Reification, Ontological Consensus and Ergonomics

Ontological consensus is the goal of the WikiCorpus project and is, in theory, possible via the on screen predictive list of predicates. Users would rather select predicates (that work for the semantics of the sentence) from the list than create new ones, just as users prefer to find the results of a search on the first page of a search engine's results. This list, hopefully produced by an accurate predictive algorithm, will facilitate consistency in the dataset.

In the user interface, the ergonomics of nested predicates would be that a completed predicate can be dragged (moved or copied) into the argument slot of a predicate being formed. The problem is, firstly, that moving and copying the predicate are both done with the drag and drop motion. Secondly, the nesting of predicates might deter from semantic and ontological consensus as complicated constructions are possible from the elements in the predictive list.

Take for example:
1) The book was put on the shelf.
2) Tommy put the book on the shelf.
3) Tommy put the book on the shelf to help the library.

Each is a semantic superset of its previous sentence duplicating and nesting the previous predicate.

1) PutOn(book,shelf)
2) Did(tommy,PutOn(book,shelf))
3) InOrderTo(Did(tommy,PutOn(book,shelf)),Help(tommy,library))

That is, the first sentence is the first predicate, the second is both the first and second, and the third is all three. Two terms related to this method of representing sentences are that sentences have a semantic core and a semantic root (I'll try to find out if other terminology already exists). The core is the predicate constructed from, often the most nested predicate, and the root is the least and the root of the corresponding tree structure.

So, while recursive binary predicates appear to be able to capture natural language, the interface considerations and ergonomics are more complicated. Also, finding the semantic core of sentences appears to relate to placing the sentence into its semantic frame via PropBank or FrameNet corpora; the algorithm would then be to construct the entirety. These nested predicates can be represented in the matrix format with some notational conventions.

Interestingly, similar to the reording of nouns in the matrix format, the reordering of the predicate arguments or nouns can be done, resembling:

InOrderTo(DoneBy(PutOn(book,shelf),tommy),Help(tommy,library))
The book was put on the shelf by Tommy to help the library.

InOrderToInverse(Help(tommy,library),DoneBy(PutOn(book,shelf),tommy))
In order to help the library, the book was put on the shelf by Tommy.
In order for Tommy to help the library, the book was put on the shelf by him.

InOrderToInverse(Help(tommy,library),Did(tommy,PutOn(book,shelf))
In order for Tommy to help the library, he put the book on the shelf.
To help the library, Tommy put the book on the shelf.

So, each binary predicate may be related to another predicate with its arguments in the opposite order. Some predicates may not, limiting the possible noun orderings for paraphrases. This is just one approach to capturing semantics using nested predicates; I look forward to learning other approaches and designing a web interface for a collaborative corpus.