Natural languages, multilinguisme and ontologies
international experts in a specific domain (not in logic) wants to editan ontology
automatic information extraction from texts
by users that don’t want to be bothered with logic
specific requirements in the project OMNIA
automatic image indexation, w.r.t. an ontology (instantiate theA-box) , using companion texts
generate short image descriptions w.r.t. the indexation
the methods have to be robust for ontology T-box modifications
SC and SR are the lexicons for concepts and relations resp. Refx : x −→ Sx is the lexical reference assignment
1Maedche et al. 2003 : “Bootstrapping an ontology-based information
−→ extend the ontology lexicon with synonym sets
the ontology lexicon is very restricted (often one head-word for eachconcept)
there are a lot of ways to say the same thing in NL
utilisation of controlled natural languages (CNLs)
CNL input engines to query/edit ontologies :
2http://attempto.ifi.uzh.ch/site & http://attempto.ifi.uzh.ch/ape3Dolbear et al. 2007 : “The Rabbit Language : description, syntax and conversion to OWL”4Funk et al. 2007 : “ CLOnE : Controlled Language for Ontology Editing”5Cregan et al. 2007 : “ Sydney OWL Syntax : towards a controlled natural language syntax for OWL 1.1”6Bernstein et al. 2005 : “Querying ontologies : a controlled english interface for end users“7Bernstein et al. 2006 : “GINSENG : a guided input natural language search engine for querying ontologies”8Bernstein et al. 2006 : “GINO : a guided input natural language ontology
utilisation of controlled natural languages for queries andedition
reduces ambiguity and complexity of natural languageallows users to insert or ask for information in the ontology without anylogical backgroundthe systems are portables
OK for user interfaces but unusable for information extraction fromspontaneous texts
allow unrestricted natural language for queries
first approach : use a parsed syntactic tree
highly dependent on advanced NLP methodshow reliable the parsed syntactic tree is ?OK for queries but too complex for longer texts
9Wang et al. 2007 : “PANTO: A Portable Natural Language Interface to Ontologies”
10Bernstein et al. 2006 : “Querix: A Natural Language Interface to Query
second approach : ontology guided content extraction
try to fill in the relations with the rest of the words
NLP-reduce 11AquaLog 12 & PowerAqua 13
use few NLP notions (reduces complexity)usable to retrieve relevant information w.r.t. an ontology inspontaneous texts
11Kaufmann et al 2007 : “NLP-reduce : a naive but domain independent natural language interface for querying ontologies”12Lopez et al. 2004 : “Ontology driven question answering in AquaLog“13Lopez et al. 2006 : ”PowerAqua : fishing the Semantic Web“
translation “on the fly” of texts to the ontology lexiconlanguage 14
no extra-work when the ontology is modified
14Chang et al. 2007 : “Approaches of Using a Word-Image Ontology and an Annotated Image Corpus as Intermedia for
automatic→ is the translation reliable ?
extra-work for each language when the ontology is modified
15Espinoza et al. 2008 : “Enriching an Ontology with Multilingual Info
collaborative Wiki environment to develop a huge ontologyexperts propose new concepts for the ontology from Wordnet entries (7languages supported)the new concept is actually inserted in the ontology when a consensusis reached between every supported languages
benefits all Wordnet featuresconstructs of a real multilingual and multicultural ontology
16Vossen et al. 2008 : “KYOTO : A system for Mining, Structuring and Distributing Knowledge Across Languages and
graft linguistic sub-ontologies to the concepts 17
built a linguistic sub-ontology frame (translation and morphosyntacticdata)
for each concept of the source ontology :
instantiate the frame in the target language
17Buitelaar et al. 2006 : “A Multilingual/Multimedia Lexicon Model for
use UNL as interlingua so the system is a priori open on every language
map UNL lexicon and relations with the ontology lexicon and logicconstructors
allow a mapping update when the ontology is modified
! not compute complete UNL graphs for sentences !
annotate chunks of the text with UWs or very simple graphs
try to find matching elements on the ontology side
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