CN112733874B - Suspicious vehicle discrimination method based on knowledge graph reasoning - Google Patents

Suspicious vehicle discrimination method based on knowledge graph reasoning Download PDF

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CN112733874B
CN112733874B CN202011144715.6A CN202011144715A CN112733874B CN 112733874 B CN112733874 B CN 112733874B CN 202011144715 A CN202011144715 A CN 202011144715A CN 112733874 B CN112733874 B CN 112733874B
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俞山川
谢耀华
闫禹
周欣
周健
王少飞
涂耘
陈晓利
叶青
陈晨
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China Merchants Chongqing Communications Research and Design Institute Co Ltd
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Abstract

The invention discloses a suspicious vehicle distinguishing method based on knowledge graph reasoning, which comprises the following steps: s1: the method comprises the steps of obtaining suspicious vehicle knowledge, representing the suspicious vehicle knowledge in a triple (h, r, t) mode based on a knowledge graph construction mode, and establishing a suspicious vehicle knowledge graph; wherein h represents a head entity, r represents a relationship, and t represents a tail entity; s2: embedding the triple suspicious vehicle knowledge graph into a vector space in a fusion manner to obtain a triple expression vector; s3: constructing a triple relation model, training the triple relation model, and determining the vector similarity between the relation r of the entity h and the tail entity t according to a relation score function; s4: and constructing a triple path model, training the triple path model, and determining the reliability between the relation r of the entity h and the tail entity t according to a path score function of the triple path model, wherein the path with the highest path score is a reasoning result.

Description

Suspicious vehicle discrimination method based on knowledge graph reasoning
Technical Field
The invention relates to a suspicious vehicle distinguishing method based on knowledge graph reasoning.
Background
With the establishment of an intelligent management system/platform of management departments such as an expressway and the like, managers can obviously improve the capacity and dimensionality in the aspects of automatic vehicle feature identification and automatic historical behavior recording, and the establishment of a large number of electronic files for vehicles becomes possible. However, the relevance between vehicles and potential traffic accidents of vehicles is lack of deep mining, and the management department is difficult to perform 'capture' and fine management on suspicious vehicles.
Disclosure of Invention
The invention aims to provide a suspicious vehicle distinguishing method based on knowledge graph reasoning, which aims to solve the problem that the suspicious vehicle is difficult to effectively identify at present.
In order to solve the technical problem, the invention provides a suspicious vehicle discrimination method based on knowledge graph reasoning, which comprises the following steps:
s1: the method comprises the steps of obtaining suspicious vehicle knowledge, representing the suspicious vehicle knowledge in a triple (h, r, t) mode based on a knowledge graph construction mode, and establishing a suspicious vehicle knowledge graph; wherein h represents a head entity, r represents a relationship, and t represents a tail entity;
s2: embedding the triple suspicious vehicle knowledge graph into a vector space in a fusion manner to obtain a triple expression vector;
s3: constructing a triple relation model, training the triple relation model, and determining the vector similarity between the relation r of the entity h and the tail entity t according to a relation score function;
s4: and constructing a triple path model, training the triple path model, and determining the reliability between the relation r of the entity h and the tail entity t according to a path score function of the triple path model, wherein the path with the highest path score is an inference result.
Further, constructing the triplet relation model specifically includes:
and establishing one-to-one, one-to-many, many-to-one or many-to-many relationship between the entity h and the entity t through mapping of the entity space and the relationship space.
Further, according to a relation score function f r (h, t) determining the vector similarity between the relation r of the entity h and the tail entity t, said relation scoring function f r (h, t) is calculated using formula (1):
Figure GDA0004055068540000021
wherein w h 、w t 、w r Mapping functions between entity/relationship representations; i is an identity matrix; each vector satisfies the following constraints:
‖h‖ 2 ≤1,‖r‖ 2 ≤1,‖t‖ 2 ≤1 (2)
Figure GDA0004055068540000022
further, the training objective of the triplet relation model is a minimization loss function, which is expressed as follows:
Figure GDA0004055068540000023
s.t.
Figure GDA0004055068540000024
wherein the content of the first and second substances,
Figure GDA0004055068540000025
representing a set of triples; />
Figure GDA0004055068540000026
A negative sample representing (h, r, t) is obtained by randomly replacing a head entity h or a tail entity t in the training process; and, if +>
Figure GDA0004055068540000027
y hrt ＝1；/>
Figure GDA0004055068540000028
y hrt ＝-1。
Further, constructing the triplet path model specifically includes:
according to the difference between the entities h and tGenerates path representation according to the relation between the paths and according to the characteristic value function s of each path h,p(e) Establishing a series of paths, wherein the path set is represented as:
p(h,t)＝{…,p i (h,t),…}
wherein p is i (h,t)＝(h,r 1 ,e 1 ,r 2 ,e 2 ,…,e k-1 ,r k T), k is the path length.
Further, a path score function f is used p (h, t) to determine the reliability between the relation r of the entity h and the tail entity t, the path score function f p (h, t) is calculated by the formula (5):
Figure GDA0004055068540000031
probability of each sample being
Figure GDA0004055068540000032
Further, the training objective of the triplet path model is a minimization loss function, which is expressed as follows:
Figure GDA0004055068540000033
the invention has the beneficial effects that: the invention judges whether a certain vehicle running in a certain area is a suspicious vehicle or not based on knowledge map reasoning, and accordingly, the invention carries out early warning to relevant management departments such as a highway and the like, thereby improving the capturing and emergency handling capabilities of the management departments on the suspicious vehicle.
Detailed Description
A suspicious vehicle discrimination method based on knowledge graph reasoning,
s1: acquiring suspicious vehicle knowledge, representing the suspicious vehicle knowledge in a triple (h, r, t) mode based on a knowledge map construction mode, and establishing a suspicious vehicle knowledge map; wherein h represents a head entity, r represents a relationship, and t represents a tail entity;
wherein the suspicious vehicle knowledge comprises: a vehicle in an area (e.g., a section of highway) meeting one of three conditions: (1) the vehicle type, the license plate and the driver characteristics are not matched with the historical records of the database; (2) have occurred in historical accident scenes; (3) the dangerous driving behaviors such as traffic violation like overspeed and overload or the like changing lanes in lushikim are generated for many times; (4) often co-occurring with a vehicle that has been determined to be suspect, i.e., a companion vehicle to the suspect vehicle.
S2: embedding the triple suspicious vehicle knowledge graph into a vector space in a fusion manner to obtain a triple expression vector;
by using
Figure GDA0004055068540000047
Represents a collection of entities, is selected based on the presence of a particular entity>
Figure GDA0004055068540000041
Representing a collection of relationships. Representing a pair of embedded relationships using a triplet (h, r, t), wherein +>
Figure GDA0004055068540000042
Indicates head entity, is present>
Figure GDA0004055068540000043
Represents a relationship, is>
Figure GDA0004055068540000044
Representing the tail entity. Set of triples is used to +>
Figure GDA0004055068540000045
And (4) showing. Thus, each entity and relationship in the knowledge-graph is represented as a vector. Examples are as follows:
if r is the relationship of vehicle type, then (h, r, t) can be expressed as (Yu A00000, vehicle type is car); if r is the "driver is" relationship, (h, r, t) can be represented as (Yu A00000, driver is, driver ID 1); if r is a "body feature is" relationship, (h, r, t) can be represented as (driver ID 1, body feature is, thin); if r is "occurred at the accident scene", then (h, r, t) can be expressed as (Yu A00000, occurred at the accident scene, accident ID 1); if r is "multiple traffic violations," then (h, r, t) can be expressed as (Yu A00000, multiple traffic violations, 0 or 1); if r is "present at location", then (h, r, t) can be expressed as (Yu A00000, present at location, K1+ 120); if r is "present at time", then (h, r, t) can be expressed as (Yu A00000, present at time, YYYY-MM-DDhh: MM: ss); and so on.
S3: constructing a triple relation model, training the triple relation model, and determining the vector similarity between the relation r of the entity h and the tail entity t according to a relation score function;
1. triple relationship model building
The ranD model establishes one-to-one, one-to-many, many-to-one or many-to-many relationships between h and t through the mapping of the entity space and the relationship space. The vector similarity between the relations r is measured by a score function. Score function f r (h, t) is calculated by the formula (1):
Figure GDA0004055068540000046
wherein, w h 、w t 、w r Mapping functions between entity/relationship representations; and I is an identity matrix. Each vector satisfies the following constraints:
‖h‖ 2 ≤1,‖r‖ 2 ≤1,‖t‖ 2 ≤1 (2)
Figure GDA0004055068540000051
2. triple relationship model training
The training goal of the triplet relation model is to minimize the loss function, which is expressed as follows:
Figure GDA0004055068540000052
s.t.
Figure GDA0004055068540000053
wherein the content of the first and second substances,
Figure GDA0004055068540000054
representing a set of triples; />
Figure GDA0004055068540000055
Negative samples representing (h, r, t) are obtained by randomly replacing a head entity h or a tail entity t in the training process; and, if +>
Figure GDA0004055068540000056
y hrt ＝1；/>
Figure GDA0004055068540000057
y hrt ＝-1。
S4: and constructing a triple path model, training the triple path model, and determining the reliability between the relation r of the entity h and the tail entity t according to a path score function of the triple path model, wherein the path with the highest path score is an inference result.
For example, if the suspicious vehicle feature knowledge graph contains relationships (li x, driving, yu a 00000), (yu a00000, affiliated to Chongqing shipping Co., ltd.), then the missing relationships (li x, working at Chongqing shipping Co., ltd.) may be obtained.
The invention calculates the characteristic value function s of each path based on a random walk model h,P(t) Thereby establishing a series of paths. A path P is defined by a series of relationships r 1 ,…,r l ,…,r n Consists of the following components:
Figure GDA0004055068540000058
wherein, T n-1 Is a relation r n Scope of action of, T n-1 While being in relation r n-1 Value range of (i.e. T) n-1 ＝ran(r n )＝dom(r n-1 ). Scope and value of the relationship, i.e. type of entity, T 0 ＝{h}，T n = t. Eigenvalue function s h,P(t) Is the probability that the tail entity t can be reached starting from the head entity h along path P. When the path goes to any intermediate entity e, s h,P(e) The updating method is
Figure GDA0004055068540000061
Wherein, in the initial stage of random walk, if e ∈ P, s h,P(e) =1; otherwise, s h,P(e) =0; i (e', e) is an indicator function if r l (e', e) present, I (r) l (e', e)) =1; otherwise, I (r) l (e′,e))＝0；
For the relation r, a series of path characteristics P are obtained through a random walk algorithm r ＝{P 1 ,…,P n A score function is then built for each training sample (i.e. a combination of head and tail entities) under relation r:
Figure GDA0004055068540000062
the probability for each sample is:
Figure GDA0004055068540000063
the minimization loss function is:
min w k (y k log P k +(1-y k )log(1-P k )) (7)
wherein, y k For training the sample (h) k ,t k ) Whether or not there is a flag of relation r, y k =1, if triplet (h) k ,r,t k ) (ii) present; otherwise y k ＝0。
The method and the device can well establish the complex relations such as the relations of 1 to N, N to 1, N to N and the like, are simpler and higher in calculation efficiency compared with other methods of the same type, and are suitable for establishing the mass suspicious vehicle data knowledge graph in the management department platforms such as the highway and the like. The model training of the method is based on open world assumption, the effect on the incomplete knowledge graph is better, and in the model training fine tuning, the model based on the open world assumption has better effect.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.

Claims (4)

1. A suspicious vehicle discrimination method based on knowledge graph reasoning is characterized by comprising the following steps:
s1: acquiring suspicious vehicle knowledge, representing the suspicious vehicle knowledge in a triple (h, r, t) mode based on a knowledge map construction mode, and establishing a suspicious vehicle knowledge map; wherein h represents a head entity, r represents a relationship, and t represents a tail entity;
s2: embedding the triple suspicious vehicle knowledge graph into a vector space in a fusion manner to obtain a triple expression vector;
s3: constructing a triple relation model, training the triple relation model, and determining the vector similarity between the relation r of the entity h and the tail entity t according to a relation score function;
s4: constructing a triple path model, training the triple path model, and determining the reliability between the relation r of an entity h and a tail entity t according to a path score function of the triple path model, wherein the path with the highest path score is a reasoning result; step S4 specifically includes:
calculating a characteristic value function s of each path based on a random walk model h，P(t) Thereby establishing a series of paths; a path P is defined by a series of relationships r 1 ，...，r l ，...，r n Consists of the following components:
Figure QLYQS_1
wherein, T n-1 Is a relation r n Scope of action of (1), T n-1 While being in the relationship r n-1 Value range of (i.e. T) n-1 ＝ran(r n )＝dom(r n-1 ) Scope and value of a relationship, i.e. type of entity, T 0 ＝{h}，T n = t }; function of eigenvalues s h，P(t) Is the probability that the tail entity t can be reached starting from the head entity h along path P; when the path goes to any intermediate entity e, s h，P(e) The updating method is
Figure QLYQS_2
Wherein, in the initial stage of random walk, if e ∈ P, s h，P(e) =1; otherwise, s h，P(e) =0; i (e', e) is an indicator function if r l (e', e) present, I (r) l (e', e)) =1; otherwise, I (r) l (e′，e))＝0；
For the relation r, a series of path features P are obtained through a random walk algorithm r ＝{P 1 ，...，P n A score function is built for each training sample under relation r:
Figure QLYQS_3
the probability for each sample is:
Figure QLYQS_4
the training target of the triplet path model is a minimization loss function, which is expressed as follows:
Figure QLYQS_5
2. the method for distinguishing suspicious vehicles based on knowledge graph reasoning according to claim 1, wherein the constructing of the triple relationship model specifically comprises:
and establishing one-to-one, one-to-many, many-to-one or many-to-many relationship between the entity h and the entity t through mapping of the entity space and the relationship space.
3. The method of claim 2, wherein the method of suspect vehicle discrimination based on knowledgegraph reasoning is based on a relational score function f r (h, t) determining the vector similarity between the relation r of the entity h and the tail entity t, the relation score function f r (h, t) is calculated using formula (1):
Figure QLYQS_6
wherein, w h 、w t 、w r Mapping functions between entity/relationship representations; i is an identity matrix; each vector satisfies the following constraints:
||h|| 2 ≤1，||r|| 2 ≤1，||t|| 2 ≤1(2)
Figure QLYQS_7
4. the method of claim 3, wherein the training objective of the triplet relational model is a minimization loss function, the minimization loss function being expressed as follows:
Figure QLYQS_8
s.t.
Figure QLYQS_9
wherein the content of the first and second substances,
Figure QLYQS_10
representing a set of triples; />
Figure QLYQS_11
A negative sample representing (h, r, t) is obtained by randomly replacing a head entity h or a tail entity t in the training process; and, if +>
Figure QLYQS_12
Figure QLYQS_13
y hrt ＝-1。/>
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