Ontology-based composite semantic correlation quantification method and system

Abstract

The invention discloses an ontology-based composite semantic correlation quantification method and system. The method comprises the following steps: S1, preprocessing a first composite semantic and a second composite semantic, and respectively mapping the preprocessed first composite semantic and second composite semantic to a first node set and a second node set of an ontology in a semantic network; s2, respectively setting a first virtual node and a second virtual node for the first composite semantics and the second composite semantics; s3, quantifying the first correlation of the first virtual node to all nodes in the first node set; s4, quantifying a second correlation between the nodes in the first node set and the nodes in the second node set; s5, quantifying third correlation of the nodes in the second node set to the second virtual node; and S6, quantifying fourth correlation from the first virtual node to the second virtual node, namely correlation between the first composite semantics and the second composite semantics. According to the method, the problem that reality semantics are difficult to map to semantic nodes and the problem of mutual conversion between reality composite semantic correlation and node set correlation are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of semantic relevance quantification, and in particular to an ontology-based composite semantic relevance quantification method and system. Background Art

[0002] While big data brings convenience to people's lives, it also brings the problem of information overload. Now people can use algorithms to collect users' personal information and interaction information to the greatest extent possible to predict users' future interests and preferences, so as to provide accurate services for different users. However, in many cases, a large amount of user information contains sensitive information that threatens personal privacy, which means that attackers have more ways to attack privacy than ever before.

[0003] Assuming that there is a need to protect specific privacy attributes, and different attributes have different protection strength requirements, the privacy protection task needs to find other information that is highly correlated with specific privacy information. Today's mainstream solutions are data set-based correlation quantification and semantics-based correlation quantification. Semantics-based correlation quantification includes corpus-based quantification and ontology-based quantification.

[0004] The existing ontology-based semantic relevance measurement scheme adopts the method of starting from a known node in a semantic network and reasoning along the semantic relationship until the target node is inferred, at which point the relevance of the node to the target node is equal to the reasoning contribution of the node to the target node.

[0005] The main problem with existing technical solutions is that most semantic relevance solutions focus on the connection between nodes, and quantify the degree of relevance of real-world semantics by calculating the degree of relevance between a single node and the target node in the graph structure. This approach has the following specific problems: (1) Real semantics contain complex semantics and rare semantics. It is difficult to find completely corresponding nodes for these semantics in the semantic network, or only edge nodes with few connections to other nodes can be found. Existing technologies can only map these semantics to the nodes with the closest semantics, which will cause large errors in the quantification of semantic relevance.

[0006] (2) The existing technology only considers the degree of node association but not the degree of association of node sets. Real semantics have complex semantics that need to be mapped to multiple nodes at the same time, and the correlation of these semantics should be represented by the correlation of node sets. The existing technology only considers one-to-one node correlation, and there is no definite solution for studying the correlation of node sets.

[0007] (3) The target privacy semantics of a privacy protection task is often more than one, and may be a collection of multiple semantics. Different privacy semantics have different sensitivities and require different levels of protection. Even if the sensitivity of the actual composite semantics has been set, there is currently no technology that clearly explains how to distribute the sensitivity of the composite semantics to multiple nodes in a node set. Summary of the invention

[0008] In view of the deficiencies in the prior art, the present invention provides an ontology-based composite semantic relevance quantification method and system, which solves the problem that real semantics are difficult to map to semantic nodes, solves the correlation calculation of node sets, and solves the mutual conversion problem between real composite semantic relevance and node set relevance.

[0009] The present invention provides an ontology-based composite semantic relevance quantification method, the method comprising: S1. Mapping the first composite semantics and the second composite semantics to the first node set and the second node set of the ontology in the semantic network after preprocessing, respectively, wherein the correlation between the first composite semantics and the second composite semantics is the correlation between the first node set and the second node set; S2, setting a first virtual node and a second virtual node for the first composite semantics and the second composite semantics respectively, wherein the correlation between the first composite semantics and the second composite semantics is the correlation of the first virtual node to the second virtual node, and the virtual node is a hyponym of all nodes in the corresponding node set; S3. quantify a first correlation of the first virtual node to all nodes in the first node set, wherein the correlation between nodes is a mutual inference probability between nodes; S4. quantifying a second correlation between the nodes in the first node set and the nodes in the second node set based on the graph features of the ontology and the information amount of the nodes in the first node set and the second node set; S5. quantifying a third relevance of the nodes in the second node set to the second virtual node based on the amount of information shared between the nodes in the second node set; S6. Based on the transitivity of the inference probability, the first correlation, the second correlation and the third correlation, quantify the fourth correlation of the first virtual node to the second virtual node, that is, the correlation between the first composite semantics and the second composite semantics.

[0010] Preferably, step S1 comprises: S11, decomposing the first compound semantics and the second compound semantics into simple semantics that can be directly mapped to nodes, and before decomposing, the process also includes semantic fusion of uncommon words, wherein the simple semantics are nouns or adjectives; S12. Map the simple semantics corresponding to the first compound semantics and the second compound semantics to the first node set and the second node set of the semantic network respectively by calling the vocabulary node mapping method of the ontology.

[0011] Preferably, the relationship between a node set and a node is expressed as the relationship between a complex semantics and sub-semantics, and the existence of the complex semantics indicates that all sub-semantics exist simultaneously; a virtual node refers to a node that does not exist in the ontology but can correspond to the actual complex semantics; a virtual node is a hyponym of all nodes in the node set, and the occurrence of the virtual node indicates the occurrence of all nodes in the corresponding node set; the correlation between two node sets can be converted into the correlation between two virtual nodes.

[0012] Preferably, the correlation between nodes is the mutual inference probability between nodes , if the node The semantics represented can be used to infer the node The probability of the semantics represented is , then the node With Node The correlation is: = (1); Inference Probability The following two conditions should be met: ① Transitivity: Assume that there is a node , then: = (2); ②Probability additivity: Assume that there are nodes , then: = (3); A virtual node is a hyponym of all nodes in a node set, and the occurrence of the virtual node indicates the occurrence of all nodes in the corresponding node set, that is, the correlation of the virtual node to all nodes in the node set is 1; in step S3, the first correlation of the first virtual node to all nodes in the first node set is 1.

[0013] Preferably, in step S4, the second correlation of the nodes in the first node set to the nodes in the second node set is quantified by the following method, that is, the correlation between the nodes in the ontology is quantified, and the correlation between the nodes is the probability that the starting node infers the end node through the relationship between the nodes: S41, perform graph traversal according to the starting node, set the longest path from the starting node to the end node, use a stack to record the current path, and set two sets to respectively record the nodes and node relationships of the current path; S42, after traversing to the end node under the specified path length, recording the nodes in the stack and the relationship between the nodes in the two sets, and constructing the sensitive value transfer subgraph of the current path after the traversal is completed; S43. Calculate the correlation between nodes in the sensitive value transfer subgraph of each path. When a node has a relationship with multiple nodes, the calculation formula for the correlation between nodes of each path is: (4); In formula (4), Represents a slave node To Node The path weight is the inference probability, that is, the correlation. Representation Node The set of all hyponyms of Represents the parent node The amount of information, The target node The amount of information of a node is determined based on the graph features of the ontology or based on a corpus; S44. If there is a single path between the starting node and the end node, the correlation between the starting node and the end node is the product of all path weights on the path, and the calculation formula is: (5); In formula (5), From the starting node To the end node The correlation of k represents the node To Node The longest path length between Represents a slave node To the adjacent node Path weight, i and k are positive integers; S45. If there are multiple paths between the start node and the end node in the same sensitive value transfer subgraph, the correlation between the start node and the end node is the sum of the correlations inferred from the multiple paths, and the correlation does not exceed 1. The calculation formula is: (6); In formula (6), From the starting node To the end node The correlation of k represents the node To Node The longest path length between them, q represents the starting node To the end node The number of paths that exist in the same sensitive value transfer subgraph, Represents a slave node To the adjacent node The path weight of , i, j, k, q are positive integers.

[0014] Preferably, in step S5, the third relevance of the nodes in the second node set to the second virtual node is quantified by the following method: When there are two nodes in the node set When the node Virtual Node The correlation is two nodes The correlation between them is calculated as: (7); When the node set has at least three nodes, the node Virtual Node The calculation formula of the correlation is: (8); In formulas (7) and (8), it is assumed that the event The corresponding composite semantics is , the simple semantics corresponding to events A and B are , composite semantics The corresponding node set is ,event The corresponding semantics is , semantics The corresponding node set is , represents the probability of event S occurring when event A occurs, Indicates that event A occurs The probability of occurrence, the degree of mutual influence of multiple nodes in a node set is related to the amount of information shared between the nodes, and the amount of information shared between the nodes is determined by the minimum common ancestor node of the nodes. express, The semantics represented is , k represents arrive The path length.

[0015] Preferably, in step S5, the correlation between the first composite semantics and the second composite semantics is the correlation between the first node set and the second node set, and is also the correlation between the first virtual node and the second virtual node, and is calculated as follows: ; In formula (9), represents the first composite semantics, represents the second composite semantics, represents the first node set, represents the second node set, represents the first virtual node, Represents the second virtual node.

[0016] Based on the same inventive concept, the present invention also provides an ontology-based composite semantic relevance quantification system, the system comprising: A semantic node mapping module, used for mapping the first composite semantics and the second composite semantics to the first node set and the second node set of the ontology in the semantic network after preprocessing, wherein the correlation between the first composite semantics and the second composite semantics is the correlation between the first node set and the second node set; a virtual node setting module, used to set a first virtual node and a second virtual node for the first composite semantics and the second composite semantics, respectively, wherein the correlation between the first composite semantics and the second composite semantics is the correlation of the first virtual node to the second virtual node, and the virtual node is a hyponym of all nodes in the corresponding node set; A first quantification module, configured to quantify a first correlation of the first virtual node to all nodes in the first node set, wherein the correlation between nodes is a mutual inference probability between nodes; A second quantification module, configured to quantify a second correlation between the nodes in the first node set and the nodes in the second node set based on the graph features of the ontology and the information amounts of the nodes in the first node set and the second node set; A third quantization module, configured to quantify a third relevance of the nodes in the second node set to the second virtual node based on the amount of information shared between the nodes in the second node set; The fourth quantization module is used to quantify the fourth correlation of the first virtual node to the second virtual node, that is, the correlation between the first composite semantics and the second composite semantics, based on the transitivity of the inference probability, the first correlation, the second correlation and the third correlation.

[0017] Preferably, the semantic node mapping module is specifically used for: Decomposing the first compound semantics and the second compound semantics into simple semantics that can be directly mapped to nodes, and before decomposing, the method further includes semantic fusion of uncommon words, wherein the simple semantics are nouns or adjectives; The simple semantics corresponding to the first compound semantics and the second compound semantics are respectively mapped to the first node set and the second node set of the semantic network by calling the vocabulary node mapping method of the ontology.

[0018] Preferably, the relationship between a node set and a node is expressed as the relationship between a complex semantics and sub-semantics, and the existence of the complex semantics indicates that all sub-semantics exist simultaneously; a virtual node refers to a node that does not exist in the ontology but can correspond to the actual complex semantics; a virtual node is a hyponym of all nodes in the node set, and the occurrence of the virtual node indicates the occurrence of all nodes in the corresponding node set; the correlation between two node sets can be converted into the correlation between two virtual nodes.

[0019] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention solves the problem that real semantics cannot be completely mapped to semantic nodes. By reconstructing the semantic granularity through semantic preprocessing, all semantics are mapped to semantic nodes in a many-to-many relationship, thereby reducing calculation errors.

[0020] 2. The present invention solves the technical deficiency of node set correlation quantification, expands the one-to-one node correlation quantification of the existing solution to the quantification of many-to-many relationships, and solves the problem of the existing technology requiring loop traversal through the idea of ​​virtual nodes.

[0021] 3. The present invention solves the mutual mapping problem between composite semantic relevance and node set relevance, and solves the problem of difficult transition between node relevance and semantic relevance, making the technology more in line with actual privacy protection needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a flow chart of a composite semantic relevance quantification method based on ontology provided by the present invention; Figure 2 A schematic diagram of a virtual node provided by the present invention; Figure 3 A schematic diagram of the transitivity of the inference probability provided by the present invention; Figure 4 A schematic diagram of the probability additivity of the inference probability provided by the present invention; Figure 5 A schematic diagram of the structure of an ontology-based composite semantic relevance quantification system provided by the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0025] like Figure 1 As shown, an embodiment of the present invention provides an ontology-based composite semantic relevance quantification method, the method comprising: S1, mapping the first composite semantics and the second composite semantics to the first node set and the second node set of the ontology in the semantic network after preprocessing, respectively, wherein the correlation between the first composite semantics and the second composite semantics is the correlation between the first node set and the second node set; S2, setting a first virtual node and a second virtual node for the first composite semantics and the second composite semantics respectively, wherein the correlation between the first composite semantics and the second composite semantics is the correlation between the first virtual node and the second virtual node, and the virtual node is a hyponym of all nodes in the corresponding node set; S3, quantifying a first correlation of the first virtual node to all nodes in the first node set, wherein the correlation between nodes is a mutual inference probability between nodes; S4. quantifying the second relevance of the nodes in the first node set to the nodes in the second node set based on the graph features of the ontology and the information amount of the nodes in the first node set and the second node set; S5. quantifying a third relevance of the nodes in the second node set to the second virtual node based on the amount of information shared between the nodes in the second node set; S6. Based on the transitivity of the inference probability, the first correlation, the second correlation and the third correlation, quantify the fourth correlation of the first virtual node to the second virtual node, that is, the correlation between the first compound semantics and the second compound semantics.

[0026] In the embodiment of the present invention, step S1 includes: S11, decomposing the first compound semantics and the second compound semantics into simple semantics that can be directly mapped to nodes, the simple semantics being nouns or adjectives, and including semantic fusion of uncommon words before decomposition; It is difficult to directly find completely corresponding nodes in the ontology for real semantics. Preprocessing real semantics can solve this problem. The main work of preprocessing is semantic decomposition. There are a large number of words that are compound relations of multiple semantics. The semantics of the words are regarded as a set of multiple semantics. In the face of more complex semantics, the attribute semantics is expanded from mapping to a node to mapping to a node set through semantic decomposition. In the field of privacy, most privacy is expressed by nouns and adjectives. Therefore, the semantic decomposition retains only adjectives and nouns by default. For example, "unmarried women" is decomposed into a semantic set {"unmarried", "female"}.

[0027] In addition, preprocessing also includes semantic fusion of uncommon words. Some words are too uncommon and are not recorded in the semantic network. By fusing the semantics, the semantics can be made more abstract and thus mapped to nodes with more connections.

[0028] S12, mapping the simple semantics corresponding to the first compound semantics and the second compound semantics to the first node set and the second node set of the semantic network respectively by calling the vocabulary node mapping method of the ontology.

[0029] ; ; in, Represents the first composite semantics, Represents the second composite semantics, Representing composite semantics The corresponding first node set, Representing composite semantics The corresponding second node set, express The simple semantics after disassembly, express The simple semantics after disassembly, Represents simple semantics Mapped to nodes of the semantic network, Represents simple semantics Nodes mapped to semantic networks.

[0030] The early technical solution is to traverse the graph from one node to another specified node within the specified path range, and calculate the correlation between the two nodes through these paths. This method can only calculate the correlation between single nodes in the ontology. In order to reuse this method on node sets, the node set can be replaced by a single node by abstracting virtual nodes.

[0031] A virtual node refers to a node that does not exist in the ontology but can correspond to a real complex semantics. For example, the complex semantics "unmarried woman" has no corresponding node in the ontology, so it can be assumed that it can be mapped to a virtual node. , The semantics of the representation is unmarried_female.n, and It is the hyponym node of the node unmarried.a.01 mapped to the semantics “unmarried” and the node female.n.02 mapped to the semantics “female”.

[0032] like Figure 2 As shown in the figure, hypernyms and hyponyms are the most common relationship in the ontology. For example, car.n.01 and motor_vehicle.n.01 are a pair of hyponyms and hypernyms. This pair of relationships can be understood as an "is-a" relationship, such as "car is akind of motor_vehicle".

[0033] In an embodiment of the present invention, the relationship between a node set and a node is expressed as the relationship between a composite semantics and a sub-semantic. The existence of the composite semantics indicates that all sub-semantics exist simultaneously. A virtual node refers to a node that does not exist in the ontology but can correspond to the actual composite semantics. A virtual node is a hyponym of all nodes in a node set. The occurrence of the virtual node indicates the occurrence of all nodes in the corresponding node set. The correlation between two node sets can be converted into the correlation between two virtual nodes, as shown below.

[0034] ; in, Indicates relevance, represents the first node set, represents the relevance of the second node set, represents the first virtual node, Indicates the relevance of the second virtual node.

[0035] In the embodiment of the present invention, the correlation between nodes is the mutual inference probability between nodes , if the node The semantics represented can be used to infer the node The probability of the semantics represented is , then the node With Node The correlation is: = (1); like Figure 3-4 As shown, the inference probability The following two conditions should be met: ① Transitivity ( Figure 3 ): Assume that there is a node , then: = (2); ②Probability additivity ( Figure 4 ): Assume that there is a node , then: = (3); The path weights of different path relationships between nodes in the ontology are the inference probabilities. As shown in Table 1.

[0036] Table 1 ;

[0037] The virtual node is a hyponym of all nodes in the node set, and the occurrence of the virtual node indicates the occurrence of all nodes in the corresponding node set, that is, the relevance of the virtual node to all nodes in the node set is 1. In step S3, the first relevance of the first virtual node to all nodes in the first node set is 1. ; In the embodiment of the present invention, in step S4, the second correlation of the nodes in the first node set to the nodes in the second node set is quantified by the following method, that is, the correlation between the nodes in the entity is quantified, and the correlation between the nodes is the probability that the starting node infers the end node through the relationship between the nodes: S41, perform graph traversal according to the starting node, set the longest path from the starting node to the end node, use a stack to record the current path, and set two sets to respectively record the nodes and node relationships of the current path; S42, after traversing to the end node under the specified path length, the nodes in the stack and the relationship between the nodes are recorded in two sets, and after the traversal is completed, the sensitive value transfer subgraph of the current path is constructed; S43. Calculate the correlation between nodes in the sensitive value transfer subgraph of each path. When a node has a relationship with multiple nodes, the calculation formula for the correlation between nodes of each path is: (4); In formula (4), Represents a slave node To Node The path weight is the inference probability, that is, the correlation. Representation Node The set of all hyponyms of , representing the parent node The amount of information, The target node The amount of information of a node is determined based on the graph features of the ontology or based on the corpus; S44. If there is a single path between the starting node and the end node, the correlation between the starting node and the end node is the product of all path weights on the path, and the calculation formula is: (5); In formula (5), From the starting node To the end node The correlation of k represents the node To Node The longest path length between Represents a slave node To the adjacent node Path weight, i and k are positive integers; S45. If there are multiple paths between the start node and the end node in the same sensitive value transfer subgraph, the correlation between the start node and the end node is the sum of the correlations inferred from the multiple paths, and the correlation does not exceed 1. The calculation formula is: (6); In formula (6), From the starting node To the end node The correlation of k represents the node To Node The longest path length between them, q represents the starting node To the end node The number of paths that exist in the same sensitive value transfer subgraph, Represents a slave node To the adjacent node The path weight of , i, j, k, q are positive integers.

[0038] In the ontology, the reasoning weight of the superordinate to the subordinate word is the weight of the information amount. This method is not applicable to virtual nodes because the information amount of virtual nodes cannot be obtained through the structure of the ontology. This method needs to be improved for calculating the semantic relevance of complex events.

[0039] In the embodiment of the present invention, in step S5, the third relevance of the nodes in the second node set to the second virtual node is quantified by the following method: When there are two nodes in the node set When the node Virtual Node The correlation is two nodes The correlation between them is calculated as: (7); When the node set has at least three nodes, the node Virtual Node The calculation formula of the correlation is: (8); In formulas (7) and (8), it is assumed that the event The corresponding composite semantics is , the simple semantics corresponding to events A and B are , composite semantics The corresponding node set is ,event The corresponding semantics is , semantics The corresponding node set is , represents the probability of event S occurring when event A occurs, Indicates that event A occurs The probability of occurrence, the degree of mutual influence of multiple nodes in a node set is related to the amount of information shared between the nodes, and the amount of information shared between the nodes is determined by the minimum common ancestor node of the nodes. express, The semantics represented is , k represents arrive The path length.

[0040] Specifically, suppose there is a complex event ,event The corresponding semantics is , where a and b are the semantics corresponding to event A and event B respectively. Set the virtual node corresponding to semantic s to , the node set is { }, and are the nodes corresponding to semantics a and b respectively, then and The correlation is: ; It represents the probability of event S occurring when event A occurs. According to the above formula, it can be deduced that: ; The problem is transformed into calculating , that is, when there are two nodes in the node set, the correlation between the node and the virtual node is the correlation between the two nodes.

[0041] For example, "single female" = "single" ∩ "female", then we can set the virtual node to unmarried_female.n, and calculate NR(female.n.02 unmarried_female.n) can be converted to calculate NR(female.n.02) unmarried.a.01).

[0042] After expanding to multiple nodes, assuming the semantics Represents event S, and the corresponding node set is , The corresponding real semantics is , representing the event , so we can get: ; This is a recursive algorithm with high complexity. It adopts a simplified approach and only considers the relationship between hypernyms and hyponyms. The degree of mutual influence between multiple nodes in a node set is related to the amount of information shared between the nodes, and the amount of shared information can be determined by the minimum common ancestor node of the nodes. To indicate, and The semantics represented This way we can simplify The calculation assumes arrive The path length is k, and we can get formula (8).

[0043] In the embodiment of the present invention, in step S5, the correlation between the first composite semantics and the second composite semantics is the correlation between the first node set and the second node set, and is also the correlation between the first virtual node and the second virtual node, and the calculation formula is: ; In formula (9), Represents the first composite semantics, Represents the second composite semantics, represents the first node set, represents the second node set, represents the first virtual node, Represents the second virtual node.

[0044] Specifically, the ultimate goal of the present invention is to calculate the semantic relevance .if There is no composite semantics, that is, nodes in the ontology can be directly mapped, then The semantic relevance of is the node relevance of the nodes mapped in the ontology: ; in, In the ontology, they can be combined with semantic Nodes that map to each other.

[0045] if is a composite semantics, and The semantics of the ontology needs to be passed through the node set in the ontology To map, the virtual node can be abstracted according to the above method Come and semantics Mapping is performed, at this time The semantic relevance of is: ; if All of them are composite semantics. Calculating the correlation of composite semantics is to calculate the correlation of node sets. The correlation of node sets can be abstracted as the correlation of virtual nodes, that is: ; like Figure 5 As shown, an embodiment of the present invention further provides an ontology-based composite semantic relevance quantification system, the system comprising: A semantic node mapping module 100, for mapping the first composite semantics and the second composite semantics to the first node set and the second node set of the ontology in the semantic network after preprocessing, wherein the correlation between the first composite semantics and the second composite semantics is the correlation between the first node set and the second node set; A virtual node setting module 200, used to set a first virtual node and a second virtual node for the first composite semantics and the second composite semantics respectively, wherein the virtual node is a hyponym of all nodes in the corresponding node set; A first quantification module 300, configured to quantify a first relevance of the first virtual node to all nodes in the first node set, wherein the relevance between nodes is a mutual inference probability between the nodes; A second quantification module 400 is used to quantify the relevance of the nodes in the first node set to the nodes in the second node set based on the graph features of the ontology and the information amount of the nodes in the first node set and the second node set; A third quantization module 500, configured to quantify a third relevance of the nodes in the second node set to the second virtual node based on the amount of information shared by the nodes in the second node set; The fourth quantization module 600 is used to quantify the fourth correlation of the first virtual node to the second virtual node, ie, the correlation of the first composite semantics and the second composite semantics, based on the transitivity of the inference probability, the first correlation, the second correlation and the third correlation.

[0046] In the embodiment of the present invention, the semantic node mapping module 100 is specifically used for: Decomposing the first compound semantics and the second compound semantics into simple semantics that can be directly mapped to nodes, wherein the simple semantics are nouns or adjectives, and the semantic fusion of uncommon words is also included before decomposition; The simple semantics corresponding to the first compound semantics and the second compound semantics are respectively mapped to the first node set and the second node set of the semantic network by calling the vocabulary node mapping method of the ontology.

[0047] In an embodiment of the present invention, the relationship between a node set and a node is expressed as the relationship between a composite semantics and a sub-semantic. The existence of the composite semantics indicates that all sub-semantics exist simultaneously. A virtual node refers to a node that does not exist in the entity but can correspond to the actual composite semantics. A virtual node is a hyponym of all nodes in a node set. The occurrence of the virtual node indicates the occurrence of all nodes in the corresponding node set. The correlation between two node sets can be converted into the correlation between two virtual nodes.

[0048] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A composite semantic relevance quantification method based on ontology, characterized in that: The method comprises: S1. Mapping the first composite semantics and the second composite semantics to the first node set and the second node set of the ontology in the semantic network after preprocessing, respectively, wherein the correlation between the first composite semantics and the second composite semantics is the correlation between the first node set and the second node set; S2, setting a first virtual node and a second virtual node for the first composite semantics and the second composite semantics respectively, wherein the correlation between the first composite semantics and the second composite semantics is the correlation of the first virtual node to the second virtual node, and the virtual node is a hyponym of all nodes in the corresponding node set; S3. quantify a first correlation of the first virtual node to all nodes in the first node set, wherein the correlation between nodes is a mutual inference probability between nodes; S4. quantifying a second correlation between the nodes in the first node set and the nodes in the second node set based on the graph features of the ontology and the information amount of the nodes in the first node set and the second node set; S5. quantifying a third relevance of the nodes in the second node set to the second virtual node based on the amount of information shared between the nodes in the second node set; S6. Based on the transitivity of the inference probability, the first correlation, the second correlation and the third correlation, quantify the fourth correlation of the first virtual node to the second virtual node, that is, the correlation between the first composite semantics and the second composite semantics.

2. The ontology-based composite semantic relevance quantification method according to claim 1, characterized in that: Step S1 includes: S11, decomposing the first compound semantics and the second compound semantics into simple semantics that can be directly mapped to nodes, and before decomposing, the process also includes semantic fusion of uncommon words, wherein the simple semantics are nouns or adjectives; S12. Map the simple semantics corresponding to the first compound semantics and the second compound semantics to the first node set and the second node set of the semantic network respectively by calling the vocabulary node mapping method of the ontology.

3. The ontology-based composite semantic relevance quantification method according to claim 1, characterized in that: The relationship between a node set and a node is expressed as the relationship between a composite semantics and a sub-semantic. The existence of a composite semantics indicates that all sub-semantics exist simultaneously. A virtual node refers to a node that does not exist in the ontology but can correspond to the actual composite semantics. A virtual node is a hyponym of all nodes in a node set. The occurrence of a virtual node indicates the occurrence of all nodes in the corresponding node set. The correlation between two node sets can be converted into the correlation between two virtual nodes.

4. The ontology-based composite semantic relevance quantification method according to claim 1, characterized in that: The correlation between nodes is the mutual inference probability between nodes , if the node The semantics represented can be used to infer the node The probability of the semantics represented is , then the node With Node The correlation is: = （1）； Inference Probability The following two conditions should be met: ① Transitivity: Assume that there is a node , then: = （2）； ②Probability additivity: Assume that there are nodes , then: = （3）； A virtual node is a hyponym of all nodes in a node set, and the occurrence of the virtual node indicates the occurrence of all nodes in the corresponding node set, that is, the correlation of the virtual node to all nodes in the node set is 1; in step S3, the first correlation of the first virtual node to all nodes in the first node set is 1.

5. The ontology-based composite semantic relevance quantification method according to claim 1, characterized in that: In step S4, the second correlation of the nodes in the first node set to the nodes in the second node set is quantified by the following method, that is, the correlation between the nodes in the ontology is quantified, and the correlation between the nodes is the probability that the starting node infers the end node through the relationship between the nodes: S41, perform graph traversal according to the starting node, set the longest path from the starting node to the end node, use a stack to record the current path, and set two sets to respectively record the nodes and node relationships of the current path; S42, after traversing to the end node under the specified path length, recording the nodes in the stack and the relationship between the nodes in the two sets, and constructing the sensitive value transfer subgraph of the current path after the traversal is completed; S43. Calculate the correlation between nodes in the sensitive value transfer subgraph of each path. When a node has a relationship with multiple nodes, the calculation formula for the correlation between nodes of each path is: （4）； In formula (4), Represents a slave node To Node The path weight is the inference probability, that is, the correlation. Representation Node The set of all hyponyms of Represents the parent node The amount of information, The target node The amount of information of a node is determined based on the graph features of the ontology or based on a corpus; S44. If there is a single path between the starting node and the end node, the correlation between the starting node and the end node is the product of all path weights on the path, and the calculation formula is: （5）； In formula (5), From the starting node To the end node The correlation of k represents the node To Node The longest path length between Represents a slave node To the adjacent node Path weight, i and k are positive integers; S45. If there are multiple paths between the start node and the end node in the same sensitive value transfer subgraph, the correlation between the start node and the end node is the sum of the correlations inferred from the multiple paths, and the correlation does not exceed 1. The calculation formula is: （6）； In formula (6), From the starting node To the end node The correlation of k represents the node To Node The longest path length between them, q represents the starting node To the end node The number of paths that exist in the same sensitive value transfer subgraph, Represents a slave node To the adjacent node The path weight of , i, j, k, q are positive integers.

6. The ontology-based composite semantic relevance quantification method according to claim 1, characterized in that: In step S5, the third relevance of the nodes in the second node set to the second virtual node is quantified by the following method: When there are two nodes in the node set When the node To virtual node The correlation is two nodes The correlation between them is calculated as: （7）； When the node set has at least three nodes, the node To virtual node The calculation formula of the correlation is: （8）； In formulas (7) and (8), it is assumed that the event The corresponding composite semantics is , the simple semantics corresponding to events A and B are , composite semantics The corresponding node set is ,event The corresponding semantics is , semantics The corresponding node set is , represents the probability of event S occurring when event A occurs, Indicates that event A occurs The probability of occurrence, the degree of mutual influence of multiple nodes in a node set is related to the amount of information shared between the nodes, and the amount of information shared between the nodes is determined by the minimum common ancestor node of the nodes. express, The semantics represented is , k represents arrive The path length.

7. The ontology-based composite semantic relevance quantification method according to claim 6, characterized in that: In step S6, the correlation between the first composite semantics and the second composite semantics is the correlation between the first node set and the second node set, and is also the correlation between the first virtual node and the second virtual node, and is calculated as follows: ； In formula (9), represents the first composite semantics, represents the second composite semantics, represents the first node set, represents the second node set, represents the first virtual node, Represents the second virtual node.

8. An ontology-based composite semantic relevance quantification system, used to implement the method according to any one of claims 1 to 7, characterized in that: The system comprises: A semantic node mapping module, used for mapping the first composite semantics and the second composite semantics to the first node set and the second node set of the ontology in the semantic network after preprocessing, wherein the correlation between the first composite semantics and the second composite semantics is the correlation between the first node set and the second node set; a virtual node setting module, used to set a first virtual node and a second virtual node for the first composite semantics and the second composite semantics, respectively, wherein the correlation between the first composite semantics and the second composite semantics is the correlation of the first virtual node to the second virtual node, and the virtual node is a hyponym of all nodes in the corresponding node set; A first quantification module, configured to quantify a first correlation of the first virtual node to all nodes in the first node set, wherein the correlation between nodes is a mutual inference probability between nodes; A second quantification module, configured to quantify a second correlation between the nodes in the first node set and the nodes in the second node set based on the graph features of the ontology and the information amounts of the nodes in the first node set and the second node set; A third quantization module, configured to quantify a third relevance of the nodes in the second node set to the second virtual node based on the amount of information shared between the nodes in the second node set; The fourth quantization module is used to quantify the fourth correlation of the first virtual node to the second virtual node, that is, the correlation between the first composite semantics and the second composite semantics, based on the transitivity of the inference probability, the first correlation, the second correlation and the third correlation.

9. The ontology-based composite semantic relevance quantification system according to claim 8, characterized in that: The semantic node mapping module is specifically used for: Decomposing the first compound semantics and the second compound semantics into simple semantics that can be directly mapped to nodes, and before decomposing, the method further includes semantic fusion of uncommon words, wherein the simple semantics are nouns or adjectives; The simple semantics corresponding to the first compound semantics and the second compound semantics are respectively mapped to the first node set and the second node set of the semantic network by calling the vocabulary node mapping method of the ontology.

10. The ontology-based composite semantic relevance quantification system according to claim 8, characterized in that: The relationship between a node set and a node is expressed as the relationship between a composite semantics and a sub-semantic. The existence of a composite semantics indicates that all sub-semantics exist simultaneously. A virtual node refers to a node that does not exist in the ontology but can correspond to the actual composite semantics. A virtual node is a hyponym of all nodes in a node set. The occurrence of a virtual node indicates the occurrence of all nodes in the corresponding node set. The correlation between two node sets can be converted into the correlation between two virtual nodes.

Images