=Paper=
{{Paper
|id=Vol-2032/om2017_poster9
|storemode=property
|title=Using word semantics on entity names for correspondence set generation
|pdfUrl=https://ceur-ws.org/Vol-2032/om2017_poster9.pdf
|volume=Vol-2032
|authors=Rafael Vieira,Kate Revoredo
|dblpUrl=https://dblp.org/rec/conf/semweb/VieiraR17
}}
==Using word semantics on entity names for correspondence set generation==
https://ceur-ws.org/Vol-2032/om2017_poster9.pdf
Using Word Semantics on Entity Names for
Correspondence Set Generation
Rafael Vieira1 and Kate Revoredo2
1,2
Federal University of the State of Rio de Janeiro (UNIRIO), Brazil,
1
katerevoredo@uniriotec.br, 2 rvieira.research@gmail.com.br
1 Introduction
On ontology Matching, many works make use of word semantics to align the
ontologies. One commonly used resource is WordNet[4][5], which groups words
that share the same meaning together. Thesaurus and lexicons like WordNet
indeed provide rich semantic information but require large amounts of human
effort to be created and maintained.
Vector space representations of word semantics are a family of language mod-
els that associate words with vectors in a semantic space, where each dimension
represents a component of the meaning of words[2][1][3]. The semantic similarity
of words is exploited by these methods, providing vectors close in space when
their related words are close in meaning. These vectors are usually calculated by
a learning algorithm on large corpora like Wikipedia and then used to evaluate
the similarity between two words.
In this work, we exploit the word-word similarities in the GloVe model as
external resources for Ontology Matching. The hypothesis is that two entities can
be matched based on the words in their names using the word-word similarity
provided by the model. We built a prototype and evaluated its performance
against the baselines from OAEI.
2 Prototype
To build the simplest prototype, we used pre-trained vectors1 from GloVe and
two ontologies O1 and O2 . Then, each entity e defined in O1 or O2 is associated
with one vector v#»e = (a1 , . . . , an ), based on its name, where each component ai
represents the semantic dimension of words that have related meaning. In case
entity e has a compound name, we average the vectors of each word in its name,
and set the resulting vector as v#»e .
To generate a correspondence between two entities e1 and e2 , from O1 and O2
respectively, we calculate the cosine similarity on vectors v#»1 and v#»2 , associated
with e1 and e2 , respectively. If the value of cosine similarity is above a lower
bound, we continue with this correspondence, otherwise, it is discarded. This
lower bound was empirically set to 0.7 as this value showed the better results.
1
Obtained at http://nlp.stanford.edu/data/glove.6B.zip
2
After doing this procedure for all entity pairs, we have the complete align-
ment. Finally, we compare this alignment with the baseline alignments edna(edit
distance based) and StringEquiv(string equivalence based) from OAEI 2016 on
the conference and benchmark data sets. The results are presented in table 1.
Dataset (method) Precision Recall F1 -measure
Conference (edna) 0.74 0.45 0.56
Conference (StringEquiv) 0.76 0.41 0.53
Conference (Prototype) 0.71 0.45 0.54
Benchmark (edna) 0.35 0.51 0.41
Benchmark (Prototype) 0.72 0.26 0.34
Table 1. Comparison between the prototype and baselines of each data set
The prototype obtained low recall on both data sets. The majority of errors
on the benchmark data set were on tests with random entity names, resulting
in the low recall. This is expected since our method uses only this source of
information to gather the entity semantics and then generate correspondences.
On the conference data set, the prototype performed between the two base-
lines. Many words from entity names were not in the vocabulary of the vectors,
#»
and were assigned the vector 0 , which contributes to the average recall.
3 Conclusion
These results are not ground-breaking, but also promising. Furthermore, given
the simplicity of the prototype, there are many places where it can be improved.
For example, in a future experiment, we should train our own vectors and fine
tune the hyperparameters of the model. We believe that these improvements
may provide increased performance and lead to further research in the area.
References
1. Pennington, J., Socher, R. Manning, C. D.: GloVe: Global Vectors for Word Rep-
resentation. Empirical Methods in Natural Language Processing (EMNLP), 1532–
1543 (2014)
2. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Repre-
sentations in Vector Space Computing Research Repository (CoRR), abs-1301-3781
(2013)
3. Gabrilovich, E., Markovitch, S.: Wikipedia-based Semantic Interpretation for Nat-
ural Language Processing J. Artif. Intell. Res., 34, 443–498 (2009)
4. He, W., Yang, X., Huang, D.: A Hybrid Approach for Measuring Semantic Simi-
larity between Ontologies Based on WordNet Knowledge Science, Engineering and
Management - 5th International Conference, 68–78 (2011)
5. Lin, F., Sandkuhl, K.: A Survey of Exploiting WordNet in Ontology Matching.
Artificial Intelligence in Theory and Practice II, 43, 341–350 (2008)