WO2022108206A1

WO2022108206A1 - Method and apparatus for completing describable knowledge graph

Info

Publication number: WO2022108206A1
Application number: PCT/KR2021/015999
Authority: WO
Inventors: 박영택; 이민호; 이완곤; 바트셀렘작바랄
Original assignee: 숭실대학교산학협력단
Priority date: 2020-11-19
Filing date: 2021-11-05
Publication date: 2022-05-27

Abstract

Disclosed are a method and apparatus for completing a describable knowledge graph. According to the present invention, provided is the apparatus for completing a describable knowledge graph, comprising: a processor; and a memory connected to the processor, wherein the memory stores program instructions executable by the processor to: extract a plurality of relation paths for connecting a subject and an object in a query triple including the subject, a predicate, and the object; generate a plurality of describable segments by using the extracted plurality of relation paths; extract an embedding vector for each of the generated plurality of describable segments by using a neural network model in which a CNN and an LSTM are combined; compare the semantic similarity between the plurality of describable segments represented by the embedding vector and the query predicate included in the query triple, by using an attention mechanism; and determine a segment having high importance for link prediction regarding the query triple from among the plurality of describable segments via the semantic similarity comparison.

Description

Method and device for completing an explanatory knowledge graph

The present invention relates to a method and apparatus for completing an explanatory knowledge graph.

The knowledge graph refers to information that expresses the relationship between resources and resources accumulated from various sources, such as the web, and graphically expresses the meaning between these concepts. However, the knowledge graph has a problem in that triples are missing or some data connection is insufficient.

In order to supplement the incomplete knowledge graph, many studies on link prediction have been conducted before. Recently, deep learning-based link prediction research has been actively conducted, but only the link prediction results are presented, but the basis for the derived results and the validity of the derivation process are not provided.

In addition, when the results of link prediction are not good, the cause of the bad is not immediately known, so how this decision was made, it is not possible to rely heavily on deep learning-based link prediction. Therefore, it is important to predict an explanatory link that can be explained and presented in a form that can be understood by humans. When you do, you can see an important direction.

In order to solve the problems of the prior art, the present invention intends to propose a method and apparatus for completing a knowledge graph that can provide validity of a derivation process as a basis for link prediction.

In order to achieve the above object, according to an embodiment of the present invention, an explanatory knowledge graph completion apparatus, comprising: a processor; and a memory connected to the processor, wherein the memory extracts a plurality of relational paths capable of connecting the subject and the object from a query triple including a subject, a predicate, and an object, and the extracted plurality of relational paths Generates a plurality of explainable segments using Compare semantic similarity between a plurality of explainable segments and a query predicate included in the query triple, and select a segment with high importance in link prediction for the query triple among the plurality of explainable segments through the semantic similarity comparison An explanatory knowledge graph completion apparatus is provided for storing program instructions executable by the processor to determine.

The plurality of relationship paths may be defined as paths connected only to the one or more relationships excluding the one or more entities among one or more entities and one or more relationships that may be connected from the subject to the object.

The program instructions may extract the plurality of relationship paths by searching the one or more entities and the one or more relationships between the subject and the object through a random walk using a path ranking algorithm (PRA).

The program instructions express the subject and object of all triples connected by the query predicate in pairs, and remove some of the plurality of relationship paths by using the pair's random walk probability for each of the plurality of relationship paths. can

The program instructions may remove some of the plurality of relational paths by using a ratio of pairs having the random walk probability greater than 0, an average value of the random walk probability, and a length of each of the plurality of relational paths.

Each of the plurality of explainable segments is preprocessed with the same length n, and each entity and relationship is expressed as a d-dimensional vector, and the CNN receives data converted into a matrix of n×d form for each of the plurality of explainable segments as input. A feature map of each of the plurality of explainable segments is output, and the LSTM includes a forward LSTM layer and a backward LSTM layer, and an embedding vector of each of the plurality of explainable segments can be generated by receiving the feature map as an input.

The program instructions may calculate an attention score for each of the plurality of explainable segments by comparing the semantic similarity, and determine a segment having high importance in a link prediction result for the query triple based on the attention score. .

According to another aspect of the present invention, there is provided a method for completing a knowledge graph that can be described in a device including a processor and a memory connected to the processor, wherein in a query triple including a subject, a predicate, and an object, the subject and the object can be connected extracting a plurality of relationship paths; generating a plurality of explainable segments using the extracted plurality of relationship paths; extracting an embedding vector for each of the generated plurality of explainable segments using a neural network model combining CNN and LSTM; comparing semantic similarity between a plurality of descriptive segments represented by the embedding vector and a query predicate included in the query triple using an attention mechanism; and determining a segment having high importance for link prediction with respect to the query triple from among the plurality of explainable segments through the semantic similarity comparison.

According to another aspect of the present invention, there is provided a computer readable program for performing the above method.

According to the present invention, it is possible to not only predict the missing link of the incomplete knowledge graph, but also provide a description of the result of link prediction, thereby providing the basis for the result and the validity of the derivation process.

1 is a diagram illustrating the configuration of an explanatory knowledge graph completion apparatus according to an exemplary embodiment of the present invention.

2 is a view for explaining a process of completing an explanatory knowledge graph according to the present embodiment.

3 is a diagram illustrating an explainable segment embedding process according to an embodiment of the present invention.

4 is a diagram illustrating the structure of an attention mechanism for link prediction according to an embodiment of the present invention.

Since the present invention can have various changes and can have various embodiments, specific embodiments are illustrated in the drawings and described in detail.

However, this is not intended to limit the present invention to specific embodiments, and it should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the present invention. In describing each figure, like reference numerals have been used for like elements.

Knowledge graph completion (link prediction) is the task of supplementing the incomplete knowledge graph by predicting missing links. It predicts the object corresponding to ? when the query triple <subject, predicate, ?> is given.

Here, the subject and the object are defined as an entity (entity), and the predicate is defined as a relation.

The present invention relates to a method capable of presenting a description of a result of link prediction, and when a query triple is input, not only predicting a link to an object corresponding to a correct answer among a plurality of candidate objects connected to a subject, but also predicting a link We present an inference path to provide an explanation supporting the predicted link outcome.

Here, the inference path is defined as a set of entities and relationships that can reach the object starting with the subject, and the explanatory inference path is defined as an explanation segment.

As shown in FIG. 1 , the knowledge graph completion apparatus according to the present embodiment may include a processor 100 and a memory 102 .

The processor 100 may include a central processing unit (CPU) capable of executing a computer program or other virtual machines.

Memory 102 may include a non-volatile storage device such as a fixed hard drive or a removable storage device. The removable storage device may include a compact flash unit, a USB memory stick, and the like. Memory 102 may also include volatile memory, such as various random access memories.

Such memory 102 stores program instructions executable by the processor 100 .

The program instructions according to the present embodiment extract a plurality of relational paths that can connect the subject and the object from a query triple including a subject, a predicate, and an object, and use the extracted plurality of relational paths to provide a plurality of explanations. Generates a possible segment, extracts an embedding vector for each of the generated plurality of explainable segments using a neural network model that combines CNN and LSTM, and uses an attention mechanism to generate a plurality of explainable segments expressed by the embedding vector and The semantic similarity with the predicate included in the query triple is compared, and a segment having a high importance in link prediction for the query triple is determined from among the plurality of explainable segments through the semantic similarity comparison. Hereinafter, a process of determining an explanatory segment with high importance for link prediction for completing the knowledge graph will be described in detail.

Here, the object of the query triple may be an object corresponding to the correct answer among objects that can be connected to the subject.

FIG. 2 is a diagram exemplarily illustrating a case in which the United States is the correct object as the object in the query triple <Tom Cruise, nationality, ?>.

A segment that can be explained in FIG. 2 means three inference paths existing between Tom Cruise and the United States as follows.

Explanation Segments1:

Explanation Segments2:

Explanation Segments3:

In this specification, explanation means an explanation supporting the result of link prediction, and the present invention classifies meaningful (high importance in link prediction) segments and meaningless segments among various explanatory segments.

A segment having a high importance in the link prediction result of the query triple may be determined as a segment having an attention score described below or higher than a preset value or a segment having a preset rank or higher among a plurality of segments.

In FIG. 2 , explanation segment3 that cannot be presented as a basis for the inference result is classified as a meaningless explanation segment, and explanation segment1,2 that cannot be presented as a basis for link prediction is classified as a meaningful explanation segment.

As described above, the explainable segment means various paths that can connect the subject (s) and the object (o) of the triple <s, r, o>.

In order to create a descriptive segment, various relational paths for the query predicate (r) included in the query triple are extracted.

Here, the relational path is a path that can be connected from the subject to the object.

When is present, a path connected only by a relationship, not an object, in that path.

means

Here, e denotes an entity and r denotes a relationship.

According to a preferred embodiment of the present invention, a number of entities and relationships between a subject and an object are searched through a random walk using a path ranking algorithm (PRA), and various relationship paths are extracted through this.

However, in the case of a large-capacity knowledge graph, there are problems in that numerous relational paths are extracted and the data increases exponentially, and there are many relational paths that are not conducive to learning the link prediction model, so a process of filtering the relational paths is required.

To this end, in the present embodiment, the subject and object of all triples connected by the query predicate are expressed as a pair (s,o), and a random walk probability value of each pair for all relationship paths is calculated.

Here, the random walk probability is a mathematical expression of moving randomly, that is, probabilistically, at every moment in a given space.

Thereafter, a relationship path in which the ratio of pairs having a random walk probability greater than 0 is greater than or equal to a preset numerical value (eg, 0.7) is extracted, and the average value of the random word probability for each relationship path is a preset numerical value (eg, 0.05) ) and the following relationship paths are removed.

In addition, a relational path whose length is less than or equal to a predetermined value (for example, 3) is extracted.

It maps entities to relational paths created through PRA to finally create descriptive segments.

For example, if the relationship path for the query predicate nationality above is

, an explanatory segment for the query triple <Tom Cruise, nationality, USA>, <Tom Cruise, bornIn, Syracuse, cityOf, New York, locatedIn, USA> is created. Various relationship paths may exist in one query triple, and multiple explainable segments may be created in the same relationship path.

In order to train a link prediction model using explainable segments, an embedding operation that effectively expresses each explainable segment through a unique vector is required.

Referring to FIG. 3 , an embedding vector for each of the generated plurality of explainable segments is extracted using a neural network model combining CNN and LSTM.

In order to use the explainable segment as an input of a convolutional neural network (CNN), zero padding of an explainable segment of a predetermined length or less (for example, 7) makes the length of all explainable segments the same length (n) preprocessed with

Then, each entity and relationship are expressed as a d-dimensional vector, transformed into an n×d matrix, and input to CNN.

CNNs are mainly used to extract and enhance features of text data as well as images, and show relatively high performance in extracting semantic and grammatical relationships between several words.

Therefore, CNN is used to express the characteristics of each entity and relationship in the explainable segment as a vector implied. CNN uses k filters with a window size of 2 to move one space in the order of entities and relationships in the explainable segment. and output the feature map.

After that, a pooling operation is performed to reduce the dimension while preserving all the key information, and finally, a vector that preserves local information is generated.

Long Short-Term Memory (LSTM) is provided to extract sequential features of descriptive segments.

According to this embodiment, bidirectional LSTM is applied.

A segment that can be explained is composed of a form that starts with a subject and arrives at an object by successively connecting entities and relationships.

In addition, a reverse LSTM layer is added to learn including inverse relationships connected in the reverse direction. Finally, by combining the vector output through the forward LSTM and the vector output through the backward LSTM, the embedding vector of one descriptive segment

create

Not all explainable segments between the subject s and the object o of the query triple <s, r, o> are helpful for link prediction.

There are meaningful explainable segments that are of great help in link prediction, while there are nonsensical explainable segments.

According to this embodiment, an attention mechanism is applied to evaluate the importance of each explainable segment.

Referring to FIG. 4 , the importance of link prediction results is identified by calculating the semantic similarity between each explanatory segment expressed as an embedding vector and a query predicate through CNN and LSTM.

First put the query predicate r into an embedded descriptive segment

Embed with the same size as the embedding query predicate

, and their semantic similarity is calculated using the following equation.

Wow

is the weight variable to be learned, and q is the weighted sum of each explainable segment with an attention score and expressed as a vector.

generated by the above calculation.

stands for the attention score, and it is an important indicator to understand how much each explainable segment affects the result of link prediction.

Therefore, by utilizing the attention score, it is possible to distinguish between meaningful and explainable segments that have a large influence on the link prediction result from those that have less influence on the link prediction results.

For example, when four explanatory segments for the query triple <Tom Cruise, nationality, USA> are generated as follows, the similarity between explanation segments 1 and 2 and the query predicate nationality, that is, the attention score is calculated high, It can be said to be an important explanation segment.

Conversely, explanation segments 3 and 4 can be classified as explanation segments that are not helpful to link prediction results because the attention score is low.

Explanation Segments1:

Attention Score: 0.55

Explanation Segments2:

Attention Score: 0.32

Explanation Segments3:

Attention Score: 0.12

Explanation Segments4:

Attention Score: 0.01

The above-described embodiments of the present invention have been disclosed for purposes of illustration, and various modifications, changes, and additions will be possible within the spirit and scope of the present invention by those skilled in the art having ordinary knowledge of the present invention, and such modifications, changes and additions should be regarded as belonging to the following claims.

Claims

As a knowledge graph completion device that can be explained,

processor; and

a memory coupled to the processor;

The memory is

Extracting a plurality of relational paths that can connect the subject and the object from a query triple including a subject, a predicate, and an object,

generating a plurality of explainable segments using the extracted plurality of relational paths;

Extracting an embedding vector for each of the generated plurality of explainable segments using a neural network model that combines CNN and LSTM,

comparing the semantic similarity between a plurality of descriptive segments represented by the embedding vector and a query predicate included in the query triple by using an attention mechanism;

To determine a segment with high importance in link prediction for the query triple from among the plurality of explainable segments through the semantic similarity comparison,

An explanatory knowledge graph completion device for storing program instructions executable by the processor.
According to claim 1,

The plurality of relational paths is an explanatory knowledge graph completion device defined as a path connected only to the one or more relationships excluding the one or more entities among one or more entities and one or more relationships that can be connected from the subject to the object.
3. The method of claim 2,

The program instructions are

An explanatory knowledge graph completion apparatus for extracting the plurality of relationship paths by using a path ranking algorithm (PRA) to search the one or more entities and the one or more relationships between the subject and the object through a random walk.
4. The method of claim 3,

The program instructions are

Expressing the subject and object of all triples connected by the query predicate as a pair,

An explanatory knowledge graph completion apparatus for removing some of the plurality of relationship paths by using the pair's random walk probability for each of the plurality of relationship paths.
5. The method of claim 4,

The program instructions are

An explanatory knowledge graph completion apparatus for removing some of the plurality of relationship paths by using the ratio of pairs having the random walk probability greater than 0, the average value of the random walk probability, and the length of each of the plurality of relationship paths.
According to claim 1,

each of the plurality of descriptive segments is preprocessed to the same length n and each entity and relationship is expressed as a d-dimensional vector,

The CNN outputs a feature map of each of the plurality of explainable segments by inputting data transformed into an n×d matrix in each of the plurality of explainable segments,

The LSTM includes a forward LSTM layer and a backward LSTM layer, and an apparatus for completing an explanatory knowledge graph to generate an embedding vector of each of the plurality of explainable segments by inputting the feature map as an input.
The method of claim 1,

The program instructions are

calculating an attention score for each of the plurality of explainable segments by comparing the semantic similarity;

An explanatory knowledge graph completion device for determining a segment with high importance in a link prediction result for the query triple through the attention score.
A method of completing a knowledge graph that can be described in a device comprising a processor and a memory connected to the processor,

extracting, from a query triple including a subject, a predicate, and an object, a plurality of relational paths capable of connecting the subject and the object;

generating a plurality of explainable segments using the extracted plurality of relationship paths;

extracting an embedding vector for each of the generated plurality of explainable segments using a neural network model combining CNN and LSTM;

comparing semantic similarity between a plurality of descriptive segments represented by the embedding vector and a query predicate included in the query triple using an attention mechanism; and

and determining a segment having high importance for link prediction with respect to the query triple from among the plurality of explainable segments through the semantic similarity comparison.
9. The method of claim 8,

The plurality of relational paths are defined as paths connected only to the one or more relationships excluding the one or more entities among one or more entities and one or more relationships that can be connected from the subject to the object.
A computer readable program for performing the method according to claim 8 .