semantic similarity (PDF)

Annotated Bibliography on Semantic Similarity
Jorge Martinez-Gil
Software Competence Center Hagenberg GmbH
Softwarepark 21, 4232 Hagenberg, Austria
jorge. martinez-gil@ scch. at

An overview of textual semantic similarity measures based on web intelligence
Computing the semantic similarity between terms (or short text expressions) that have the same
meaning but which are not lexicographically similar is a key challenge in many computer related fields.
The problem is that traditional approaches to semantic similarity measurement are not suitable for all
situations, for example, many of them often fail to deal with terms not covered by synonym dictionaries
or are not able to cope with acronyms, abbreviations, buzzwords, brand names, proper nouns, and so
on. In this paper, we present and evaluate a collection of emerging techniques developed to avoid this
problem. These techniques use some kinds of web intelligence to determine the degree of similarity
between text expressions. These techniques implement a variety of paradigms including the study of
co-occurrence, text snippet comparison, frequent pattern finding, or search log analysis. The goal is
to substitute the traditional techniques where necessary (Martinez-Gil, 2014).

CoTO: A novel approach for fuzzy aggregation of semantic similarity
Semantic similarity measurement aims to determine the likeness between two text expressions that
use different lexicographies for representing the same real object or idea. There are a lot of semantic
similarity measures for addressing this problem. However, the best results have been achieved when
aggregating a number of simple similarity measures. This means that after the various similarity
values have been calculated, the overall similarity for a pair of text expressions is computed using an
aggregation function of these individual semantic similarity values. This aggregation is often computed
by means of statistical functions. In this work, we present CoTO (Consensus or Trade-Off) a solution
based on fuzzy logic that is able to outperform these traditional approaches (Martinez-Gil, 2016).

Preprint submitted to Elsevier

January 3, 2019

Semantic similarity measurement using historical google search patterns
Computing the semantic similarity between terms (or short text expressions) that have the same
meaning but which are not lexicographically similar is an important challenge in the information
integration field. The problem is that techniques for textual semantic similarity measurement often
fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem
by determining the semantic similarity for terms using the knowledge inherent in the search history
logs from the Google search engine. To do this, we have designed and evaluated four algorithmic
methods for measuring the semantic similarity between terms using their associated history search
patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b)
computation of the relationship between search patterns, c) outlier coincidence on search patterns,
and d) forecasting comparisons. We have shown experimentally that some of these methods correlate
well with respect to human judgment when evaluating general purpose benchmark datasets, and
significantly outperform existing methods when evaluating datasets containing terms that do not
usually appear in dictionaries. (Martinez-Gil & Montes, 2013).

References
Martinez-Gil, J. (2014). An overview of textual semantic similarity measures based on web intelligence.
Artif. Intell. Rev., 42 , 935–943. URL: https://hal.archives-ouvertes.fr/hal-01630890/
document. doi:10.1007/s10462-012-9349-8.
Martinez-Gil, J. (2016).

Coto: A novel approach for fuzzy aggregation of semantic similarity

measures. Cognitive Systems Research, 40 , 8–17. URL: https://hal.archives-ouvertes.fr/
hal-01695099/document. doi:10.1016/j.cogsys.2016.01.001.
Martinez-Gil, J., & Aldana-Montes, J. F. (2013). Semantic similarity measurement using historical google search patterns. Information Systems Frontiers, 15 , 399–410. URL: https://hal.
archives-ouvertes.fr/hal-01628399/document. doi:10.1007/s10796-012-9404-7.

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