HyperGraphDB: A Generalized Graph Database by Borislav Iordanov.
Abstract:
We present HyperGraphDB, a novel graph database based on generalized hypergraphs where hyperedges can contain other hyperedges. This generalization automatically reifies every entity expressed in the database thus removing many of the usual difficulties in dealing with higher-order relationships. An open two-layered architecture of the data organization yields a highly customizable system where specific domain representations can be optimized while remaining within a uniform conceptual framework. HyperGraphDB is an embedded, transactional database designed as a universal data model for highly complex, large scale knowledge representation applications such as found in artificial intelligence, bioinformatics and natural language processing.
A formal treatment of HyperGraphDB.
Merits being printed out and given a slow read.
Borisla comments on both RDF and Topic Maps:
…Two other prominent issues are contextuality (scoping) and reification.
…
Those and other considerations from semantic web research disappear or find natural solutions in the model implemented by HyperGraphDB.
But when I search the paper, scoping comes up in an NLP example as:
The tree-like structure of the document is also recorded in HyperGraphDB with scoping parent-child binary links between (a) the document and its paragraphs, (b) a paragraph and its sentences, (c) a sentence and each linguistic relationship inferred from it.
Scoping at least in one sense of the word, but not the in the sense of say a name being “scoped” by the language French.
Reification, other than the discussion of RDF and topic maps, doesn’t appear again in the paper.
As I said, it needs a slow read but if you see something about scoping and/or reification that I have missed, please give a shout!