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Due to some work with @cassiatrojahn and @sven-h I am suggesting to add the following categories of match types related to neural networks in the wider sense.
| IRI | skos:prefLabel | skos:definition | dc:source | skos:example | rdfs:comment | altLabel | Parent |
|---|---|---|---|---|---|---|---|
| semapv:TransformerBasedMatching | transformer-based matching process | A matching process that utilizes transformer models, which are a type of deep learning model architecture designed to handle sequential data, particularly for natural language processing tasks. | Matches between entities are established based on the contextual relationships learned by the transformer from large datasets. | semapv:Matching | |||
| semapv:LLMBasedMatching | LLM-based matching process | A matching process that employs large language models (LLMs) which are pre-trained on vast amounts of text data and can understand and generate human-like text, making them suitable for tasks requiring a deep understanding of language. | Matches between entities are determined through the language understanding capabilities of LLMs, such as semantic context and language inference. | semapv:Matching | |||
| semapv:MachineLearningBasedMatching | machine learning-based matching process | A matching process that involves machine learning algorithms which learn from data to find patterns or make decisions with minimal human intervention. | Matches between entities are made by applying learned models to data points to predict similarities or relationships. | semapv:Matching | |||
| semapv:GraphRepresentationLearningBasedMatching | graph representation learning-based matching process | A matching process that uses graph representation learning which is a method in machine learning that focuses on learning a compact representation for graphs, capturing their structural information. | Matches between entities are identified by analyzing the learned representations that encode the structural features and relationships within graph data. | semapv:Matching |
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