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Vessel data relationship extraction method based on PGM and PSO clustering

A data relationship and ship technology, applied in the field of data processing, can solve the problems of low efficiency and low accuracy of ship data extraction, and achieve the effect of high accuracy and efficiency

Active Publication Date: 2020-09-04
HARBIN ENG UNIV
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Problems solved by technology

[0003] The purpose of the present invention is to propose a ship data relation extraction based on PGM and PSO clustering in view of the problems of low accuracy and low efficiency of ship data extraction in the process of constructing a knowledge map in the prior art. method

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  • Vessel data relationship extraction method based on PGM and PSO clustering
  • Vessel data relationship extraction method based on PGM and PSO clustering
  • Vessel data relationship extraction method based on PGM and PSO clustering

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specific Embodiment approach 1

[0050] Specific implementation mode one: refer to figure 1 Specifically illustrate this embodiment, a kind of relation extraction method based on PGM and PSO clustering described in this embodiment, comprises the following steps: A kind of ship data relation extraction method based on PGM and PSO clustering, it is characterized in that comprising the following step:

[0051] Step 1: Obtain the ship data source, and perform entity extraction on the ship data in the ship data source, identify the named entity existing in the sentence, and mark it. The described entity, for example, is a noun, Can represent a thing, such as the Titanic ship is an entity.

[0052] Step 2: Use the probability graph model to select all possible entity pairs from the marked ship data, and then select the highest probability candidate entity by Perform disambiguation operations on all possible entity pairs;

[0053] Step 3: Set k cluster centers, then calculate the distance between the entity pair...

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Abstract

The invention discloses a vessel data relationship extraction method based on PGM and PSO clustering, and relates to the technical field of data processing, the method aims at solving the problems that in the prior art, in the knowledge graph construction process, relation extraction is low in accuracy and efficiency of vessel data extraction. According to the method, the similarity score is calculated by using one probability graph model, and the similarity degree between different candidate objects is divided according to the score, so that the entity pair is better matched, the ambiguity ofentity matching is eliminated by using a flexible similarity criterion, and more relationships can be extracted. A fitness function in an existing clustering algorithm is optimized, two criteria areadded, and convergence can be accelerated under the condition that a local optimal solution is not easy to achieve, so that the optimal solution is obtained, and the accuracy and efficiency of relation extraction in the knowledge-oriented graph construction process are high.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a ship data relationship extraction method based on PGM and PSO clustering. Background technique [0002] Ship data relationship extraction is mainly to extract the implicit relationship between entities from ship data. At present, there are already many methods for ship data relationship extraction. Since the ship data relationship extraction method needs to be marked in advance, it is based on rules, and many rules need to be defined and then trained to extract relationships. The extraction of relations is very limited. A lot of the current network information is in the nature of articles, and there is no systematic and regular language to describe it. In this case, it is necessary to add new rules, which makes the applicability not high. . For the unsupervised relationship extraction method, this situation can be effectively avoided. Unsupervised machine learning d...

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Application Information

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IPC IPC(8): G06K9/62G06F40/295
CPCG06F40/295G06F18/22G06F18/23213
Inventor 周连科邵璐何鸣王红滨王念滨薛冬梅崔琎
Owner HARBIN ENG UNIV
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