CN114036307A - Knowledge graph entity alignment method and device - Google Patents

Knowledge graph entity alignment method and device Download PDF

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CN114036307A
CN114036307A CN202111095446.3A CN202111095446A CN114036307A CN 114036307 A CN114036307 A CN 114036307A CN 202111095446 A CN202111095446 A CN 202111095446A CN 114036307 A CN114036307 A CN 114036307A
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entities
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CN114036307B (en
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曾开胜
李涓子
侯磊
冯铃
唐杰
许斌
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Abstract

The invention provides a method and a device for aligning knowledge graph entities, which comprise the following steps: acquiring data of two knowledge maps to be fused; performing neighborhood aggregation entity representation learning on the data of the two knowledge graphs respectively to obtain entity representations of all entities in the two knowledge graphs; performing relation representation learning for enhancing entity semantics according to entity representation, and modeling the relation between entities to obtain entity relation representation; carrying out concept and concept hierarchy system representation learning according to entity representation, and modeling the relationship between entities and concepts and between concepts to obtain concept and concept hierarchy system representation; and constraining the entity representation in the entity alignment process based on the vector distance through the entity relationship representation, the concept and the concept hierarchy representation to obtain the result of the alignment of the two knowledge graph entities. The concept and the concept hierarchy are fused into an entity alignment framework and play a role, and the accuracy of entity alignment is improved.

Description

Knowledge graph entity alignment method and device
Technical Field
The invention relates to the field of computer artificial intelligent natural language processing, in particular to a knowledge graph entity alignment method and device.
Background
The knowledge map fusing multi-language and multi-knowledge source information becomes an important knowledge source for a plurality of artificial intelligence applications such as information extraction, intelligent question answering and the like. Entity alignment attracts the interest of many scholars and becomes an important research problem in order to more efficiently fuse information with overlapping and complementary knowledge-graphs. Many knowledge maps provide rich structural knowledge for different applications, and due to different construction purposes, the knowledge maps have great heterogeneity and also contain some complementary knowledge. In order to better support tasks such as a cross-language question-answering system and a cross-language recommendation system on an upper layer, the integration of different knowledge maps becomes an important research direction. Entity alignment is a key technology of knowledge graph fusion.
In the traditional method for aligning knowledge graph entities, a series of similarity is calculated mainly by utilizing entity text information, entity attribute information, entity network structure information and the like, and then whether a given entity pair is an equivalent entity is judged by a manually set threshold or a classification algorithm in machine learning, so that error conditions of entity alignment belonging to different concepts can occur in the process of an entity alignment task, and the accuracy of entity alignment is influenced.
Disclosure of Invention
The invention provides a knowledge graph entity alignment method and a knowledge graph entity alignment device, which are used for solving the defect of low entity alignment accuracy in a knowledge graph in the prior art, realizing the alignment of entities belonging to the same concept under the constraint of the concept and improving the accuracy of entity alignment.
The invention provides a knowledge graph entity alignment method, which comprises the following steps:
acquiring data of two knowledge maps to be fused;
performing neighborhood aggregation entity representation learning on the data of the two knowledge graphs respectively to obtain entity representations of all entities in the two knowledge graphs;
performing relation representation learning for enhancing entity semantics according to the entity representation, and modeling the relation between the entities to obtain entity relation representation;
carrying out concept and concept hierarchy system representation learning according to the entity representation, and modeling the relationship between entities and concepts and between concepts to obtain concept and concept hierarchy system representation;
and constraining the entity representation in the entity alignment process based on the vector distance through entity relationship representation, concept and concept hierarchy representation to obtain the result of aligning the two knowledge graph entities.
According to the method for aligning knowledge graph entities, provided by the invention, data of two knowledge graphs are a head entity, a tail entity and a relational triple set of the relationship between the two entities; the learning of the entity representation of neighborhood aggregation is performed on the data of the two knowledge graphs respectively to obtain the entity representation of each entity in the two knowledge graphs, and the learning specifically comprises the following steps:
encoding each of two of said knowledge-maps using an attention-seeking neural network, entity eiThe vector in the l +1 th layer network is represented as
Figure BDA0003269032640000021
The calculation method is as follows:
Figure BDA0003269032640000022
wherein the content of the first and second substances,
Figure BDA0003269032640000023
is the weight of the l-th network, the entity initial vector
Figure BDA0003269032640000024
From the entity representation matrix
Figure BDA0003269032640000025
d is the dimension of the vector representation, σ () is a non-linear activation function,
Figure BDA0003269032640000026
is entity e in the l-th networkiAnd ejAttention weight between;
calculating attention coefficient by using two matrixes to perform linear transformation on head entity and tail entity respectively
Figure BDA0003269032640000027
Figure BDA0003269032640000028
Wherein the content of the first and second substances,
Figure BDA0003269032640000031
is a parameter matrix of two linear transformationsTFor matrix transpose operations, LeakyReLU (.) is a nonlinear activation function;
by combining entities eiThe attention coefficient with the neighbor entity is normalized to obtain the attention weight between the entities
Figure BDA0003269032640000032
Figure BDA0003269032640000033
Obtaining an entity e through an L-layer graph convolution networkiVector representation of converged domain entity information
Figure BDA0003269032640000034
H(L)The matrix is represented for the neighborhood aggregated entities.
According to the knowledge graph entity alignment method provided by the invention, the relation representation learning for enhancing entity semantics is carried out according to the entity representation, the relation between the entities is modeled to obtain the entity relation representation, and the method specifically comprises the following steps:
representing entities and relations to the same through a knowledge representation translation model TransEA vector space for each of the relational triplets (e)h,r,et) E.g. T, calculating a rationality score:
Figure BDA0003269032640000035
wherein e ishAnd etRepresenting two entities, r represents an entity ehAnd entity etThe relationship between;
optimization objective O of applying interval ordering-based loss function as knowledge representation translation model TransER：
Figure BDA0003269032640000036
Wherein entity ehAnd etThe vector representation of (a) is taken from the entity representation matrix H of the neighborhood aggregation(L)The vector representation of the relation r is taken from a relation matrix needing to be learned
Figure BDA0003269032640000037
γ2>And 0 is an interval hyper-parameter, and the training negative sample set T' is obtained by carrying out type negative sampling on the relational triple set T.
According to the knowledge graph entity alignment method provided by the invention, the concept and concept hierarchy representation learning is carried out according to the entity representation, the relation between the entity and the concept and between the concept and the concept is modeled to obtain the concept and concept hierarchy representation, and the method specifically comprises the following steps:
establishing a box representation model as a concept and concept hierarchy representation, wherein the box embedding representation model represents a concept by a hyper-rectangle with aligned axes in space, the hyper-rectangle is provided with an internal space, and a concept c is formally defined as a vector in a real number space
Figure BDA0003269032640000041
The area covered by concept c is:
Figure BDA0003269032640000042
wherein, the relation of the order deviation is not more than the order deviation,
Figure BDA0003269032640000043
is the center of the hyper-rectangle c,
Figure BDA0003269032640000044
is the range offset of the hyper-rectangle c; when each element in off (c) is 0, BoxcDegenerating into a d-dimensional vector, i.e. a point in d-dimensional space, and representing the same as the entity, therefore, the relationship between the entity and the concept is described by using the point and the hyper-rectangle in space, and the entity vector belonging to the concept c can be represented as:
{ei∈Boxc|ei∈E}
judging whether an entity belongs to a concept, measuring by the distance between a spatial midpoint and a hyper-rectangle, giving an entity e and a concept c, and defining the distance between the entity e and the concept c as follows:
Figure BDA0003269032640000045
wherein, cmax＝Cen(c)+Off(c),cminCen (c) -off (c), from an external distance distoutside(e, c) and an internal distance distinside(e,c)Measuring the distance between an entity and a concept in two aspects, wherein the outer distance represents the distance between the entity and the boundary of a hyper-rectangle in which the concept is positioned, and the inner distance represents the distance between the entity and the center of the rectangle in which the concept is positioned; and 0<β<1 is a hyper-parameter for balancing the specific gravity of two types of distances;
optimization target O for defining instanceOf relationship between entity and conceptI：
Figure BDA0003269032640000051
WhereinVector representation of entity e is taken from the neighborhood aggregated entity representation matrix H(L)The vector representation of concept c is taken from the concept matrix to be learned
Figure BDA0003269032640000052
γ3>0 is a predefined interval hyperparameter, training the negative sample set L'instanceOfIs a set L of dependencies between entities and conceptsinstanceOfAnd carrying out random uniform sampling to obtain the target.
According to the knowledge graph entity alignment method provided by the invention, the concept and concept hierarchy representation learning is carried out according to the entity representation, the relation between the entity and the concept and between the concept and the concept is modeled to obtain the concept and concept hierarchy representation, and the method specifically comprises the following steps:
definition of the concept of upper and lower bits<ci,subclassOf,cj>Distance function of (d):
Figure BDA0003269032640000053
wherein, cy.max＝Cen(cy)+Off(cy),cy.min＝Cen(cy)-pff(cy) Y ∈ { i, j }, if concept ciIs completely covered with concept cjThe hyper-rectangles contain, the conceptual distance f between thembox(ci,cj)＝0；
Optimization objective O defining concepts and SubclassOf relationships between conceptsS：
Figure BDA0003269032640000054
According to the method for aligning the knowledge graph entities, which is provided by the invention, the entity representation is restricted in the entity aligning process based on the vector distance through the entity relationship representation, the concept and the concept hierarchy representation, so that the aligning result of the two knowledge graph entities is obtained, and the method specifically comprises the following steps:
the distance function between two entities is defined as:
Figure BDA0003269032640000055
wherein the content of the first and second substances,
Figure BDA0003269032640000056
l representing a vector1/L2Norm, applying interval ordering based loss function as optimization target O of entity alignmentE：
Figure BDA0003269032640000061
Wherein, [.]+Max {0, } denotes taking the maximum value between the input vector and 0, γ1>0 is a spacing hyperparameter, eiAnd ejThe vector representation of (a) is taken from the entity representation matrix H of the domain aggregation(L)The training negative sample S' is generated by nearest neighbor sampling of the set S by known pre-aligned equivalent entities between the two pre-fused knowledge-graphs.
According to the method for aligning the knowledge graph entities, which is provided by the invention, the entity representation is restricted in the entity aligning process based on the vector distance through the entity relationship representation, the concept and the concept hierarchy representation, so that the aligning result of the two knowledge graph entities is obtained, and the method specifically comprises the following steps:
obtaining two optimization targets of pre-fusion knowledge graph entity alignment according to the entity alignment optimization target, the entity-relationship optimization target, the entity-concept relationship optimization target and the concept-concept relationship optimization target:
O＝α1OE+α2OR+α3OI+α4OS
wherein, OE，OR，OI，OSOptimization of entity alignment, relationship representation, instanceOf relationship and subgradioasof relationship respectivelyTarget, alpha1，α2，α3，α4>0 is a weight parameter to balance the targets of each part.
The invention also provides a knowledge graph entity alignment device, comprising:
the system comprises a knowledge graph acquisition unit, a fusion unit and a fusion unit, wherein the knowledge graph acquisition unit is used for acquiring data of two knowledge graphs to be fused;
the entity representation acquisition unit is used for respectively carrying out neighborhood aggregation entity representation learning on the data of the two knowledge maps to obtain entity representations of all entities in the two knowledge maps;
the entity relationship expression acquisition unit is used for performing relationship expression learning for enhancing entity semantics according to the entity expression and modeling the relationship between the entities to obtain entity relationship expression;
the concept and concept hierarchy representation acquisition unit is used for carrying out concept and concept hierarchy representation learning according to the entity representation and modeling the relationship between the entity and the concept and the relationship between the concept and the concept to obtain concept and concept hierarchy representation;
and the entity alignment result acquisition unit is used for constraining the entity representation in the entity alignment process based on the vector distance through entity relationship representation and concept hierarchy representation to obtain the result of the alignment of the two knowledge graph entities.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for aligning knowledge-graph entities as described in any one of the above.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method for knowledge-graph entity alignment as described in any of the above.
The method and the device for aligning the knowledge graph entities fuse concepts and the concept hierarchy into the entity alignment framework and play a role in constraining in the entity alignment process, so that entities belonging to the same concept are aligned, and the accuracy of entity alignment is improved.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flow diagram of a method for knowledge-graph entity alignment provided by the present invention;
FIG. 2 is a schematic diagram of the structure of the knowledge-graph entity alignment process provided by the present invention;
FIG. 3 is a schematic diagram of a knowledge-graph entity alignment apparatus provided by the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Although knowledge-graph entity alignment technology has made great progress with the continuous development of technology, an important structural information in knowledge-graph is ignored in the current method: concepts and concept hierarchies. The concept is abstract description of things with certain similar characteristics in the knowledge graph, the concept and concept, and the upper and lower relations between the entity and the concept form a concept hierarchy, and the knowledge graphs such as DBpedia and YAGO have the concept and concept hierarchy. Unlike equivalent entity linking, the concept and concept hierarchy can provide assistance for entity alignment from another level.
In the embodiment of the invention, two knowledge graphs needing to be aligned are formally expressed as G1＝(E1，R1，T1) And G2＝(E2，R2，T2) In which EiRepresents a set of entities, RiRepresenting a set of relationships, TiA fact triplet that is composed of entities and relationships is represented (i.e.,<head entity, relationship, Tail entity>) The set, i ∈ {1, 2} is the sequence number of the two knowledge-graphs, respectively. Given EiAn entity e of (2), whose set of neighbour entities is formally denoted Ne＝{e′|(e，r，e′)∈Ti}∪{e′|(e′，r，e)∈Ti}. Formalizing a set of equivalent entity pairs between two knowledge graphs into
Figure BDA0003269032640000081
Wherein
Figure BDA0003269032640000082
Denotes e1And e2In reality, the entities have the same semantics, and are the same entity, that is, the equivalent entity pair is formed together. Formally representing a set of concepts to which an entity belongs
Figure BDA0003269032640000083
The conceptual hierarchy may be formally represented as L ═ LinstanceOf∪LsubclassOfWherein the set of dependencies between entities and concepts is
Figure BDA0003269032640000084
The upper and lower relation between concepts is formalized as
Figure BDA0003269032640000085
Wherein instanceOf represents that entity e is an entity of concept c, and subclassOf represents concept c1Is a concept c2A sub-concept of (1). Two knowledge maps G1And G2Merging into a large knowledge graph G for processing, and correspondingly formalizing entity set and relationship setThe fact ternary set is E ═ E1∪E2、R＝R1∪R2、T＝T1∪T2。
As shown in fig. 1, an embodiment of the present invention provides a method for aligning knowledge-graph entities, including:
step 110: acquiring data of two knowledge maps to be fused;
specifically, the data of the two to-be-fused knowledge graphs are a head entity, a tail entity, and a relational triple set in the form of < head entity, relation, tail entity > of the relation between the two entities.
Step 120: performing neighborhood aggregation entity representation learning on the data of the two knowledge graphs respectively to obtain entity representations of all entities in the two knowledge graphs;
specifically, the entity representation of the domain aggregation is learned, and the entity is encoded according to the graph structure of the knowledge graph, so that the entity vector representation embedded in the same space is obtained.
Step 130: performing relation representation learning for enhancing entity semantics according to the entity representation, and modeling the relation between the entities to obtain entity relation representation;
specifically, the relation representation learning of the entity semantics is enhanced, the relation between the entities is modeled, the semantic information coded by the entities is enriched, and the entity discrimination is improved.
Step 140: carrying out concept and concept hierarchy system representation learning according to the entity representation, and modeling the relationship between entities and concepts and between concepts to obtain concept and concept hierarchy system representation;
specifically, the concept and concept hierarchy represents learning, the concept and concept hierarchy is modeled to naturally establish a connection with an entity, and the entity alignment task is assisted by constraining the entity representation.
Step 150: and constraining the entity representation in the entity alignment process based on the vector distance through entity relationship representation, concept and concept hierarchy representation to obtain the result of aligning the two knowledge graph entities.
In the embodiment of the present invention, the process corresponding to step 120 specifically includes:
for better aggregation of entity characteristics, equivalent information between entities is passed in a Graph structure, a multi-layer Attention-seeking neural Network (GAT) pair is applied for encoding, and an entity eiThe vector in the l +1 th layer network is represented as
Figure BDA0003269032640000091
The calculation method is as follows:
Figure BDA0003269032640000092
wherein the content of the first and second substances,
Figure BDA0003269032640000101
is the weight of the l-th network, the entity initial vector
Figure BDA0003269032640000102
From the entity representation matrix
Figure BDA0003269032640000103
d is the dimension of the vector representation, σ () is a non-linear activation function, in particular ReLU () -max (0,) is chosen as the non-linear activation function,
Figure BDA0003269032640000104
is entity e in the l-th networkiAnd ejAttention weight between;
in the embodiment of the invention, two different matrixes are used for respectively carrying out linear transformation on two end entities to calculate the attention coefficient
Figure BDA0003269032640000105
Figure BDA0003269032640000106
Wherein the content of the first and second substances,
Figure BDA0003269032640000107
a parameter matrix for two different linear transformations, ()TFor matrix transpose operations, LeakyReLU (.) is a nonlinear activation function;
by combining entities eiThe attention coefficient with the neighbor entity is normalized to obtain the attention weight between the entities
Figure BDA0003269032640000108
Figure BDA0003269032640000109
Obtaining an entity e through an L-layer graph convolution networkiVector representation of converged domain entity information
Figure BDA00032690326400001010
H(L)The matrix is represented for the aggregated entities of the neighborhood, thereby resulting in a low-dimensional vector representation of the entities.
In step 120, the multi-layer graph attention encoder may better aggregate the features of the neighborhood entities via the graph attention neural network, such that the equivalence performance between entities is propagated throughout the graph.
In the embodiment of the present invention, the process corresponding to step 130 specifically includes:
the expression of the relation is introduced by selecting a classical knowledge expression translation model TransE, the entity and the relation are expressed to the same vector space, the relation is equivalent to the vector translation from a head entity to a tail entity, and the TransE is that each relation type three is each relation type triple (e)h,r,et) E.g. T, calculating a rationality score:
Figure BDA0003269032640000111
wherein e ishAnd etRepresenting two entities, r represents an entity ehAnd entity etThe relationship between;
optimization objective O of applying interval ordering-based loss function as knowledge representation translation model TransER：
Figure BDA0003269032640000112
Wherein entity ehAnd etThe vector representation of (a) is taken from the entity representation matrix H of the neighborhood aggregation(L)The vector representation of the relation r is taken from a relation matrix needing to be learned
Figure BDA0003269032640000113
γ2>And 0 is an interval hyper-parameter, and the training negative sample set T' is obtained by carrying out type negative sampling on the relational triple set T.
In step 130, the knowledge graph representation method TransE is used to introduce vector representation of the relationship between the entities and to bring constraints to the representation of the entities, enrich semantic information encoded by the entities and improve the discrimination of the entities.
In the embodiment of the present invention, the process corresponding to step 140 specifically includes:
due to the fact that the entity and the concept have hierarchical relations, the selected concept representation model has the capability of hierarchical representation. In embodiments of the present invention, a box-embedded representation model is proposed to model concepts and concept hierarchies.
Building a box representation model as a conceptual representation model, the box-embedded representation model representing a concept with a hyper-rectangle aligned with axes in space, the hyper-rectangle having an inner space, unlike points in space, whereby we can represent a concept as a hyper-rectangle, entities belonging to the concept are represented as points in the hyper-rectangle, and formally define concept c as a real spaceVector of
Figure BDA0003269032640000114
The area covered by concept c is:
Figure BDA0003269032640000121
wherein, the relation of the order deviation is not more than the order deviation,
Figure BDA0003269032640000122
is the center of the hyper-rectangle c,
Figure BDA0003269032640000123
is the range offset of the hyper-rectangle c; when each element in off (c) is 0, BoxcDegenerating into a d-dimensional vector, i.e. a point in d-dimensional space, and representing the same as the entity, therefore, the relationship between the entity and the concept is described by using the point and the hyper-rectangle in space, and the entity vector belonging to the concept c can be represented as:
{ei∈Boxc|ei∈E} (7)
judging whether an entity belongs to a concept, measuring by the distance between a spatial midpoint and a hyper-rectangle, giving an entity e and a concept c, and defining the distance between the entity e and the concept c as follows:
Figure BDA0003269032640000124
wherein, cmax＝Cen(c)+Off(c),cminCen (c) -off (c), from an external distance distoutside(e, c) and an internal distance distinside(e,c)Measuring the distance between an entity and a concept in two aspects, wherein the outer distance represents the distance between the entity and the boundary of a hyper-rectangle in which the concept is positioned, and the inner distance represents the distance between the entity and the center of the rectangle in which the concept is positioned; and 0<β<1 is a hyper-parameter for balancing the proportion of two types of distances, specifically, a smaller beta is set, the distance from an entity positioned in the hyper-rectangle to the center is reduced to be beta times of the range of the hyper-rectangle,the aim of weakening the internal distance and putting more attention on the external distance is thus achieved, but it is still necessary to measure the internal distance (i.e. β ≠ 0), since it is not desirable to have the range of any hyper-rectangle expand indefinitely, it is desirable for the entity representation under a concept to be as close as possible to it, and the entity representation not belonging to the concept to be as far away from it, thus defining an optimization objective O of the instanceOf relationship between the entity and the conceptI：
Figure BDA0003269032640000125
Wherein the vector representation of entity e is taken from the neighborhood aggregated entity representation matrix H(L)The vector representation of concept c is taken from the concept matrix to be learned
Figure BDA0003269032640000126
γ3>0 is a predefined interval hyperparameter, training the negative sample set L'instanceOfIs a set L of dependencies between entities and conceptsinstanceOfRandomly and uniformly sampling to obtain;
in order to make the learned concept representations have hierarchical relationships, i.e., properties of sublasof relationships, each group of concept representations having upper and lower relationships is constrained, and the hyper-rectangle of the upper concept can contain the hyper-rectangle of the lower concept, which naturally conforms to the characteristic that the concept located at the upper level in the concept hierarchy has a larger description range than the lower concept. Therefore, the concept of upper and lower bits is defined<ci，subclassOf,cj>Distance function of (d):
Figure BDA0003269032640000131
wherein, cy.nax＝Cen(cy)+Off(cy)，cy.min＝Cen(cy)-Off(cy) Y ∈ { i, j }, if concept ciIs completely covered with concept cjThe hyper-rectangles contain, the conceptual distance f between thembox(ci,cj)＝0；
Optimization objective O defining concepts and SubclassOf relationships between conceptsS：
Figure BDA0003269032640000132
In step 140, the embodiment of the invention applies a box embedded representation model to model concepts and a concept hierarchy, and represents and learns the concepts to naturally establish connection with the entities and help entity alignment by constraining entity representation.
In the embodiment of the present invention, the process corresponding to step 150 specifically includes:
although the same graph neural network is applied to different knowledge graphs to encode the entities, the entities of different knowledge graphs are still located in different vector spaces, in order to represent the entities to the same vector space, the distance between each pair of equivalent entity vector representations is reduced by using a pre-aligned equivalent entity pair set S, so that the aim of aligning the entities of the two graphs is achieved, and a distance function between the two entities is defined as follows:
Figure BDA0003269032640000133
wherein the content of the first and second substances,
Figure BDA0003269032640000134
l representing a vector1/L2Norm, applying interval ordering based loss function as optimization target O of entity alignmentE：
Figure BDA0003269032640000135
Wherein, [.]+Max {0, } denotes taking the maximum value between the input vector and 0, γ1>0 is a spacing hyperparameter, eiAnd ejThe vector representation of (a) is taken from the entity representation matrix H of the domain aggregation(L)The training negative sample S' is generated by nearest neighbor sampling of the set S by known pre-aligned equivalent entities between the two pre-fused knowledge-graphs.
In the embodiment of the present invention, the corresponding step 150 further includes: obtaining two optimization targets of pre-fusion knowledge graph entity alignment according to the entity alignment optimization target, the entity-relationship optimization target, the entity-concept relationship optimization target and the concept-concept relationship optimization target:
O＝α1OE+α2OR+α3OI+α4OS (14)
wherein, OE,OR,OI,OSRespectively corresponding to the optimization targets of entity alignment, relationship representation, instanceOf relationship and subbcloaseOf relationship, alpha1,α2,α3,α4>0 is a weight parameter to balance the targets of each part. In the embodiment of the invention, the number of layers of the graph neural network is set to be 3 (including input layers), and the initialized entity vector representation matrix X, the relation vector representation matrix R and the concept vector representation matrix C are all randomly initialized by adopting Xavier uniform distribution. This objective function is optimized to a minimum using the AdaGrad algorithm. Therefore, the entity alignment between the two knowledge graphs is completed, and therefore the fusion is realized.
The following describes a process and a result of a simulation experiment performed on the performance of the knowledge graph entity alignment method provided by the embodiment of the present invention. In the course of the following description, "C4 EA" is used as a marker for experiments carried out by the method of the example of the invention.
The specific experimental process is as follows:
1. introduction of data sets.
The method of the present embodiment was evaluated using a data set DBP15K that is disclosed and widely used in the art. Where the DBP15K contains three cross-language datasets constructed from different language versions of DBpedia, each dataset containing 15,000 pairs of equivalent entities. The relevant information of the data set is shown in table 1:
TABLE 1 correlation statistics of data sets
Figure BDA0003269032640000151
30% of the equivalent pairs were used for training in the experiment, and the remaining 70% were used for testing.
2. And (4) setting an experiment.
Consistent with the existing research work, Hits @ N and MRR are used to evaluate the experimental effect. Where Hits @ N represents the percentage of correct entities contained in the first N results of the alignment, and MRR (mean Recyclical rank) represents the average of the reciprocals of the correct entity ordering in all alignment results. The comparison method comprises the following steps: MTransE model, JAPE model, AlignEA model, GCN-Align model, KECG model, MuGNN model and AliNet model; and its own comparative model: c4EA (w/o Box). An ablation model C4EA (w/o Box), C4EA with the module for learning concept and concept hierarchy representation removed, was isolated from C4EA to explore the impact of adding concept and concept hierarchy on entity alignment. In the aspect of model parameters, the learning rate lambda of the AdaGrad algorithm is selected from {0.001, 0.005, 0.01, 0.05 }; selecting a representation dimension d of the vector from {100, 150, 200 }; selecting interval hyperparameter gamma from {1.0, 2.0, 3.0, 5.0}1,γ2,γ3(ii) a Selecting balance hyper parameter alpha from {0.2, 0.4, 0.6, 0.8, 1.0 })1,α2,α3，α4. For the entity representation learning of neighborhood aggregation, the sampling number of a negative case corresponding to a positive case is 25; for the relation representation learning and the concept and concept hierarchy representation learning of the enhanced entity semantics, the sampling number of the negative examples corresponding to one positive example is 2. Through experiments, the optimal parameter combination of the knowledge graph entity alignment model of the embodiment of the invention is found out: λ 0.005, d 200, γ1＝3.0，γ2＝3.0，γ3＝1.0，β＝0.02，α1＝1.0，α2＝0.8，α3＝0.6，α40.6, the distance measure in the method is selected from L2And (4) norm.
3. Experimental results and analysis.
By adopting the data sets and the experimental settings, the performance of entity alignment through the knowledge graph entity alignment model disclosed by the invention is tested on each data set and compared with the mainstream method. As shown in table 2, the evaluation results are the entity alignments. On each data set, C4EA is obviously superior to the comparative method under 3 evaluation indexes, and the accuracy and the stability of the device disclosed by the invention are proved.
TABLE 2 knowledge-graph entity alignment results (Hits @ N units%)
Figure BDA0003269032640000161
As can be seen from the results of table 2, the C4EA effect is superior to the comparative prior art method. C4EA combining learning of relationship representation and learning of concept and concept hierarchy representation of enhanced entity semantics can make full use of structural information in knowledge graph, make entity representation rich in more semantics, and facilitate distinguishing confusing entities when aligning, such as DBP15KFR-ENThe experimental results on the subdata set show that Hits @1 of C4EA is improved by 0.026 compared to AliNet and by 0.334 compared to the earliest entity alignment method MtransE. C4EA utilizes multilayer GAT to carry out entity representation learning of neighborhood aggregation, weakens the influence of local entities difficult to align on global alignment, achieves the effect of relieving the influence of map heterogeneity on entity alignment, and is applied to DBP15KJA-ENHits @10 of C4EA on the sub data set reached 0.892, which is much higher than 0.745 achieved with GCN-Align, which also uses graph neural networks but fails to overcome graph structure differences.
To explore the impact of increasing concept-to-concept hierarchy on entity alignment, an ablation model C4EA (w/o Box) was isolated from C4EA, and the experimental results listed in table 2 reflect the positive impact of concept-to-concept hierarchy representation learning on entity alignment. At DBP15KFR-EOn the subdata set, the Hits @1 of C4EA was raised from 0.550 to 0.578, the MRR was raised from 0.664 to 0.671, and the rise in MRR well reflects the positive contribution of the joint concept and concept hierarchy as a whole to entity alignment.
As shown in fig. 2, a schematic process diagram of performing an alignment task on each entity in two to-be-fused knowledge graphs according to the entity alignment method provided by the embodiment of the present invention is shown. The method for aligning knowledge graph entities based on the constraint of the concept and the concept hierarchy system starts from two knowledge graphs, and utilizes a multilayer graph attention neural network under the setting of shared parameters as a coder to carry out entity coding to obtain entity vector representation of field fusion; the classical knowledge representation translation model TransE is selected to introduce the relation of the knowledge map, the relation between the entities is effectively modeled, the semantic information coded by the entities is enriched, and the discrimination of entity representation is improved, so that the aim of enhancing the learning of the relation representation of entity semantics is fulfilled; the method comprises the steps of applying a box embedded representation model to model concepts and concept hierarchies of a knowledge graph, and performing representation learning on the concepts to establish direct connection between the concepts and representations of entities, so that final representations of the entities are influenced and constrained, and finally entity alignment tasks are completed. The method effectively overcomes the defect that the prior method cannot fully utilize the important structural information of the knowledge graph concept and the concept hierarchy, and more efficiently realizes the aim of aligning the cross-language knowledge graph entities.
The knowledge-graph entity alignment apparatus provided in the embodiment of the present invention is described below, and the knowledge-graph entity alignment apparatus described below and the knowledge-graph entity alignment method described above may be referred to with reference to each other, as shown in fig. 3, and the embodiment of the present invention provides a knowledge-graph entity alignment apparatus, including:
a knowledge graph obtaining unit 310, configured to obtain data of two knowledge graphs to be fused;
an entity representation obtaining unit 320, configured to perform neighborhood aggregation entity representation learning on data of the two knowledge maps respectively to obtain entity representations of entities in the two knowledge maps;
an entity relationship representation obtaining unit 330, configured to perform relationship representation learning for enhancing entity semantics according to the entity representation, and model a relationship between entities to obtain an entity relationship representation;
a concept and concept hierarchy representation obtaining unit 340, configured to perform concept and concept hierarchy representation learning according to the entity representation, and model relationships between entities and concepts, and between concepts and concepts to obtain a concept and concept hierarchy representation;
and an entity alignment result obtaining unit 350, configured to perform constraint on entity representation in an entity alignment process based on vector distance through entity relationship representation and concept-concept hierarchy representation, so as to obtain a result of aligning two knowledge graph entities.
An entity structure schematic diagram of an electronic device provided in an embodiment of the present invention is described below with reference to fig. 4, and as shown in fig. 4, the electronic device may include: a processor (processor)410, a communication Interface (Communications Interface)420, a memory (memory)430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 communicate with each other via the communication bus 440. Processor 410 may invoke logic instructions in memory 430 to perform a knowledge-graph entity alignment method comprising: acquiring data of two knowledge maps to be fused; performing neighborhood aggregation entity representation learning on the data of the two knowledge graphs respectively to obtain entity representations of all entities in the two knowledge graphs; performing relation representation learning for enhancing entity semantics according to the entity representation, and modeling the relation between the entities to obtain entity relation representation; carrying out concept and concept hierarchy system representation learning according to the entity representation, and modeling the relationship between entities and concepts and between concepts to obtain concept and concept hierarchy system representation; and constraining the entity representation in the entity alignment process based on the vector distance through entity relationship representation, concept and concept hierarchy representation to obtain the result of aligning the two knowledge graph entities.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the method for aligning knowledge-graph entities provided by the above methods, where the method includes: acquiring data of two knowledge maps to be fused; performing neighborhood aggregation entity representation learning on the data of the two knowledge graphs respectively to obtain entity representations of all entities in the two knowledge graphs; performing relation representation learning for enhancing entity semantics according to the entity representation, and modeling the relation between the entities to obtain entity relation representation; carrying out concept and concept hierarchy system representation learning according to the entity representation, and modeling the relationship between entities and concepts and between concepts to obtain concept and concept hierarchy system representation; and constraining the entity representation in the entity alignment process based on the vector distance through entity relationship representation, concept and concept hierarchy representation to obtain the result of aligning the two knowledge graph entities.
In yet another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to perform the methods for aligning knowledge-graph entities provided in the foregoing: acquiring data of two knowledge maps to be fused; performing neighborhood aggregation entity representation learning on the data of the two knowledge graphs respectively to obtain entity representations of all entities in the two knowledge graphs; performing relation representation learning for enhancing entity semantics according to the entity representation, and modeling the relation between the entities to obtain entity relation representation; carrying out concept and concept hierarchy system representation learning according to the entity representation, and modeling the relationship between entities and concepts and between concepts to obtain concept and concept hierarchy system representation; and constraining the entity representation in the entity alignment process based on the vector distance through entity relationship representation, concept and concept hierarchy representation to obtain the result of aligning the two knowledge graph entities.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for knowledge-graph entity alignment, comprising:
acquiring data of two knowledge maps to be fused;
performing neighborhood aggregation entity representation learning on the data of the two knowledge graphs respectively to obtain entity representations of all entities in the two knowledge graphs;
performing relation representation learning for enhancing entity semantics according to the entity representation, and modeling the relation between the entities to obtain entity relation representation;
carrying out concept and concept hierarchy system representation learning according to the entity representation, and modeling the relationship between entities and concepts and between concepts to obtain concept and concept hierarchy system representation;
and constraining the entity representation in the entity alignment process based on the vector distance through entity relationship representation, concept and concept hierarchy representation to obtain the result of aligning the two knowledge graph entities.
2. The method of knowledge-graph entity alignment of claim 1 wherein the data of two knowledge-graphs are a head entity, a tail entity and a set of relational triples of the relationship between the two entities; the learning of the entity representation of neighborhood aggregation is performed on the data of the two knowledge graphs respectively to obtain the entity representation of each entity in the two knowledge graphs, and the learning specifically comprises the following steps:
encoding each of two of said knowledge-maps using an attention-seeking neural network, entity eiThe vector in the l +1 th layer network is represented as
Figure FDA0003269032630000011
The calculation method is as follows:
Figure FDA0003269032630000012
wherein the content of the first and second substances,
Figure FDA0003269032630000013
is the weight of the l-th network, the entity initial vector
Figure FDA0003269032630000014
From the entity representation matrix
Figure FDA0003269032630000015
d is the dimension of the vector representation, σ () is a non-linear activation function,
Figure FDA0003269032630000016
is entity e in the l-th networkiAnd ejAttention weight between;
calculating attention coefficient by using two matrixes to perform linear transformation on head entity and tail entity respectively
Figure FDA0003269032630000021
Figure FDA0003269032630000022
Wherein the content of the first and second substances,
Figure FDA0003269032630000023
is a parameter matrix of two linear transformationsTFor matrix transpose operations, LeakyReLU (.) is a nonlinear activation function;
by combining entities eiThe attention coefficient with the neighbor entity is normalized to obtain the attention weight between the entities
Figure FDA0003269032630000024
Figure FDA0003269032630000025
Obtaining an entity e through an L-layer graph convolution networkiVector representation of converged domain entity information
Figure FDA0003269032630000026
H(L)The matrix is represented for the neighborhood aggregated entities.
3. The method of knowledge-graph entity alignment of claim 2, wherein performing relationship representation learning for enhancing entity semantics according to the entity representations, modeling relationships between entities to obtain entity relationship representations, specifically comprises:
representing the entity and the relation to the same vector space through a knowledge representation translation model TransE, and representing the entity and the relation to each relation type triple (e)h，r，et) E.g. T, calculating a rationality score:
Figure FDA0003269032630000027
wherein e ishAnd etRepresenting two entities, r represents an entity ehAnd entity etThe relationship between;
optimization objective O of applying interval ordering-based loss function as knowledge representation translation model TransER：
Figure FDA0003269032630000028
Wherein entity ehAnd etThe vector representation of (a) is taken from the entity representation matrix H of the neighborhood aggregation(L)The vector representation of the relation r is taken from a relation matrix needing to be learned
Figure FDA0003269032630000031
γ2If the interval is more than 0, the training negative sample set T' is obtained by carrying out type negative sampling on the relational triple set T.
4. The method for knowledge graph entity alignment according to claim 3, wherein the learning of concept and concept hierarchy representation according to the entity representation and modeling of entity-to-concept and concept-to-concept relationships to obtain concept-to-concept hierarchy representation specifically comprises:
establishing a box representation model as a concept and concept hierarchy representation, wherein the box embedded representation model represents a concept by a hyper-rectangle with aligned axes in space, the hyper-rectangle has an internal space, and a concept c is formally defined as a vector in a real number space
Figure FDA0003269032630000032
The area covered by concept c is:
Figure FDA0003269032630000033
wherein, the relation of the order deviation is not more than the order deviation,
Figure FDA0003269032630000034
is the center of the hyper-rectangle c,
Figure FDA0003269032630000035
is the range offset of the hyper-rectangle c; when each element in off (c) is 0, BoxcDegenerating into a d-dimensional vector, i.e. a point in d-dimensional space, and representing the same as the entity, therefore, the relationship between the entity and the concept is described by using the point and the hyper-rectangle in space, and the entity vector belonging to the concept c can be represented as:
{ei∈Boxc|ei∈E}
judging whether an entity belongs to a concept, measuring by the distance between a spatial midpoint and a hyper-rectangle, giving an entity e and a concept c, and defining the distance between the entity e and the concept c as follows:
Figure FDA0003269032630000036
wherein, Cmax＝Cen(c)+Off(c)，cminCen (c) -off (c), from an external distance distoutside(e, c) and an internal distance distinside(e，c)Measuring the distance between an entity and a concept in two aspects, wherein the outer distance represents the distance between the entity and the boundary of a hyper-rectangle in which the concept is positioned, and the inner distance represents the distance between the entity and the center of the rectangle in which the concept is positioned; beta is more than 0 and less than 1, which is a hyper-parameter for balancing the specific gravity of the two types of distances;
optimization target O for defining instanceOf relationship between entity and conceptI：
Figure FDA0003269032630000041
Wherein the vector representation of entity e is taken from the neighborhood aggregated entity representation matrix H(L)The vector representation of concept c is taken from the concept matrix to be learned
Figure FDA0003269032630000042
γ3> 0 is a predefined interval hyperparameter, training the negative sample set L'instanceOfIs a set L of dependencies between entities and conceptsinstanceOfAnd carrying out random uniform sampling to obtain the target.
5. The method for knowledge graph entity alignment according to claim 4, wherein the learning of concept and concept hierarchy representation according to the entity representation and modeling of entity-to-concept and concept-to-concept relationships to obtain concept-to-concept hierarchy representation specifically comprises:
define the top and bottomBit concept<ci，subclassOf，cj>Distance function of (d):
Figure FDA0003269032630000043
wherein, cy.max＝Cen(cy)+Off(cy)，cy.min＝Cen(cy)-Off(cy) Y ∈ { i, j }, if concept ciIs completely covered with concept cjThe hyper-rectangles contain, the conceptual distance f between thembox(ci，cj)＝0；
Optimization objective O defining concepts and SubclassOf relationships between conceptsS：
Figure FDA0003269032630000044
6. The method of aligning knowledge-graph entities according to claim 5, wherein the constraining the entity representation in the entity aligning process based on the vector distance through the entity relationship representation, the concept and the concept hierarchy representation to obtain the aligning result of the two knowledge-graph entities specifically comprises:
the distance function between two entities is defined as:
Figure FDA0003269032630000051
wherein the content of the first and second substances,
Figure FDA0003269032630000052
l representing a vector1/L2Norm, applying interval ordering based loss function as optimization target O of entity alignmentE：
Figure FDA0003269032630000053
Wherein, [.]+Max {0, } denotes taking the maximum value between the input vector and 0, γ1> 0 is the interval hyperparameter, eiAnd ejThe vector representation of (a) is taken from the entity representation matrix H of the domain aggregation(L)The training negative sample S' is generated by nearest neighbor sampling of the set S by known pre-aligned equivalent entities between the two pre-fused knowledge-graphs.
7. The method of aligning knowledge-graph entities according to claim 6, wherein the constraining the entity representation in the entity aligning process based on the vector distance through the entity relationship representation, the concept and the concept hierarchy representation to obtain the aligning result of the two knowledge-graph entities specifically comprises:
obtaining two optimization targets of pre-fusion knowledge graph entity alignment according to the entity alignment optimization target, the entity-relationship optimization target, the entity-concept relationship optimization target and the concept-concept relationship optimization target:
O＝α1OE+α2OR+α3OI+α4OS
wherein, OE，OR，OI，OSRespectively corresponding to the optimization targets of entity alignment, relationship representation, instanceOf relationship and subbcloaseOf relationship, alpha1，α2，α3，α4> 0 are weight parameters that balance the targets of the parts.
8. A knowledge-graph entity alignment apparatus, comprising:
the system comprises a knowledge graph acquisition unit, a fusion unit and a fusion unit, wherein the knowledge graph acquisition unit is used for acquiring data of two knowledge graphs to be fused;
the entity representation acquisition unit is used for respectively carrying out neighborhood aggregation entity representation learning on the data of the two knowledge maps to obtain entity representations of all entities in the two knowledge maps;
the entity relationship expression acquisition unit is used for performing relationship expression learning for enhancing entity semantics according to the entity expression and modeling the relationship between the entities to obtain entity relationship expression;
the concept and concept hierarchy representation acquisition unit is used for carrying out concept and concept hierarchy representation learning according to the entity representation and modeling the relationship between the entity and the concept and the relationship between the concept and the concept to obtain concept and concept hierarchy representation;
and the entity alignment result acquisition unit is used for constraining the entity representation in the entity alignment process based on the vector distance through entity relationship representation and concept hierarchy representation to obtain the result of the alignment of the two knowledge graph entities.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of knowledge-graph entity alignment of any one of claims 1 to 7 are implemented when the program is executed by the processor.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the method for knowledge-graph entity alignment of any of claims 1 to 7.
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