and the absolute time information corresponding to "the first week of next month" is from July 1, 20XX to July 7, 20XX.

[0049] In this way, the relative time information can be converted into absolute time information as a query basis, so that the query result can be accurately obtained.

[0050] In some embodiments, based on the first information, the second information is generated by using a first machine learning model. For example, after obtaining the absolute time information, the second information including the absolute time information can be regenerated in an artificial intelligence manner.

[0051] For example, the first information "xx project technical scheme review held last Tuesday, plan to complete development in the first week of next month, can submit test" including the relative time information "last Tuesday" and "the first week of next month" can be input into the machine learning model, and rewritten into the second information "xx project technical scheme review held on June 4, 20XX, plan to complete development in the week from July 1, 20XX to July 7, 20XX, can submit test" including the absolute time information "June 4, 20XX" and "from July 1, 20XX to July 7, 20XX".

[0052] In this way, the rewriting of information in an artificial intelligence manner can make the expression of the second information including the absolute time information more accurate, so that the accuracy of the query can be improved based on the query basis.

[0053] The screening method of the first information before generating the second information is exemplarily illustrated by some embodiments.

[0054] In some embodiments, the content related to time is screened from the first information; whether the content includes the relative time information is determined by using a second machine learning model; and the second information is generated in response to the content including the relative time information. For example, the content including the specified keyword is screened from the first information by using a word list.

[0055] For example, the first information can include text information, voice information, video information, etc. In response to the first information being text information, the text information can be first sentence segmented, and each sentence can be filtered; in response to the first information being voice information, the voice information can be segmented to obtain the voice of each sentence, and the voice of each sentence can be filtered; in response to the first information being voice information, the voice information can also be voice recognized to obtain voice recognition results, and then the voice recognition results can be sentence segmented, and each sentence can be filtered; in response to the first information being video information, the voice information in the video information can be segmented to obtain the voice of each sentence, and the voice of each sentence can be filtered; in response to the first information being video information, the voice information in the video information can also be voice recognized to obtain voice recognition results, and then the voice recognition results can be sentence segmented, and each sentence can be filtered.

[0056] For example, first, the first information can be sentence segmented to obtain a plurality of sentences; then, through a word list, sentences related to time in these sentences can be filtered out as candidate content. For example, sentences containing specified keywords related to time points such as days, days, months, weeks, etc. can be filtered out as candidate content.

[0057] In this way, the information for absolute time information conversion can be reduced, thereby improving the efficiency of the query and reducing the cost.

[0058] For example, a second machine learning model smaller than the first machine learning model and the third machine learning model can be used to determine whether the candidate content contains relative time information; the content containing relative time information can be used as an object for absolute time information conversion.

[0059] In this way, the content that includes specified keywords related to time (such as “every day” and the like) but does not include relative time information can be filtered out, thereby improving the efficiency of the query and reducing the cost.

[0060] The determination method of information related to the first query request will be described below by way of some embodiments.

[0061] In some embodiments, according to the relative time information included in the first query request, a second query request including absolute time information is generated; and according to the second query request, the related information is determined. For example, the absolute time information can be determined according to the generation time of the first query request and the relative time information therein. For example, the generation time of the first query request can be taken as the time base point of the relative time information; and the absolute time information can be calculated by adding or subtracting the time base point from the relative time information.

[0062] For example, a user initiates a first query request "What technical scheme reviews do we have in the beginning of this month" at the end of June, 20XX. The generation time of the first query request is the end of June, 20XX, which includes the relative time information "in the beginning of this month". It can be determined that the absolute time information corresponding to "in the beginning of this month" is "from June 1, 20XX to June 10, 20XX".

[0063] In this way, the relative time information in the query request can be converted into absolute time information, thereby improving the accuracy of the query.

[0064] In some embodiments, based on the first query request, a second query request is generated by using a first machine learning model. For example, after obtaining the absolute time information, a second query request including the absolute time information can be regenerated in an artificial intelligence manner.

[0065] For example, the first query request "What technical scheme reviews do we have in the beginning of this month" including the relative time information "in the beginning of this month" can be input into the machine learning model and rewritten into the second query request "What technical scheme reviews do we have from June 1, 20XX to June 10, 20XX" including the absolute time information "from June 1, 20XX to June 10, 20XX".

[0066] In this way, the rewriting of information in an artificial intelligence manner can make the expression of the second query request including the absolute time information more accurate, thereby improving the accuracy of the query.

[0067] In some embodiments, according to the keywords in the second query request and the feature vector of the second query request, relevant information is determined. For example, according to the keywords and the feature vector in the second query request and the keywords and the feature vector of each second information, a similarity query can be performed, and the second information with the highest similarity to the second query request is determined as the relevant information.

[0068] For example, a plurality of relevant information can be determined as the basis for determining the query result in combination with the keywords and the feature vector.

[0069] In this way, the query in combination with the keywords and the feature vector realizes multi-way recall of the query request, thereby improving the accuracy of the query.

[0070] In the following, some embodiments are exemplarily described to illustrate the determination method of the query result.

[0071] In some embodiments, according to the corresponding first information, a third machine learning model is used to generate the query result. For example, the reply information corresponding to the query request can be generated according to the corresponding first information in an artificial intelligence manner.

[0072] For example, the first machine learning model and the third machine learning model can be the same machine learning model, or they can be two different machine learning models.

[0073] In the above embodiment, the first information containing relative time information is converted into second information containing absolute time information as the basis for the query; and based on the correspondence between the first and second information, the first information that satisfies the query request is found. In this way, even if the information contains relative time information, the query result can still be obtained accurately, thereby improving accuracy.

[0074] The information retrieval method of this disclosure is illustrated below with reference to some embodiments shown in Figures 2 and 3.

[0075] Figure 2 shows a flowchart of an information query method according to some other embodiments of the present disclosure.

[0076] As shown in Figure 2, during the production stage of the first information that serves as the query object, the two time information items can be pre-processed during the information entry or embedding process: the generation time (e.g., data entry) of the first information is recorded as meta information; the relative time information mentioned in the first information is converted into absolute time information, and the converted second information is recorded as the mapping information or attribute information of the first information to store the correspondence between the two. For example, this can be achieved through the following steps.

[0077] In step 210, the first information (i.e., the original text to be queried) can be segmented into multiple sentences. For example, the generation time of the first information can be recorded as the time base point for relative time information.

[0078] The following methods can be used to filter statements, such as vocabularies and second machine learning models, to reduce the number of statements that need to be processed, thereby improving query performance and reducing costs.

[0079] In step 220, a vocabulary can be used to filter out time-related statements from these sentences as candidate content. For example, statements containing specific keywords related to time points such as day, month, and week can be filtered out as candidate content.

[0080] This reduces the amount of information that needs to be converted to absolute time information, thereby improving query efficiency and reducing costs.

[0081] In step 230, a second machine learning model, smaller in scale than the first and third machine learning models, can be used to determine whether these candidate contents contain relative time information; the contents containing relative time information are used as objects for absolute time information conversion.

[0082] In this way, it is possible to filter out content that includes specified time-related keywords (such as "Making Progress Every Day", etc.) but does not include relative time information, thereby improving the query efficiency and reducing costs.

[0083] In step 240, the first machine learning model can be used to determine the absolute time information corresponding to the relative time information and generate the second information (i.e., the rewritten statement).

[0084] In this way, by rewriting information in an artificial intelligence manner, the expression of the second information including absolute time information can be made more accurate, and based on this as the query basis, the query accuracy can be improved.

[0085] In step 250, the generated second information and its corresponding first information are stored in the database, and the corresponding relationship between the two is established.

[0086] In this way, the relative time information can be converted into absolute time information as the query basis, so as to accurately obtain the query result.

[0087] In the above embodiment, the first information including relative time information is converted into the second information including absolute time information as the query basis; and based on the corresponding relationship between the first information and the second information, the first information that meets the query request is found. In this way, even if the information includes relative time information, the query result can still be accurately obtained, thereby improving the accuracy.

[0088] FIG. 3 shows a flowchart of an information query method according to some further embodiments of the present disclosure.

[0089] As shown in FIG. 3, in the consumption stage of the first information as the query object: in response to the user's query request involving time-related content, the relative time information is rewritten into absolute time information, and then a similarity query is performed based on the second information to determine the recall content of this query; the recall content is sent to the third machine learning model for processing; after the processing is completed, the saved first information is returned to the user. For example, it can be implemented through the following steps.

[0090] In step 310, it is judged whether the first query request is time-related.

[0091] In step 320, in response to the first query request being time-related and including relative time information, the relative time information is rewritten into absolute time information to generate a second query request.

[0092] This allows the relative time information in the query request to be converted into absolute time information, thereby improving the accuracy of the query; and by rewriting the information through artificial intelligence, the expression of the second query request, which includes absolute time information, can be made more accurate, thereby improving the accuracy of the query.

[0093] In step 330, a second query request including absolute time information is used to perform multi-path recall processing including feature vector recall and keyword recall to determine information related to the first query request.

[0094] By combining keywords and feature vectors for querying, multi-path recall of query requests is achieved, thereby improving the accuracy of the query.

[0095] In step 340, the information related to the first query request is sent to a third machine learning model for unified processing and then returned to the user. For example, multi-path recall might recall relevant second information based on the time information mentioned by the user, and return the first information corresponding to the second information to the user.

[0096] In the above embodiment, the first information containing relative time information is converted into second information containing absolute time information as the basis for the query; and based on the correspondence between the first and second information, the first information that satisfies the query request is found. In this way, even if the information contains relative time information, the query result can still be obtained accurately, thereby improving accuracy.

[0097] Figure 4 shows a block diagram of an information query device according to some embodiments of the present disclosure.

[0098] As shown in Figure 4, the information query device 4 includes: a generation unit 41, used to generate second information including absolute time information corresponding to the first information based on the relative time information included in the first information, wherein the relative time information and the absolute time information indicate the same time point; a query unit 42, used to determine information related to the first query request from the second information based on the time information in the first query request; and a determination unit 43, used to determine the query result based on the first information corresponding to the related information.

[0099] In some embodiments, the generation unit 41 determines the absolute time information based on the generation time and relative time information of the first information.

[0100] In some embodiments, the generation unit 41 generates second information based on the first information and using a first machine learning model.

[0101] In some embodiments, the generation unit 41 filters out time-related content from the first information; uses a second machine learning model to determine whether the content includes relative time information; and generates second information in response to the content including relative time information.

[0102] In some embodiments, the generation unit 41 uses a vocabulary to filter content that includes specified keywords from the first information.

[0103] In some embodiments, the query unit 42 generates a second query request including absolute time information based on the relative time information included in the first query request; and determines relevant information based on the second query request.

[0104] In some embodiments, the query unit 42 determines relevant information based on the keywords in the second query request and the feature vector of the second query request.

[0105] In some embodiments, the query unit 42 generates query results using a third machine learning model based on the corresponding first information.

[0106] In the above embodiment, the first information containing relative time information is converted into second information containing absolute time information as the basis for the query; and based on the correspondence between the first and second information, the first information that satisfies the query request is found. In this way, even if the information contains relative time information, the query result can still be obtained accurately, thereby improving accuracy.

[0107] Figure 5 shows a block diagram of an information query device according to other embodiments of the present disclosure.

[0108] As shown in FIG5, the information query device 5 of this embodiment includes: a memory 51 and a processor 52 coupled to the memory 51. The processor 52 is configured to execute the information query method in any embodiment of this disclosure based on the instructions stored in the memory 51.

[0109] The memory 51 may include, for example, system memory, fixed non-volatile storage media, etc. The system memory stores, for example, the operating system, application programs, boot loader, database, and other programs.

[0110] Figure 6 shows a block diagram of an information query device according to some embodiments of the present disclosure.

[0111] As shown in FIG6, the information query device 6 of this embodiment includes: a memory 610 and a processor 620 coupled to the memory 610. The processor 620 is configured to execute the information query method of any of the foregoing embodiments based on the instructions stored in the memory 610.

[0112] The memory 610 may include, for example, system memory, fixed non-volatile storage media, etc. The system memory may store, for example, the operating system, application programs, boot loader, and other programs.

[0113] Device 6 may also include an input / output interface 630, a network interface 640, and a storage interface 650. These interfaces 630, 640, and 650, as well as the memory 610 and processor 620, can be connected via, for example, a bus 660. The input / output interface 630 provides a connection interface for input / output devices such as a monitor, mouse, keyboard, touchscreen, microphone, and speakers. The network interface 640 provides a connection interface for various networked devices. The storage interface 650 provides a connection interface for external storage devices such as SD cards and USB flash drives.

[0114] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] The present disclosure has now been described in detail. To avoid obscuring the concept of this disclosure, some details known in the art have not been described. Those skilled in the art will fully understand how to implement the technical solutions disclosed herein based on the above description.

[0116] The methods and systems of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the specific order described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0117] While specific embodiments of this disclosure have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. An information query method, comprising: generating, according to relative time information included in first information, second information corresponding to the first information and including absolute time information, the relative time information and the absolute time information indicating a same time point; determining, according to time information in a first query request, information related to the first query request from the second information; determining a query result according to first information corresponding to the related information.

2. The information search method according to claim 1, wherein The generating, according to relative time information included in first information, second information corresponding to the first information and including absolute time information includes: determining the absolute time information according to a generation time of the first information and the relative time information.

3. The information search method according to claim 1 or 2, wherein The generating, according to relative time information included in first information, second information corresponding to the first information and including absolute time information includes: generating the second information based on the first information by using a first machine learning model.

4. The information search method according to any one of claims 1 to 3, wherein The generating, according to relative time information included in first information, second information corresponding to the first information and including absolute time information includes: screening, from the first information, content related to time; determining, by using a second machine learning model, whether the content includes the relative time information; generating the second information in response to the content including the relative time information.

5. The information query method of claim 4, wherein, The screening, from the first information, content related to time includes: screening, from the first information, the content including a specified keyword by using a word list.

6. The information search method according to any one of claims 1 to 5, wherein The determining, according to time information in a first query request, information related to the first query request from the second information includes: generating, according to relative time information included in the first query request, a second query request including absolute time information; determining the related information according to the second query request. The determining the related information according to the second query request includes:

7. The information query method of claim 6, wherein, determining the related information according to a keyword in the second query request and a feature vector of the second query request. The determining a query result according to first information corresponding to the related information includes:

8. The information search method according to any one of claims 1 to 7, wherein generating the query result according to the corresponding first information by using a third machine learning model. 9.An information query apparatus, comprising: a generating unit configured to generate, according to relative time information included in first information, second information corresponding to the first information and including absolute time information, the relative time information and the absolute time information indicating a same time point; a query unit configured to determine, according to time information in a first query request, information related to the first query request from the second information; a determining unit configured to determine a query result according to first information corresponding to the related information. 10.An information query apparatus, comprising: a memory; and a processor coupled to the memory and configured to perform the information query method of any one of claims 1-8 based on instructions stored in the memory. ​ 11. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the information query method according to any one of claims 1-8.

12. A computer program product comprising instructions which, when executed by a processor, cause the processor to carry out the information query method according to any one of claims 1-8.

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