=Paper=
{{Paper
|id=Vol-1111/om2013_poster7
|storemode=property
|title=Ontological quality control in large-scale, applied ontology matching
|pdfUrl=https://ceur-ws.org/Vol-1111/om2013_poster7.pdf
|volume=Vol-1111
|dblpUrl=https://dblp.org/rec/conf/semweb/LeggS13
}}
==Ontological quality control in large-scale, applied ontology matching==
https://ceur-ws.org/Vol-1111/om2013_poster7.pdf
Ontological Quality Control in Large-scale, Applied
Ontology Matching
Catherine Legg, Samuel Sarjant
The University of Waikato, New Zealand
Email: clegg@waikato.ac.nz, sarjant@waikato.ac.nz
Abstract. To date, large-scale applied ontology mapping has relied greatly on label
matching and other relatively simple syntactic features. In search of more holistic and
accurate alignment, we offer a suite of partially overlapping ontology mapping
heuristics which allows us to hypothesise matches and test them against the
knowledge in our source ontology (OpenCyc). We thereby automatically align our
source ontology with 55K concepts from Wikipedia with 93% accuracy.
1. Introduction
We have developed a method of specifically ontological quality control in
ontology mapping which combines a suite of partially overlapping mapping
heuristics with common-sense knowledge in OpenCyc. Our approach differs from
previous largely label-matching approaches (Suchanek et al, 2008, Ponzetto and
Navigli, 2009) in its use of knowledge, and also from previous knowledge-based
approaches (Shvaiko and Euzenat, 2005, Sabou et al, 2006), in treating potential
matches as hypotheses, and testing them more iteratively and open-endedly than
previously accomplished.
2. Iterative Mapping Process
Concept to Wikipedia article mapping is governed by a priority queue which
iteratively evaluates potential mappings ordered via continuously updated
weightings. The process begins with concept-to-article mappings (Table 1), then
verifies these using article-to-concept heuristics. The weight of each potential
mapping is equal to the product of weights produced by the two sets of heuristics.
Table 1. Heuristics that map between source ontology concepts and Wikipedia articles.
Concept → Article Example
TITLE MATCHING Batman-TheComicStrip → {Batman (comic strip):1.0}
SYNONYM MATCHING ComputerWorm → {Worm:1.0, Computer worm:0.39, ... (+5 more)}
CONTEXT-RELATED ComputerWorm → {Computer worm:1.0, Worm:0.59,... (+4 more)}
SYNONYM MATCHING
Article → Concept Example
TITLE MATCHING Dog → {Dog:1.0, HotDog:1.0}
LABEL MATCHING Dog → {Dog:1.0, HotDog:0.995, CanineAnimal:0.03, CanineTooth:0.03}
A final quality control measure is the ‘consistency check’ between information on
concept and the mapped article. Most Wikipedia first sentences are conventionally
structured as: ‘X is/was/are/were a/an/the Y’, where Y is links to articles typically
2
representing appropriate classes. The mapping weight is multiplied by the
proportion of assertions not rejected using OpenCyc’s disjointness knowledge.
Example 1: “Bill Laswell is an American [[bassist]], [[record
producer|producer]] and [[record label]] owner.” Only three of the four
assertions in this sentence are kept: BillLaswell is a UnitedStatesPerson,
BassGuitarist, and Producer. BillLaswell cannot be a RecordCompany
because OpenCyc knows a person cannot be a company.
Example 2: The concept Basketball-Ball initially maps as follows
(Basketball:1.0, Basketball (ball):0.95, College basketball:0.02). The second
candidate is the correct one, as the first refers to the team sport. The algorithm
attempts to map its first choice Basketball back to Basketball-Ball, which
succeeds but also creates a new potential reverse mapping Basketball →
Basketball. Consistency checking now tests “Basketball-Ball is a
TeamSport”, which fails, removing this potential mapping. The next highest
reverse-mapping is Basketball → Basketball, which is found to be consistent, so
a mapping is recorded for that. The process now backtracks to hypothesising the
second-best option from the original list: Basketball (ball):0.95, which also
successfully reverse-maps and is consistent, creating a new (correct) mapping. It is
worth emphasising how similar the two ‘basketball concepts’ are by standard
semantic relatedness measures, and thus the subtlety our methods are capable of.
3. Results and Conclusions
The algorithm identified 54,987 mappings of OpenCyc concepts to Wikipedia
articles. Applying manual analysis to a random 300 mappings, 266 were judged
‘True’ (88.5%), 21 ‘False’ (7%) and 13 (4.3%) were assigned ‘B’ for ‘Broader
term’ (the mapping was largely correct but one side generalised the other). Thus 93%
of our mappings were either ‘True’ or highly related. Although YAGO reports 95%
accuracy, what is being rated is not mapping joins between Wordnet and
Wikipedia, but the truth of assertions in infoboxes. Although our efforts so far lack
the scale of projects such as YAGO, we suggest they have a role to play in long-
term development towards maximum accuracy in this field. We offer our results at:
http://bit.ly/10MlLjl.
References
Euzenat, J. and Shvaiko, P. (2007). Ontology Matching. Springer-Verlag.
Ponzetto, S.P., and Navigli, R. (2009). Large-Scale Taxonomy Mapping for Restructuring
and Integrating Wikipedia, IJCAI 2009, Pasadena, California, pp. 2083-2088.
Sabou, M., D’Aquin, M., Motta, E. (2006). Using the Semantic Web as Background
Knowledge for Ontology Mapping, OM-2006, Athens, GA, USA.
Shvaiko, P. and Euzenat, J. (2005). A Survey of Schema-based Matching Approaches.
Journal on Data Semantics 4.
Suchanek, F. M., Kasneci, G., and Weikum, G. (2008). Yago: A Large Ontology from
Wikipedia and WordNet. Elsevier Journal of Web Semantics 6(3), 203-217.