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Micro rule learning model for mining rules with different lengths in knowledge graph
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A technology of knowledge graph and learning model, applied in the field of differentiable rule learning model, which can solve the problems of DRUM model, such as allocation reliability of difficult new relations, wrong rules, difficult learning rules, etc.
Pending Publication Date: 2022-05-27
UNIV OF SCI & TECH OF CHINA
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However, experiments demonstrate that the DRUM model has difficulty assigning appropriate confidences to new relations, making it difficult to learn rules of different lengths
The Neural LP model uses an attention mechanism to learn rules of different lengths, however, it often introduces false rules when mining longer rules
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Embodiment 1
[0075] like figure 1 As shown, this embodiment provides a differentiable rule learning model for mining rules of different lengths in a knowledge graph. The differentiable rule learning model may be called MineRAL, which is suitable for mining rules of different lengths from a knowledge graph, The technical content involved in the rules includes:
[0076] A knowledge graph is a collection of facts, a knowledge graph where, e h ,e t , r represents the head entity, the tail entity and the relationship respectively; ε is a set composed of entities; is a set of relations;
[0077] A rule in the knowledge graph consists of a rule header and a rule body, the rule header is an atomq(x,y), and the rule body is a set of atomr i (·,·); an atom is a fact that can contain variables at head and tail positions. For example, r(e h ,e t ) is an atom, where x and y are variables that can be replaced with entities;
[0078] An example of a rule in a knowledge graph is: mother(C,A)←fa...
Embodiment 2
[0136] This embodiment uses the differentiable rule learning model of the present invention to mine the chain logic rules from the knowledge graph, and describes its application, including:
[0137] (1) Training stage:
[0138] Given a knowledge graph dataset containing several facts, divide it into a training set and a validation set. The gradient descentalgorithm is used to optimize the overall loss function of the differentiable rule learning model of the present invention on the training set, and when its performance on the verification set is stable, the parameters of the model are saved;
[0140] Given a query, input it into the trained differentiable rule learning model, and use the algorithm in (rule restoration) to restore the mined chain logic rules.
[0141] From the above, it can be seen that the differentiable rule learning model of the embodiment of the present invention can accurately and efficiently mine all chain logic rules ...
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Abstract
The invention discloses a microgradable rule learning model for mining rules with different lengths in a knowledge graph, which comprises a player unit comprising a plurality of player modules arranged in parallel, and each player module can find queries of chained logic rules with different lengths from the knowledge graph according to the input queries of the chained logic rules with different lengths. Finding a corresponding chain logic rule from the knowledge graph; the leader unit is in communication connection with the player unit and can select a proper player module from the player unit to process a corresponding query; and the judgment unit is in communication connection with the leader unit and the player unit, can evaluate the chain logic rules found by the player unit to obtain an evaluation feedback result, and is used for the player unit and the leader unit to improve themselves according to the evaluation feedback result. The microgradable rule learning model can accurately and efficiently mine all the chain logic rules from the knowledge graph.
Description
technical field [0001] The invention relates to the field of knowledge graphs, in particular to a differentiable rule learning model for mining rules of different lengths in the knowledge graphs. Background technique [0002] Knowledge graphs consist of a large number of fact triples, which store structured human knowledge. In recent years, knowledge graphs have made great achievements in fields such as natural languageprocessing, intelligent question answering, recommender systems, and computer vision. Commonly used knowledge graphs usually contain billions of triples, but there will still be a large number of triples missing. Due to the extremely large scale of the knowledge graph, it is expensive to manually complete the graph. Therefore, knowledge graph reasoning techniques to automatically predict missing links based on known links in knowledge graphs have attracted much attention in recent years. [0003] Common knowledge reasoning methods include rule-based method...
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