RE: [ACTION-94]: go and find examples of concept ontology (semantic features of terms as opposed to domain type ontologies)

Dear Felix,


Thank you very much. Probably Tadej can prepare the use cases you mention,
with the consolidated data category. About the question 3 and 4, I can tell
you the following:


3) Would it be produced also by an automatic text annotation tool?


For the pointers to the three information referred (concepts in Ontology,
meanings in Lexical DB, and terms in Terminological resources) I think it
would be possible semiautomatic annotation tools, that is, proposed by the
tool and confirmed by user.


The fully automatic text annotation  would need more sophisticate “semantic
calculus”, and most of these are under research, as far as I know. Maybe, in
this cases, it should be combined with textAnalysisAnnotation, specifying in
Annotation agent – and Confidence score – which system and with which
reliability has been produced.


4) Would 1-2 be consumed by an MT tool, or by other tools?


These can be basically consumed by language processing tools, like MT, and
other Linguistic Technology that needs content or semantic info. For
instance Text Analytics, Semantic search, etc.. In the localization chains,
these information can be also used by automatic or semiautomatic processes
(like selection of dictionaries for translations, or selection of
translators/revisers by subject area) 


It could be also used by humans for translation or post-edition in case of
ambiguity or lake of context in the content, but mostly by automatic


I hope this helps.




De: Felix Sasaki [] 
Enviado el: sábado, 02 de junio de 2012 14:13
Para: Tadej Stajner; pedro.diez
Asunto: Re: [ACTION-94]: go and find examples of concept ontology (semantic
features of terms as opposed to domain type ontologies)


Hi Tadej, Pedro, all,


this looks like a great chain of producing and consuming metadata.


Apologies if this was explained during last weeks call or before, but can
you clarify a bit the following:


1) How would the actual HTML markup produced in the original text annotation
use case look like?

2) How would the markup in this use case look like?

3) Would it be produced also by an automatic text annotation tool?

4) Would 1-2 be consumed by an MT tool, or by other tools?


Thanks again,



2012/5/31 Tadej Stajner <>

Hi Pedro, 
thanks for the excellent explanation. If I understand you correctly, a
sufficient example for this use case would be annotation of individual words
with synset URI of the appropriate wordnet? If so, then I believe this route
can be practical - I think linking to the synset is a more practical idea
than expressing semantic features of the word given the available tools. 

Enrycher can do automatic all-word disambiguation into the english wordnet,
whereas  we don't have anything specific in place for semantic features
(which I suspect also holds for other text analytics providers).

I'm also in favor of prescribing wordnets for individual languages as valid
selector domains as you suggest in option 1). That would make validation
easier since we have a known domain. 

@All: Can we come up with a second implementation for this use case,
preferrably a consumer? 

-- Tadej

On 5/29/2012 2:00 PM, Pedro L. Díez Orzas wrote: 

Dear all,


Sorry for the delay. I tried to contact some people I think can contribute
to this, but they are not available these weeks. 


Before providing an example to consider all if it is worthwhile to maintain
“semantic selector” attribute in the consolidation of “Disambiguation” I
would like to do a couple considerations:


1.	Probably we will not have short term any implementation, but there
are for example few semantic networks available in web (see that could be mapped
using semantic selectors. See on line for example, the famous <>
2.	The W3C working group SKOS (Simple Knowledge Organization System
Reference) are maybe dealing with similar things.


The “semántica selector” allows further lexical (simple words or multi
words) distinctions than a “domain” or an ontology like NERD. Also, the
denotation is different from the “concept reference”, most of all in part of
speech like verbs.  


Within the same domain, referring to very similar concepts, languages have
semantic differences. Depending on the semantic theory used, each tries to
captivate these differences by means of different systems (semantic
features, semantic primitives, semantic nodes (in semantic networks), other
semantic representations). An example could be the German verb “löschen”,
which in different contexts can take different meanings that can be try to
capture using different selectors, with the different systems.


–         löschen                        -> clear             (some bits)

                                   -> delete           (files)
                                   -> cancel          (programs)
                                   -> erase            (a scratchpad)
                                   -> extinguish     (a fire)


Other possible translations of the verb “löschen” are:


löschen, streichen, tilgen, ausstreichen, herausstreichen


löschen, klären, klarmachen, leeren, räumen, säubern


löschen, auslöschen, tilgen, ausradieren, radieren, abwischen


löschen, auslöschen, zerstören


löschen, stillen, abschrecken, dämpfen

put out

löschen, bringen, ausmachen, ausschalten, treiben, verstimmen


entladen, abladen, ausladen, löschen, abstoßen, abwälzen


entladen, erfüllen, entlassen, entlasten, löschen, ausstoßen

wipe out

auslöschen, löschen, ausrotten, tilgen, zunichte machen, auswischen


stillen, löschen


schließen, verschließen, abschließen, sperren, zumachen, löschen


löschen, abtupfen, klecksen, beklecksen, sich unmöglich machen, sich

turn off

ausschalten, abbiegen, abstellen, abdrehen, einbiegen, löschen

blow out

auspusten, löschen, aufblasen, aufblähen, aufbauschen, platzen


abknallen, düsen, umschalten, löschen, töten, kaputtmachen


einlösen, erlösen, zurückkaufen, tilgen, retten, löschen

pay off

auszahlen, bezahlen, tilgen, abzahlen, abbezahlen, löschen

switch out



ausladen, entladen, abnehmen, löschen


eintauchen, durchtränken, löschen, nass machen

rub off

abreiben, abgehen, abwetzen, ausradieren, abscheuern, löschen

strike off



landen, an Land gehen, kriegen, an Land ziehen, aufsetzen, löschen




According to this, the consolidation of disambiguation/namedEntity/  data
categories under “Terminology”
guation could be the following. It is thought to cover operational URI or
XPath pointers to the current three most important semantic resources:
conceptual (ontology), semantic (semantic networks or lexical databases) and
terminological (glossaries and terminological resources), where ontologies
are used for both general lexicon and terminology, semantic networks to
represent general vocabulary (lexicon), and terminological resources
specialized vocabulary.



Includes data to be used by MT systems in disambiguating difficult content


Data model

*	concept reference: points to a concept in an ontology that this
fragment of text represents. May be an URI or an XPath pointer.
*	semantic selector: points to a meaning in an semantic network that
this fragment of text represents. May be an URI or an XPath pointer.
*	terminology reference: points to a term in a terminological resource
that this fragment of text represents. May be an URI or an XPath pointer.
*	equivalent translation: expressions of that concept in other
languages, for example for training MT systems



Also, I would keep textAnalysisAnnotation, since the purpose is quite


Anyway, if we consider not to include “semantic selector” now, maybe it can
be for future versions or to be treated in liaison with other groups.


I hope it helps,





Pedro L. Díez Orzas

Presidente Ejecutivo/CEO

Linguaserve Internacionalización de Servicios, S.A.

Tel.: +34 <tel:%2B34%2091%20761%2064%2060>  91 761 64 60
Fax: +34 91 542 89 28 <tel:%2B34%2091%20542%2089%2028>  

E-mail:  <> <> 


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Received on Monday, 4 June 2012 11:54:32 UTC