=Paper=
{{Paper
|id=Vol-292/paper-2
|storemode=property
|title=Ontology Learning: Where are we? And where are we going?
|pdfUrl=https://ceur-ws.org/Vol-292/paper2.pdf
|volume=Vol-292
|authors=Paul Buitelaar,pages 3-4
|dblpUrl=https://dblp.org/rec/conf/semweb/Buitelaar07
}}
==Ontology Learning: Where are we? And where are we going?==
<pdf width="1500px">https://ceur-ws.org/Vol-292/paper2.pdf</pdf>
<pre>
           Ontology Learning: Where are we?
               And where are we going?

                                 Paul Buitelaar

                                DFKI GmbH
          Language Technology Lab & Competence Center Semantic Web
                            Saarbrücken, Germany

    Ontology learning concerns the development of automatic methods for the
extraction of a domain model from a relevant, i.e. domain-specific data set. In the
context of ontology evolution, a specific domain model is already given and the
task of ontology learning reduces to the extension or adaptation of this domain
model on the basis of a changing underlying data set.
    Ontology learning largely builds on methods previously developed in knowl-
edge acquisi- tion, natural language processing and machine learning although
with the specific purpose of automatically deriving an ontology, i.e. an explicit,
shared and formally defined logical model. Unfortunately, the current state-of-
the-art in ontology learning cannot be said to have reached this goal yet, although
progress is made on various levels over the last couple of years.
    Ontology learning is in fact not really one task but rather a collection of
tightly connected subtasks that can be organized in a layered representation
of increasing complexity, i.e. term extraction, synonym and translation detec-
tion, concept formation, instantiation, relation extraction, paraphrase and rule
derivation, axiomatization. On each of these levels, methods and tools have been
developed that address one or more subtasks. Methodologies are still needed
however that address all subtasks in a coherent way and provide benchmarks for
evaluation of methods on all levels, separately and in combination.
    Ontology learning tools need to perform well on all levels of analysis, but
even this is no ultimate guarantee for being actually useful. In addition to per-
formance considerations, ontology learning tools need to be fully integrated into
the knowledge engineering life-cycle, working in the background and providing
the human domain expert with relevant input for ontology construction or evo-
lution. Usability of ontology learning tools will thus be measured in terms of
productivity of the human domain expert.
    Ontology learning until recently has been based mostly on knowledge ex-
traction from textual data, although some work has been done on extraction
from tables and other structured data. Currently however, more and more semi-
structured data becomes available in the form of Wikis and User Tags that shows
a number of advantages for ontology learning as these data sets carry a lot of
implicit knowledge (i.e. relations by linking or by social grouping) that can be
more easily extracted than similarly implicit knowledge available in textual data.
Additionally, more and more ontologies become publicly available that may be
used as input by ontology learning tools, possibly in combination with knowledge
derived from Wikis and User Tags and from more traditional textual data sets.
    Ontology learning is a relatively new field of research, although building on
long-standing methods in AI. In the developing context of the Semantic Web it
is and will remain a central field of attention as ontologies form the semantic
backbone of the Semantic Web, whereas their construction is complex and there-
fore knowledge- and cost-intensive. Automating this process through ontology
learning thus remains an attractive proposition.

</pre>