[open-linguistics] [Corpora-List] WordNet vs Ontology

yversley at gmail.com yversley at gmail.com
Fri Aug 8 07:52:00 UTC 2014

Dear Sebastian,

let me start out by saying that including that I’m not sure if broadening an already diffuse discussion by adding more people to it is helpful in the sense of achieving a better signal-to-noise ratio. Corpora-List is (in)famous for occasionally having discussions between people with very different background assumptions (e.g. Ramesh’s insistence that language is best seen as behaviour vs. the point that language is a tool to get meanings across). This can be both good and bad, and lots of people who are only interested in factual information did or will hit the “Mute thread" button (or moral equivalent) in the process.

Your whole post seems to boil down to a claim that only RDF-encoded data should count as ontology. This seems to be a bit near-sighted to me, as LemonRDF’s encoding of WordNet is just that, an encoding which is very convenient but which adds nothing to the existing semantics.

I completely agree that using a powerful database (be it RDF or SQL or anything else) is better than using the 90s infrastructure that was once designed for Wordnet, and that linking datasets together is much easier with a common format thst reduces the m:n problem to an 1:n problem.

We already established earlier that WordNet is a combination between a dictionary and an ontological component, which is exactly why it’s more useful for NLP than the ontologies that were part of the original conception of the Semantic Web. Fortunately for us though, people woke up to that idea and resources such as DBPedia now also include dictionary entries that mediate between natural-language strings and the concepts of the respective ontology.

Saying that some people think that “the ontology is already in the text" is unnecessarily putting up a strawman. No one claimed this, and you’d do better by understanding the actual arguments put forward - for example, that in the absence of a central authority, as with marriage or taxonomies in Biology, ontologies are conceptualizations that are intersubjective rather than purely objective. E.g. Kafka may be a German writer in one ontology and a Czech writer in another, yet either of these ontologies would be useful and intuitively plausible. (This creates a tension/incompatibility between the perspective that ontologies are logical things and that you should be able to reason with them, and the view that you should be able to freely combine ontologies on related things.)

Your discussion of layers is absolutely orthogonal to that - modeling text, annotations, metadata, and ontology in one database is surely convenient if you can make it work in a sense that's practically relevant but it doesn’t add anything to the discussion we’re having here.

Best wishes,


Von: Sebastian Hellmann
Gesendet: ‎Freitag‎, ‎8‎. ‎August‎ ‎2014 ‎09‎:‎35
An: John F Sowa, corpora, A list for those interested in open data in linguistics., nlp2rdf

Dear all,
(I included some more lists to ping them, discussion started here: http://mailman.uib.no/public/corpora/2014-August/020939.html)

I see that there are many viewpoints on this issue in this thread.
So let me add my personal biased view.

In the broadest sense, we start to create an ontology by stating facts:

married (a, b) . 

Imho we have an ontology, solely for the reason, that we start to relate a to b with "married" . Even if there is not an explicit ontology defining "married", it is still used in an "ontological" way, just not explicit. There are other aspects missing, which have been discussed throughout the literature (i.e. the fact that it must be "shared" by Gruber), but in the broadest sense, it qualifies. 

Regarding language technology and this discussion, I would say that we should be careful not to mix levels. This is done by lexical-semantic resources, i.e. WordNet, but we could separate it again. 

In my view, we have these different layers:

1. the content, i.e. the characters (html, plaintext), e.g in  unicode.
2. the container of the content, i.e. document or tweet
3. annotations on the content
4. metadata on the container, e.g. the tweeter or author for context
5. collection of content (with or without annotations) i.e. the corpora
6. ontologies and data describing language, i.e. lexica, dictionaries, terminologies, etc. such as WordNet
7. factual databases inluding their taxonomies, i.e. the DBpedia knowledge graph http://dbpedia.org

(@John: I hope you are noticing, that I am trying to be keep all of it as underspecified as possible)

Then in addition, there are ontologies on a meta-level that try to capture all seven layers. Some examples (more below): NIF, lemon, ITS, NERD [1]
which we are trying to combine in the http://nlp2rdf.org and http://lider-project.eu 

We can model WordNet using the lemon ontology: http://datahub.io/dataset/lemonwordnet
However for certain purposes, it makes sense to transform WordNet to become a taxonomy as YAGO is doing:

I am not fixed upon any of the definitions I gave above, as I am aware that you can and should! transform one in the other (with some effort, e.g. corpora to dictionary, fact extraction, language generation).

If we are talking about extracting ontologies from text, there might be philosophical people who might want to argue that the ontology is already in the text. Discussion can be endless, if you take the wrong linguistic turn.

If we are focusing on engineering of information machines, then things are much clearer. 

All the best, 

[1] related to the different layers:
1. NIF: http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core#
2. (there is a gap here, Dublin Core or Foaf are not enough imho)
3 a) MARL: http://www.gi2mo.org/marl/0.1/ns.html
   b) ITS: Docu: http://www.w3.org/TR/its20/ , RDF: http://www.w3.org/2005/11/its/rdf#
   c) OLIA: http://purl.org/olia/
4. a) Dublin Core: http://dublincore.org/documents/dcmi-terms/
    b) Prov-O: http://www.w3.org/TR/prov-o/
5. also NIF: http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core#
6. lemon: http://lemon-model.net/
7. a) DCAT and DataId: http://wiki.dbpedia.org/coop/DataIDUnit
   b) NERD: http://nerd.eurecom.fr/ontology

On 08.08.2014 06:11, John F Sowa wrote:

On 8/7/2014 10:57 PM, Ken Litkowski wrote: 

It would seem to me that our goal should be a classification 
of all existing things (not to exclude the narrower types). 

Yes, but note the slides I suggested in my first note: 


Slides 7 to 9:  Cyc project.  30 years of work (since 1984). 
After the first 25 years, 100 million dollars and 1000 person-years 
of work (one person-millennium!), 600,000 concepts, defined by 
5,000,000 axioms, organized in 6,000 microtheories -- and counting. 

Slide 10:  2300 years of universal ontology schemes -- and counting. 

The Brandeis Shallow Ontology attempts to do this, and incidentally 
is being used to characterize arguments of verbs in Patrick Hanks 
corpus pattern analysis, i.e., in the imperfect world of language. 

I strongly believe in shallow, underspecified ontologies -- especially 
when they're supplemented with lots of lexical information about verbs 
and their characteristic patterns. 

But I also believe that the key to having an open-ended variety of 
specialized ontologies is to make the computers do what people do: 
extend their ontologies automatically by reading books. 

Lenat made the mistake of assuming that you need to hand-code 
a huge amount of knowledge before a system can start to read 
by itself.  But that's wrong.  You need to design a system that 
can automatically augment its ontology every step of the way. 


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Sebastian Hellmann
AKSW/NLP2RDF research group
Insitute for Applied Informatics (InfAI) and DBpedia Association
* Sept. 1-5, 2014 Conference Week in Leipzig, including 
** Sept 2nd, MLODE 2014 
** Sept 3rd, 2nd DBpedia Community Meeting
** Sept 4th-5th, SEMANTiCS (formerly i-SEMANTICS) 
Venha para a Alemanha como PhD: http://bis.informatik.uni-leipzig.de/csf
Projects: http://dbpedia.org, http://nlp2rdf.org, http://linguistics.okfn.org, https://www.w3.org/community/ld4lt
Homepage: http://aksw.org/SebastianHellmann
Research Group: http://aksw.org
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