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.

Wikipedia, Semantic Web

After I make the web-deliverable interface that allows users to expand these semantic frames into all possible related entities, I refactor some SRL parsers to turn natural language into this semantic data and I point the system at Wikipedia... weeks later, what does this enormous OWL file (possibly stored numerically) have to do with the Semantic Web?

A quote from Cycorp:

The success of the Semantic Web hinges on solving two key problems: (1) enabling novice users to create semantic markup easily, and (2) developing tools that can harvest the semantically rich but ontologically inconsistent web that will result. To solve the first problem, it is important that any novice be able to author a web page effortlessly, with full semantic markup, using any ontology he understands. The Semantic Web must allow novices to construct their own individual or specialized-local ontologies, without imposing the need for them to learn about or integrate with an overarching, globally consistent, master ontology.

Allowing users to type in natural language is the easiest way to generate semantic markup. Because users prefer to use natural language, any ontology that software can roundtrip natural language with will likely be an overarching (prevalent) one. A possible problem with the approach I'm using is that the ontology of the dataset used to train SRL-based parsers would, instead of being handcrafted by an expert, be a collaborative effort of people, hopefully experts, visiting a site — wikiontology is a relatively new idea.

Only after I have the dataset will I be able to say if the ontology from the planned website is advantageous to machine reasoning tasks. It shouldn't be terribly difficult to make a benchmark for the consistency of the wiki-generated ontology — possibly using natural language (after the parser is completed). For example, paraphrase corpora and other instruments could be of use in both generating and refining.

Wikipedia is a proof of concept that people can come together to generate collective knowledge resources, so — if we get the post-NLP/pre-NLG ontology right (prevalent as argued above) — the Semantic Web may resemble a distributed wiki-knowledgebase. The gigabytes of Wikipedia data would be a launching point.

Ontology and File Compression

Thinking on this post-SRL/pre-NLG ontology made me realize there was no easy way to compare different models.

If we look at rule systems, ontology and taxonomy as interoperating towards efficiently storing knowledge, then there might be a metric. That is, if system A compresses the same knowledge set better than system B and is more computationally efficient (in decompression/utilization), then we can say that system A is superior to system B (on that set) without resorting to aesthetics or philosophy. We have SUMO, the CYC upper ontology, ISO 15926, and others designed around real-world data and it's difficult to rank them.

The metaphor of file compression to knowledgebases might allow competition between differing methods. As systems are envisioned that mechanically generate rules, ontological structure or taxonomy (optimizing generators that create a system for a given knowledgebase), these metrics may be of use in comparing the resulting generated systems. Personally, I think it would be interesting to have algorithms that compress knowledgebases like tar, zip and 7zip do to files. Unfortunately, this approach is storage and speed-based and doesn't consider interface considerations-- for example, sets of things that are categorized for navigation.

Here's a link to a paper describing a relationship between AI (my field of research) and file compression. Apparently, there's a prize for compressing Wikipedia.

Semantic Role Labeling

Semantic Role Labeling (SRL) appears to be the algorithmically independent term for parsing sentences into structures like PropBank or FrameNet.

I'm investigating ontology to store the SRL'd structure that can additionally be used for NLG. I'm looking at loom, kpml, cypher and others to see if there's any overlap. It'd be “easier” to code if there's one format to and from natural language. Not sure if this SRL'd/pre-NLG would work well for machine reasoning (which is ideally what'd be stored in a db).

[1] CoNLL-2005 Shared Task: Semantic Role Labeling

[2] CCG: Semantic Role Labeling Demo