CN112883186B - Method, system, equipment and storage medium for generating information map - Google Patents
Method, system, equipment and storage medium for generating information map Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a system, equipment and a storage medium for generating an information map. The information map generation method comprises the following steps: acquiring a plurality of first documents, wherein each first document comprises first image information and/or first text information; when the first document comprises first image information and first text information, extracting first image embedded features of the first image information by using a first model trained in advance, and extracting first text embedded features of the first text information by using a second model trained in advance; generating first concept embedded features according to the first image embedded features and the first text embedded features, and generating third coordinates of each first document according to the first concept embedded features; and generating a first information map of the first documents according to the third coordinates. The embodiment of the invention realizes visual information display.
Description
Technical Field
The embodiment of the invention relates to an information display technology, in particular to a method, a system, equipment and a storage medium for generating an information map.
Background
With the rapid development of internet technology and the increasing update and application of multimedia devices, the importance of information acquisition to people's life is increasingly highlighted.
However, in the process of acquiring information, the existing method cannot display the information which the user wants to acquire very intuitively. For example, a user wants to know all patent information of one company, and only patent information displayed in a list manner can be obtained, and for a huge and unordered amount of patent information, the user needs a lot of time and effort to obtain useful information desired by the user.
Even if a user can classify the patent information by himself so as to study, for the patent document comprising the text content and the drawing, the patent document cannot be classified by the text content or the drawing alone, so that the user can hardly and accurately obtain the information view which is needed and is intuitive by himself, and the work efficiency and the use experience of people are obviously greatly reduced.
Disclosure of Invention
The embodiment of the invention provides a method, a system, equipment and a storage medium for generating an information map so as to realize visual information display.
To achieve the object, an embodiment of the present invention provides a method for generating an information map, including:
Acquiring a plurality of first documents, wherein each first document comprises first image information and/or first text information;
when the first document comprises first image information, extracting first image embedded features of the first image information by using a first model trained in advance; when the first document comprises first text information, extracting first text embedding characteristics of the first text information by using a pre-trained second model; when the first document comprises first image information and first text information, extracting first image embedded features of the first image information by using a first model trained in advance, and extracting first text embedded features of the first text information by using a second model trained in advance;
when the first documents comprise first image information, generating first coordinates of each first document according to the first image embedded features; when the first documents comprise first text information, generating second coordinates of each first document according to the first text embedding characteristics; when the first document comprises first image information and first text information, generating first concept embedding features according to the first image embedding features and the first text embedding features, and generating third coordinates of each first document according to the first concept embedding features;
And generating a first information map of the plurality of first documents according to the first coordinates, the second coordinates or the third coordinates.
Further, the first information map is displayed in a scatter diagram, and the plurality of first documents are represented by points at a first coordinate, a second coordinate or a third coordinate position.
Further, the scattered points in the first information map have different colors, and the colors of the points are set in a distinguishing mode according to authors, patent applicants or patent rights of documents corresponding to the points, or the colors of the points are set in a distinguishing mode according to text information of the documents corresponding to the points.
Further, the display form of the first information map supports user interaction, and when the user selects, clicks or hovers at a certain point, image information and/or text information of a document corresponding to the point are displayed in a specific area of the page.
Further, the first information map is displayed in the form of an image list, each document is displayed in the form of a representative image, and the positions of the images are arranged according to the positions of the first coordinates, the second coordinates or the third coordinates of the corresponding document.
Further, the display form of the first information map supports user interaction, and when a certain area in the map selected by a user is received, the map in the selected area range is enlarged and displayed.
Further, the generating a first concept embedding feature according to the first image embedding feature and the first text embedding feature includes:
generating the first concept embedding feature according to the first image embedding feature and the first text embedding feature by utilizing a pre-trained third model, wherein a loss function used in the training process of the third model comprises a relative hinge loss function and/or an absolute hinge loss function.
Further, the generating the first information map of the plurality of first documents according to the first coordinates, the second coordinates or the third coordinates includes:
acquiring one or more second documents, wherein each second document comprises second image information and/or second text information;
extracting second image embedded features of the second image information by using a first model trained in advance when the second document comprises the second image information; extracting a second text embedding feature of the second text information by using a pre-trained second model when the second document comprises the second text information; when the second document comprises second image information and second text information, extracting second image embedded features of the second image information by using a pre-trained first model, and extracting second text embedded features of the second text information by using a pre-trained second model;
Generating fourth coordinates of each second document according to the second image embedded features when the second document comprises second image information; generating a fifth coordinate of each second document according to the second text embedding characteristic when the second document comprises second text information; when the second document comprises second image information and second text information, generating second concept embedded features according to the second image embedded features and the second text embedded features, and generating sixth coordinates of each second document according to the second concept embedded features;
and displaying the fourth coordinate, the fifth coordinate or the sixth coordinate in the first information map.
Further, the generating a second concept embedding feature from the second image embedding feature and the second text embedding feature includes:
generating the second concept embedded features according to the second image embedded features and the second text embedded features by using a pre-trained third model, wherein the loss function used in the training process of the third model comprises a relative hinge loss function and/or an absolute hinge loss function.
Further, the displaying the fourth coordinate, the fifth coordinate or the sixth coordinate in the first information map includes:
Acquiring a fourth coordinate, a fifth coordinate or a sixth coordinate, wherein the minimum distance between the fourth coordinate, the fifth coordinate or the sixth coordinate and the first coordinate, the second coordinate or the third coordinate is smaller than a preset value;
and highlighting the fourth coordinate, the fifth coordinate or the sixth coordinate of which the minimum distance is smaller than a preset value.
Further, the first model includes an image neural network and an image mapping neural network, and the second model includes a text neural network and a text mapping neural network.
Further, the extracting the first image embedded feature of the first image information using the first model trained in advance, and the extracting the first text embedded feature of the first text information using the second model trained in advance, includes:
extracting a first image vector of the first image information by using a pre-trained image neural network;
mapping the first image vector into a public space of image-text joint embedding by using a pre-trained image mapping neural network, and transforming the first image vector into a first image embedding feature;
extracting a first text vector of the first text information by using a pre-trained text neural network;
and mapping the first text vector into the public space of the graphic joint embedding by using a pre-trained text mapping neural network, and converting the first text vector into a first text embedding feature.
In one aspect, an embodiment of the present invention further provides a system for generating an information map, where the system for generating an information map includes:
the document acquisition module is used for acquiring a plurality of first documents, wherein each first document comprises first image information and/or first text information;
the feature extraction module is used for extracting first image embedded features of the first image information by using a pre-trained first model when the first document comprises the first image information; when the first document comprises first text information, extracting first text embedding characteristics of the first text information by using a pre-trained second model; when the first document comprises first image information and first text information, extracting first image embedded features of the first image information by using a first model trained in advance, and extracting first text embedded features of the first text information by using a second model trained in advance;
the coordinate generation module is used for generating a first coordinate of each first document according to the first image embedding characteristics when the first document comprises first image information; when the first documents comprise first text information, generating second coordinates of each first document according to the first text embedding characteristics; when the first document comprises first image information and first text information, generating first concept embedding features according to the first image embedding features and the first text embedding features, and generating third coordinates of each first document according to the first concept embedding features;
And the map generation module is used for generating first information maps of the first documents according to the first coordinates, the second coordinates or the third coordinates.
On the other hand, the embodiment of the invention also provides a generating device of the information map, which comprises: one or more processors; and a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a generating method as provided by any of the embodiments of the present invention.
In yet another aspect, an embodiment of the present invention further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a generating method as provided by any embodiment of the present invention.
According to the embodiment of the invention, a plurality of first documents are acquired, and each first document comprises first image information and/or first text information; when the first document comprises first image information and first text information, extracting first image embedded features of the first image information by using a first model trained in advance, and extracting first text embedded features of the first text information by using a second model trained in advance; generating first concept embedded features according to the first image embedded features and the first text embedded features, and generating third coordinates of each first document according to the first concept embedded features; and generating first information maps of the plurality of first documents according to the first coordinates, the second coordinates or the third coordinates, solving the problem that the existing information display mode is not visual enough and realizing the effect of displaying information intuitively.
Drawings
Fig. 1 is a method flowchart of a method for generating an information map according to an embodiment of the present invention;
FIG. 2 is a schematic view of a public space provided by a first embodiment of the present invention;
FIG. 3 is a schematic diagram of a first information map according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a first information map according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a first information map according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a first information map according to an embodiment of the present invention;
fig. 7 is a flowchart of a method for generating an information map according to a second embodiment of the present invention;
fig. 8 is a schematic structural diagram of an information map generating system according to a third embodiment of the present invention;
fig. 9 is a schematic structural diagram of an information map generating apparatus according to a fourth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are for purposes of illustration and not of limitation. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts steps as a sequential process, many of the steps may be implemented in parallel, concurrently, or with other steps. Furthermore, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Furthermore, the terms "first," "second," and the like, may be used herein to describe various directions, acts, steps, or elements, etc., but these directions, acts, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, a first document may be referred to as a second document, and similarly, a second document may be referred to as a first document, without departing from the scope of the present application. Both the first document and the second document are documents, but they are not the same document. The terms "first," "second," and the like, are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the embodiments of the present invention, the meaning of "plurality" is at least two, for example, two, three, etc., unless explicitly defined otherwise.
Example 1
As shown in fig. 1, a first embodiment of the present invention provides a method for generating an information map, where the method includes:
s110, acquiring a plurality of first documents, wherein each first document comprises first image information and/or first text information.
In this embodiment, the first document may be one or more of a patent document, a paper, a web page document, a journal document, and a book document, and the first document may include first image information, first text information, or both first image information and first text information. Specifically, the user may upload a plurality of first documents, where each first document may include a plurality of first text information and first image information.
S120, when the first document comprises first image information, extracting first image embedded features of the first image information by using a first model trained in advance; when the first document comprises first text information, extracting first text embedding characteristics of the first text information by using a pre-trained second model; when the first document comprises first image information and first text information, a first model trained in advance is utilized to extract first image embedded features of the first image information, and a second model trained in advance is utilized to extract first text embedded features of the first text information.
S130, when the first document comprises first image information, generating first coordinates of each first document according to the first image embedded features; when the first documents comprise first text information, generating second coordinates of each first document according to the first text embedding characteristics; when the first document comprises first image information and first text information, generating first concept embedding features according to the first image embedding features and the first text embedding features, and generating third coordinates of each first document according to the first concept embedding features.
In this embodiment, the first model includes an image neural network and an image mapping neural network, and the second model includes a text neural network and a text mapping neural network. The image neural network may use a res net or a MobileNet trained in advance on an ImageNet or Google Open Image, the text neural network may use word2vec, gloVe, BERT or the like, and through embedding of the image neural network and the text neural network, images and texts may be embedded in two different vector spaces, and it is also required to bridge the image embedding and the text embedding in the different vector spaces into the same common space through a multi-layer sensor or a graph rolling network, that is, the image mapping neural network and the text mapping neural network, so as to obtain a first image embedding feature and a first text embedding feature in the same common space.
As shown in fig. 2, in the public space, the first image information is trained by the first model to obtain a first image embedded feature, namely a first coordinate point 201, and the first text information is trained by the second model to obtain a first text embedded feature, namely a second coordinate point 202, that is, the first image information and the first text information can be represented in the same public space and the relationship between the first image information and the first text information can be obtained. In addition, because of the difference in semantics, when the user inputs languages of a plurality of countries, the obtained first text embedding features, such as the third coordinate point 203, the fourth coordinate point 204, and the fifth coordinate point 205, have different distances from the first coordinate point 201.
Specifically, if the first document only comprises first image information, extracting a first image vector of the first image information by using a pre-trained image neural network, mapping the first image vector into a public space in which graphics and texts are jointly embedded by using the pre-trained image mapping neural network, converting the first image vector into first image embedded features, and generating first coordinates of each first document, namely a plurality of coordinate points in the public space, according to the first image embedded features; if the first document only comprises first text information, extracting a first text vector of the first text information by using a pre-trained text neural network, mapping the first text vector into a public space in which the graphics context is jointly embedded by using a pre-trained text mapping neural network, converting the first text vector into first text embedded features, and generating second coordinates of each first document, namely a plurality of coordinate points in the public space, according to the first text embedded features; if the first document comprises first image information and first text information, extracting a first image vector of the first image information by utilizing a pre-trained image neural network, extracting a first text vector of the first text information by utilizing the pre-trained text neural network, mapping the first image vector into a public space in which the graphics and texts are jointly embedded by utilizing a pre-trained image mapping neural network, mapping the first text vector into the public space in which the graphics and texts are jointly embedded by utilizing the pre-trained text mapping neural network to obtain a first image embedded feature and a first text embedded feature, generating a first concept embedded feature according to the first image embedded feature and the first text embedded feature, and generating a third coordinate of each first document, namely a plurality of coordinate points in the public space according to the first concept embedded feature.
The first image embedded feature and the first text embedded feature may be generated by using a pre-trained third model to generate the first concept embedded feature according to the first image embedded feature and the first text embedded feature, wherein a loss function used in the training process of the third model includes a relative hinge loss function and/or an absolute hinge loss function, preferably, a weight of the hinge loss function and the absolute hinge loss function is used in the training process of the third model to generate the first concept embedded feature, and the first image embedded feature and the first text embedded feature are converted into the first concept embedded feature, that is, two coordinate points representing the same patent document in a public space are converted into one coordinate point.
And S140, generating a first information map of the first documents according to the first coordinates, the second coordinates or the third coordinates.
In this embodiment, the first information map is displayed in a scatter diagram, and the plurality of first documents are represented by points at a first coordinate, a second coordinate, or a third coordinate position. The scattered points in the first information map have different colors, and the colors of the points are set in a distinguishing mode according to authors, patent applicants or patent owners of documents corresponding to the points, or the colors of the points are set in a distinguishing mode according to text information of the documents corresponding to the points. Specifically, as shown in fig. 3, after the user uploads all the first documents, a scatter diagram of the first information map is displayed in the first area 301, where a color of each point may correspond to a classification corresponding to each document, and classification information corresponding to each color is displayed in the second area 302, such as the author, the patent applicant, or text information described above.
Further, the presentation form of the first information map supports user interaction, and when a user selects, clicks or hovers over a certain point, image information, text information and/or other information of a document corresponding to the point are displayed in a specific area of the page, including but not limited to an author, a patent applicant, a patent owner, a class number and the like. Specifically, as shown in fig. 4, after the user clicks any coordinate point of the scatter diagram in the third area 401, image information and/or text information of the document corresponding to the point is displayed in the fourth area 402.
Further, the user can zoom in on the graph 401, and after zooming in, the scatter plot will become displayed as shown in the fourth region 501 on the right side of fig. 5. Specifically, when receiving a certain area or a plurality of areas in the scatter diagram selected by the user, the scatter diagram in the area of the selected area may be further displayed in an enlarged manner, so as to obtain a partial enlarged diagram of the fifth area 502 shown on the right side of fig. 5, where the partial enlarged diagram can clearly show the denser scatter diagram in the original scatter diagram. Of course, the user interaction described above is also supported in the enlarged display diagram.
In an alternative embodiment, as shown in fig. 6, the first information map is displayed in the form of an image list, each document is displayed in the form of its representative image, if the document is a patent document, the representative image may be a drawing of the patent document, and the positions of the images are arranged according to the positions of the first coordinate, the second coordinate or the third coordinate of the corresponding document. And the display form of the first information map supports user interaction, and when a certain region in the map selected by the user is received, the map in the region range is enlarged and displayed.
According to the embodiment of the invention, a plurality of first documents are acquired, and each first document comprises first image information and/or first text information; when the first document comprises first image information and first text information, extracting first image embedded features of the first image information by using a first model trained in advance, and extracting first text embedded features of the first text information by using a second model trained in advance; generating first concept embedded features according to the first image embedded features and the first text embedded features, and generating third coordinates of each first document according to the first concept embedded features; and generating first information maps of the plurality of first documents according to the first coordinates, the second coordinates or the third coordinates, solving the problem that the existing information display mode is not visual enough and realizing the effect of displaying information intuitively.
Example two
As shown in fig. 7, a second embodiment of the present invention provides a method for generating an information map, which is further optimized based on the first embodiment of the present invention, and after step S140 of the first embodiment of the present invention, the method further includes:
S210, acquiring one or more second documents, wherein each second document comprises second image information and/or second text information.
S220, when the second document comprises second image information, extracting second image embedded features of the second image information by using a first model trained in advance; extracting a second text embedding feature of the second text information by using a pre-trained second model when the second document comprises the second text information; when the second document comprises second image information and second text information, extracting second image embedded features of the second image information by using a first model trained in advance, and extracting second text embedded features of the second text information by using a second model trained in advance.
S230, when the second document comprises second image information, generating fourth coordinates of each second document according to the second image embedded features; generating a fifth coordinate of each second document according to the second text embedding characteristic when the second document comprises second text information; and when the second document comprises second image information and second text information, generating second concept embedded features according to the second image embedded features and the second text embedded features, and generating sixth coordinates of each second document according to the second concept embedded features.
The implementation method of step S210-step S230 in this embodiment is the same as that of the first embodiment of the present invention.
S240, displaying the fourth, fifth or sixth coordinates in the first information map.
The first document uploaded by the user may be all patent documents of the company, after the first information map of the company patent is generated, the user may continue uploading the second document, the second document may be all patent documents of the competing company, after the same steps as those of the first embodiment of the present invention, the coordinates corresponding to the second document are displayed in the first information map, and the user may explicitly analyze the patent competition situation of the competing company and the affiliated company, where the first coordinate, the second coordinate or the third coordinate corresponding to the first document is different from the display color of the fourth coordinate, the fifth coordinate or the sixth coordinate corresponding to the second document.
S250, acquiring a fourth coordinate, a fifth coordinate or a sixth coordinate, wherein the minimum distance between the fourth coordinate, the fifth coordinate or the sixth coordinate and the first coordinate, the second coordinate or the third coordinate is smaller than a preset value.
S260, highlighting the fourth coordinate, the fifth coordinate or the sixth coordinate of which the minimum distance is smaller than a preset value.
In this embodiment, after receiving one or more second documents, the first information map has a plurality of first documents, and the second documents with a minimum distance from the first, second or third coordinates smaller than a preset value are indicated to be similar to the first documents, and the coordinates of the second documents are distributed in the first, second or third coordinates, so that the first documents and the second documents are more intuitively distinguished by the user, and therefore, the fourth, fifth or sixth coordinates with a minimum distance smaller than the preset value can be highlighted.
Furthermore, coordinate points with high similarity between the company to which the user belongs and the competing company can be obtained from the first information map, and the coordinate points are highlighted, so that the user can obtain the patent information with relatively high competing strength more intuitively. Likewise, the user may zoom in on the first information map, obtain image information for highly similar patents, and click or otherwise obtain detailed information for those patents.
Further, the user can further input patent documents of a plurality of competing companies, and correspondingly, the patent documents can be displayed in the first information map in a distinguishing mode through coordinate points with different colors.
According to the embodiment of the invention, the fourth coordinate, the fifth coordinate or the sixth coordinate, the distance between the fourth coordinate and the first coordinate, the fifth coordinate or the third coordinate is smaller than a preset value, are obtained; acquiring a fourth coordinate, a fifth coordinate or a sixth coordinate, wherein the distance between the fourth coordinate, the fifth coordinate or the sixth coordinate and the first coordinate, the second coordinate or the third coordinate is smaller than a preset value; the fourth coordinate, the fifth coordinate or the sixth coordinate, the distance of which is smaller than the preset value, are highlighted, the problem that the existing information display mode is not visual enough is solved, and the effect of displaying information intuitively is achieved.
Example III
As shown in fig. 8, a third embodiment of the present invention provides a system 100 for generating an information map, where the system 100 for generating an information map provided in the third embodiment of the present invention can execute the method for generating an information map provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. The generation system 100 includes a document acquisition module 110, a feature extraction module 120, a coordinate generation module 130, and a map generation module 140.
Specifically, the document obtaining module 110 is configured to obtain a plurality of first documents, where each first document includes first image information and/or first text information; the feature extraction module 120 is configured to extract, when the first document includes first image information, a first image embedded feature of the first image information using a first model trained in advance; when the first document comprises first text information, extracting first text embedding characteristics of the first text information by using a pre-trained second model; when the first document comprises first image information and first text information, extracting first image embedded features of the first image information by using a first model trained in advance, and extracting first text embedded features of the first text information by using a second model trained in advance; the coordinate generating module 130 is configured to generate, when the first document includes first image information, first coordinates of each of the first documents according to the first image embedded feature; when the first documents comprise first text information, generating second coordinates of each first document according to the first text embedding characteristics; when the first document comprises first image information and first text information, generating first concept embedding features according to the first image embedding features and the first text embedding features, and generating third coordinates of each first document according to the first concept embedding features; the map generating module 140 is configured to generate a first information map of the plurality of first documents according to the first coordinates, the second coordinates or the third coordinates.
In this embodiment, the first information map is displayed in a scatter diagram, where the plurality of first documents are represented by points at a first coordinate, a second coordinate, or a third coordinate, the scatter points in the first information map have different colors, and the colors of the points are set in a distinguishing manner according to authors, patent applicants, or patent owners of the documents corresponding to the points, or the colors of the points are set in a distinguishing manner according to text information of the documents corresponding to the points. And the display form of the first information map supports user interaction, and when the user selects, clicks or hovers at a certain point, the image information and/or text information of the document corresponding to the point are displayed in a specific area of the page. When a user selects a certain area in the map, the map within the selected area is displayed in an enlarged manner. The first model includes an image neural network and an image mapping neural network, and the second model includes a text neural network and a text mapping neural network. In an alternative embodiment, the first information map is displayed in the form of an image list, each document is displayed in the form of its representative image, and the positions of the images are arranged according to the distance between the first coordinates, the second coordinates or the third coordinates of the corresponding document.
Further, the document obtaining module 110 is further configured to obtain one or more second documents, where each second document includes second image information and/or second text information; the feature extraction module 120 is further configured to extract, when the second document includes second image information, second image embedded features of the second image information using a first model trained in advance; extracting a second text embedding feature of the second text information by using a pre-trained second model when the second document comprises the second text information; when the second document comprises second image information and second text information, extracting second image embedded features of the second image information by using a pre-trained first model, and extracting second text embedded features of the second text information by using a pre-trained second model; the coordinate generating module 130 is further configured to generate, when the second document includes second image information, fourth coordinates of each of the second documents according to the second image embedded feature; generating a fifth coordinate of each second document according to the second text embedding characteristic when the second document comprises second text information; when the second document comprises second image information and second text information, generating second concept embedded features according to the second image embedded features and the second text embedded features, and generating sixth coordinates of each second document according to the second concept embedded features; the map generating module 140 is further configured to display the fourth coordinate, the fifth coordinate, or the sixth coordinate in the first information map.
Further, the feature extraction module 120 is specifically configured to extract a first image vector of the first image information by using a pre-trained image neural network; mapping the first image vector into a public space of image-text joint embedding by using a pre-trained image mapping neural network, and transforming the first image vector into a first image embedding feature; extracting a first text vector of the first text information by using a pre-trained text neural network; and mapping the first text vector into the public space of the graphic joint embedding by using a pre-trained text mapping neural network, and converting the first text vector into a first text embedding feature. The coordinate generating module 130 is specifically configured to generate the first concept embedding feature according to the first image embedding feature and the first text embedding feature by using a pre-trained third model, and further configured to generate the second concept embedding feature according to the second image embedding feature and the second text embedding feature by using a pre-trained third model, where a loss function used in the training process of the third model includes a relative hinge loss function and/or an absolute hinge loss function.
Further, the information map generating system 100 further includes a coordinate highlighting module 150, where the coordinate highlighting module 150 is configured to obtain a fourth coordinate, a fifth coordinate, or a sixth coordinate, where a distance between the fourth coordinate, the fifth coordinate, or the sixth coordinate and the first coordinate, the second coordinate, or the third coordinate is smaller than a preset value; and highlighting the fourth coordinate, the fifth coordinate or the sixth coordinate of which the distance is smaller than a preset value.
Example IV
Fig. 9 is a schematic structural diagram of an information map generating apparatus according to a fourth embodiment of the present invention. Fig. 9 illustrates a block diagram of an exemplary generating device 12 suitable for use in implementing embodiments of the present invention. The generating device 12 shown in fig. 9 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the invention.
As shown in fig. 9, the generating device 12 is in the form of a general purpose computing device. The components of the generating device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Generating device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by generating device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Generating device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 9, commonly referred to as a "hard disk drive"). Although not shown in fig. 9, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The generating device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the generating device 12, and/or any devices (e.g., network card, modem, etc.) that enable the generating device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, the generating device 12 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 20. As shown, network adapter 20 communicates with other modules of generating device 12 via bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with generating device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing the generation method provided by the embodiment of the present invention:
acquiring a plurality of first documents, wherein each first document comprises first image information and/or first text information;
when the first document comprises first image information, extracting first image embedded features of the first image information by using a first model trained in advance; when the first document comprises first text information, extracting first text embedding characteristics of the first text information by using a pre-trained second model; when the first document comprises first image information and first text information, extracting first image embedded features of the first image information by using a first model trained in advance, and extracting first text embedded features of the first text information by using a second model trained in advance;
when the first documents comprise first image information, generating first coordinates of each first document according to the first image embedded features; when the first documents comprise first text information, generating second coordinates of each first document according to the first text embedding characteristics; when the first document comprises first image information and first text information, generating first concept embedding features according to the first image embedding features and the first text embedding features, and generating third coordinates of each first document according to the first concept embedding features;
And generating a first information map of the plurality of first documents according to the first coordinates, the second coordinates or the third coordinates.
Example five
The fifth embodiment of the present invention further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a generating method as provided by all the inventive embodiments of the present application:
acquiring a plurality of first documents, wherein each first document comprises first image information and/or first text information;
when the first document comprises first image information, extracting first image embedded features of the first image information by using a first model trained in advance; when the first document comprises first text information, extracting first text embedding characteristics of the first text information by using a pre-trained second model; when the first document comprises first image information and first text information, extracting first image embedded features of the first image information by using a first model trained in advance, and extracting first text embedded features of the first text information by using a second model trained in advance;
when the first documents comprise first image information, generating first coordinates of each first document according to the first image embedded features; when the first documents comprise first text information, generating second coordinates of each first document according to the first text embedding characteristics; when the first document comprises first image information and first text information, generating first concept embedding features according to the first image embedding features and the first text embedding features, and generating third coordinates of each first document according to the first concept embedding features;
And generating a first information map of the plurality of first documents according to the first coordinates, the second coordinates or the third coordinates.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the above embodiments, but may include many other equivalent embodiments without departing from the spirit of the invention, the scope of which is determined by the scope of the appended claims.
Claims (13)
1. A method of generating an information map, comprising:
acquiring a plurality of first documents, wherein each first document comprises first image information and/or first text information;
when the first document comprises first image information, extracting first image embedded features of the first image information by using a first model trained in advance; when the first document comprises first text information, extracting first text embedding characteristics of the first text information by using a pre-trained second model; when the first document comprises first image information and first text information, extracting first image embedded features of the first image information by using a first model trained in advance, and extracting first text embedded features of the first text information by using a second model trained in advance;
When the first documents comprise first image information, generating first coordinates of each first document according to the first image embedded features; when the first documents comprise first text information, generating second coordinates of each first document according to the first text embedding characteristics; when the first document comprises first image information and first text information, generating first concept embedding features according to the first image embedding features and the first text embedding features, and generating third coordinates of each first document according to the first concept embedding features;
generating a first information map of the plurality of first documents according to the first coordinates, the second coordinates or the third coordinates; the first information map is displayed in a scatter diagram, and the plurality of first documents are represented by points at a first coordinate, a second coordinate or a third coordinate position; the scattered points in the first information map have different colors, and the colors of the points are set in a distinguishing mode according to authors, patent applicants or patentees of documents corresponding to the points, or the colors of the points are set in a distinguishing mode according to text information of the documents corresponding to the points.
2. The method according to claim 1, wherein the presentation form of the first information map supports user interaction, and when a user selects, clicks or hovers over a certain point, image information and/or text information of a document corresponding to the point is displayed in a specific area of the page.
3. The method according to claim 1, wherein the first information map is displayed in the form of an image list, each document is displayed as its representative image, and the positions of each image are arranged according to the positions of the first coordinates, the second coordinates, or the third coordinates of the corresponding document.
4. A method of generating a map according to any one of claims 1 to 3, wherein the presentation of the first information map supports user interaction, and when a user selects a region in the map, the map within the selected region is displayed in an enlarged manner.
5. The method of generating of claim 1, wherein the generating a first concept embedding feature from the first image embedding feature and a first text embedding feature comprises:
generating the first concept embedding feature according to the first image embedding feature and the first text embedding feature by utilizing a pre-trained third model, wherein a loss function used in the training process of the third model comprises a relative hinge loss function and/or an absolute hinge loss function.
6. The method of generating according to claim 1, wherein the generating the first information map of the plurality of first documents according to the first coordinates, the second coordinates, or the third coordinates comprises:
acquiring one or more second documents, wherein each second document comprises second image information and/or second text information;
extracting second image embedded features of the second image information by using a first model trained in advance when the second document comprises the second image information; extracting a second text embedding feature of the second text information by using a pre-trained second model when the second document comprises the second text information; when the second document comprises second image information and second text information, extracting second image embedded features of the second image information by using a pre-trained first model, and extracting second text embedded features of the second text information by using a pre-trained second model;
generating fourth coordinates of each second document according to the second image embedded features when the second document comprises second image information; generating a fifth coordinate of each second document according to the second text embedding characteristic when the second document comprises second text information; when the second document comprises second image information and second text information, generating second concept embedded features according to the second image embedded features and the second text embedded features, and generating sixth coordinates of each second document according to the second concept embedded features;
And displaying the fourth coordinate, the fifth coordinate or the sixth coordinate in the first information map.
7. The method of generating of claim 6, wherein the generating a second concept embedding feature from the second image embedding feature and a second text embedding feature comprises:
generating the second concept embedded features according to the second image embedded features and the second text embedded features by using a pre-trained third model, wherein the loss function used in the training process of the third model comprises a relative hinge loss function and/or an absolute hinge loss function.
8. The method according to claim 6, wherein the displaying the fourth, fifth, or sixth coordinates in the first information map comprises:
acquiring a fourth coordinate, a fifth coordinate or a sixth coordinate, wherein the minimum distance between the fourth coordinate, the fifth coordinate or the sixth coordinate and the first coordinate, the second coordinate or the third coordinate is smaller than a preset value;
and highlighting the fourth coordinate, the fifth coordinate or the sixth coordinate of which the minimum distance is smaller than a preset value.
9. The method of generating of claim 6, wherein the first model comprises an image neural network and an image mapping neural network, and the second model comprises a text neural network and a text mapping neural network.
10. The method of generating of claim 9, wherein the extracting the first image embedded feature of the first image information using the first model trained in advance and the extracting the first text embedded feature of the first text information using the second model trained in advance comprises:
extracting a first image vector of the first image information by using a pre-trained image neural network;
mapping the first image vector into a public space of image-text joint embedding by using a pre-trained image mapping neural network, and transforming the first image vector into a first image embedding feature;
extracting a first text vector of the first text information by using a pre-trained text neural network;
and mapping the first text vector into the public space of the graphic joint embedding by using a pre-trained text mapping neural network, and converting the first text vector into a first text embedding feature.
11. A system for generating an information map, comprising:
the document acquisition module is used for acquiring a plurality of first documents, wherein each first document comprises first image information and/or first text information;
the feature extraction module is used for extracting first image embedded features of the first image information by using a pre-trained first model when the first document comprises the first image information; when the first document comprises first text information, extracting first text embedding characteristics of the first text information by using a pre-trained second model; when the first document comprises first image information and first text information, extracting first image embedded features of the first image information by using a first model trained in advance, and extracting first text embedded features of the first text information by using a second model trained in advance;
The coordinate generation module is used for generating a first coordinate of each first document according to the first image embedding characteristics when the first document comprises first image information; when the first documents comprise first text information, generating second coordinates of each first document according to the first text embedding characteristics; when the first document comprises first image information and first text information, generating first concept embedding features according to the first image embedding features and the first text embedding features, and generating third coordinates of each first document according to the first concept embedding features;
the map generation module is used for generating first information maps of the first documents according to the first coordinates, the second coordinates or the third coordinates; the first information map is displayed in a scatter diagram, and the plurality of first documents are represented by points at a first coordinate, a second coordinate or a third coordinate position; the scattered points in the first information map have different colors, and the colors of the points are set in a distinguishing mode according to authors, patent applicants or patentees of documents corresponding to the points, or the colors of the points are set in a distinguishing mode according to text information of the documents corresponding to the points.
12. An information map generation apparatus, characterized in that the apparatus comprises:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the generating method of any of claims 1-10.
13. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the generating method according to any of claims 1-10.
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| CN103885989A (en) * | 2012-12-24 | 2014-06-25 | 腾讯科技（武汉）有限公司 | Method and device for estimating new word document frequency |
| CN107368506A (en) * | 2015-05-11 | 2017-11-21 | 斯图飞腾公司 | Unstructured data analysis system and method |
| CN106372087A (en) * | 2015-07-23 | 2017-02-01 | 北京大学 | Information retrieval-oriented information map generation method and dynamic updating method |
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