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Use something like Word2Vec. If a particular node has two edges that are very far from each other, besides the node in question, split the node into word(1) and word(2) nodes.


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In general, how algorithm should distinguish the word meaning and recognise the word within the context? I don't think anybody knows how to answer this for the general case. If they did, they'd have basically solved AGI. But we can certainly talk about techniques that get part-of-the-way there, and approaches that could work. One thing I would consider ...


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If I understand you correctly, you should check out Word2Vec. From Wikipedia: Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a high-...


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I would think you could use a graph database, perhaps Neo4J or Titan or something of that nature. Or, if you want a simple file format, you could use one of the many formats that exist for representing graphs. You can find a list and overview of some of them here. Another option would be to store them in RDF using a triplestore like Jena.


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