In this post i’ll give you an overview of some similarity metrics I’ve discovered when working with WordNet. Even though they were originally proposed as linguistic similarity metrics, I thought it would make sense to explore their behaviour if we generalise their use to a taxonomy-annotated dataset.
I will use public data from Wikipedia and what topic to choose on the week that Percy landed in Mars? No other than the rich domain of uncrewed spacecraft. Follow me!
Continue reading “QuickGraph#18 Semantic similarity metrics in taxonomies: A wikipedia example on uncrewed spacecraft”
Roughly a year and a half ago I posted QuickGraph#8 on how to copy all or part of your graph between neo4j DBs by serialising it as RDF with Neosemantics. It concluded on a sad note though, something along these lines: “Relationship properties will be lost in this process because RDF does not allow the representation of properties in edges”. Well, now we have RDF-star and the problem is solved. This is a brief update to that post where I explain how to overcome that hurdle.
Continue reading “QuickGraph#8 revisited: LossLess graph copy between Neo4j DBs with RDF-star”
In this second post on WordNet on Neo4j I will be focusing on querying and analysing the graph that we created in the previous post. I’ll leave for a third instalment some more advanced analysis and maybe integrations with NLTK or RDF.
Remember that you can test all the examples in this post directly on the demo server. The access credentials are wordnet/wordnet (also you’ll need to select the database of the same name). I’ve also put the queries in a Colab python notebook if you prefer to run them from there.
Let’s crack on.
Continue reading “QuickGraph#17 The English WordNet in Neo4j (part 2)”