=Paper=
{{Paper
|id=None
|storemode=property
|title=Harnessing the power of folksonomies for formal
ontology matching on-the-fly
|pdfUrl=https://ceur-ws.org/Vol-689/om2010_poster4.pdf
|volume=Vol-689
|dblpUrl=https://dblp.org/rec/conf/semweb/TogiaMB10
}}
==Harnessing the power of folksonomies for formal
ontology matching on-the-fly==
https://ceur-ws.org/Vol-689/om2010_poster4.pdf
Harnessing the power of folksonomies for formal
ontology matching on-the- y
Theodosia Togia, Fiona McNeill and Alan Bundy
School of Informatics, University of Edinburgh, EH8 9LE, Scotland
Abstract. This paper is a short introduction to our work on build-
ing and using folksonomies to facilitate communication between Seman-
tic Web agents with disparate ontological representations. We brie y
present the Semantic Matcher, a system that measures the semantic
proximity between terms in interacting agents' ontologies at run-time,
fully automatically and minimally: that is, only for semantic mismatches
that impede communication. The system is designed to allow agents to
"understand" the meanings of terms to be matched by comparing their
folksonomy-based "mental representations".
1 Introduction
The Semantic Matcher is an extension of the Ontology Repair System (ORS)
[2], a plug-in for a service-requesting agent (requester ) in the Semantic Web.
Terms unknown to the requester which are encountered during interaction with a
service-providing agent (provider ) are mapped to terms in the former's ontology.
We assume that the requester, and therefore ORS, has no access to the provider's
ontology beyond what is revealed during interation: we are thus concerned with
matching not two full ontologies but only individual terms from the provider's
ontology to the most relevant terms of the requester's ontology.
We believe that the most serious obstacle for meaning sharing between agents
is the lack of symbol grounding in ontologies: ontology terms are unable to refer
to the objective world without human interpretation. We argue [3] that this can
be dealt with if we allow agents to interprete the meanings of their terms by
building a mental representation (sense) [1] for each one of these terms. In our
work, senses are simulated by broad folksonomies [4] which annotate physical
or abstract resources as opposed to digital resources (e.g. the set of cats in
the world vs. a web-page about cats). Folksonomies are created and related
to the requester's (formal) ontology. We show that combining ontologies and
folksonomies in this way can allow fast and eective matching to be done on-
the- y and provides a way of grounding terms in ontologies to real-world entities.
2 Using Folksonomies for Ontology Matching
The architecture of our matcher is inspired by that of search engines. Broad
folksonomies (comparable to "virtual documents" [5] or bags of words) are built
for every candidate term (i.e. name of a relation, class or individual) in the re-
quester's ontology with our sense creation algorithm (which extracts information
from databases such as WordNet and SUMO and manipulates it with techniques
such as stemming and stopping1 ). During agent interaction, when ORS diagnoses
a semantic mismatch, a sense must be created for the unknown term, which will
act as a query to the search engine. This step must be performed on-the- y with-
out interrupting normal interaction more than necessary. Our search engine then
takes the sense representing the unknown term and a list of senses representing
the requester's candidate terms as input and returns as output a ranking of the
candidate terms.
3 Implementation and Evaluation
The system brie y discussed here has been fully implemented. Evaluation was
performed using dierent versions of the SUMO ontology and its sub-ontologies
from the Sigmakee repository2 . When terms are changed between SUMO ver-
sions (e.g. "Corn" becomes "Maize"), we have an objective way of measuring
the performance of the matcher because we can safely regard terms and their re-
namings as synonyms and compare these pairings with our system's prediction.
Initial results are encouraging, with 57% of correct matches chosen as the best
by the system, 19% as the second-best.
4 Further Work and Conclusions
This paper brie y introduced our work on integrating folksonomies with formal
ontologies to perform matching on-the- y, whenever the need becomes apparent.
We believe these ideas could be a major step forward in the problem of ontology
matching in an agent communication environment, and in providing symbol
grounding for ontology terms. Furthermore, they can provide a framework for
the design of matchers which exploit the vast amount of tag data available on
the web. Full details of the theory on which this work is based, together with
full descriptions of the implementation and evaluation, can be found in [3]. We
are currently extending this work and evaluating it more fully.
References
1. G. Frege (1892). On sense and reference. In P. Ludlow, editor, Readings in the
Philosophy of Language. MIT, 1997.
2. F. McNeill and Alan Bundy. Dynamic, automatic, rst-order ontology repair by
diagnosis of failed plan execution. IJSWIS (International Journal on Semantic Web
and Information Systems) special issue on Ontology Matching, 3:135, 2007.
3. T. Togia. Automated ontology evolution: Semantic matching. Mas-
ter's thesis, University of Edinburgh, 2010. Available online at
http://dream.inf.ed.ac.uk/projects/dor/ (unpublished).
4. T. Vander Wal. Explaining and showing broad and norrow folksonomies, 2005.
available online at http://www.vanderwal.net/random/entrysel.php?blog=1635.
5. W. Hu Y. Qu and G. Cheng. Constructing virtual documents for ontology matching.
In Proceedings of the 15th International WWW Conference, pages 2331, 2006.
1
Supplementing this data with folksonomies discovered on the web (e.g. tags that
often co-occur with the tag "cat") is also possible though not currently implemented.
2
http://sigmakee.cvs.sourceforge.net/viewvc/sigmakee/KBs/