Hello. I am trying to do some ontology matching and am wondering if there are any other ways I can go about matching other than through string similarity matching (I’ve already done that). I would be interested to match terms or URIs or by semantic similarity. I have tried using association rules but am not able to make it work with string data and I’ve also tried the subset matcher, but the rule engine dictionary node is taking so long to process the data that I don’t see this as a viable method. Does anyone have any experience in this area and feel able to offer me some suggestion or guidance? I am interested in any methods, machine learning, rule based, or other.
With Machine learning Glove or Word2Vec come into mind. With text also recommender systems based on cosine similarity could be of interest to you. Hope that gives you some ideas to explore
br
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Thank you so much for your responses. I will definitely look into those. This is so new to me, but I am going to give it a try. If you know of any workflows that might give me an idea of how to go about this. I would be very grateful. Thank you again.
Hello @jayshan,
and welcome to KNIME Community!
Maybe KNIME Indexing and Searching extension can help you out. Here is link to couple of examples:
Also you can search KNIME Hub for other workflow examples that might help you out.
Br,
Ivan
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Thank you so much. I will look into these.
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