# Similarity measurement applied to information research and indexing.

V. Hoareau, Fabien Gandon, A. Giboin, G. Denhière, Sandra Jhean-Larose, W. Lehnard, H. Baier

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## To cite this version:

V. Hoareau, Fabien Gandon, A. Giboin, G. Denhière, Sandra Jhean-Larose, et al. Similarity measurement applied to information research and indexing. Proceedings of the First European Workshop on Latent Semantic Analysis in Technology Enhanced Learning, 2007, Heerlen, Netherlands. pp.5-6. hal-00644236

HAL Id: hal-00644236

https://hal.science/hal-00644236v1

Submitted on 30 Sep 2025

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# Similarity Measurement Applied to Information Research and Indexing

Y. V. Hoareau, F. Gandon, A. Giboin, G. Denhière, S. Jhean-Larose, W. Lenhard, H. Baier

## I. INTRODUCTION

THE main goal of the work reported here is to compare two methods for simulating of similarity measures and to identify opportunities to combine them. The first method is based on distance measures between concepts as captured in ontologies; for this part we rely on « Corese » a semantic web search engine [1]. The second method is based on a similarity measure in a word/document vector space; for this we rely on Latent Semantic Analysis [2] to compare different effects of corpus modification [3] on performances in research-indexing task [4]. We have created different semantic spaces from corpora composed of titles and abstracts of research reports of INRIA<fnref n="1" />

## II. EXPERIENCES

### A. *Description of semantic spaces*

We built five vector spaces: one space in French with a corpus which contains stop-words and four spaces in English : one with a lemmatised corpus, a second with a corpus which contains stop-words, a third which do not contains stop-words and the last one where the corpus has been augmented by adding broader terms from the ACM98 thesaurus to simulate the inclusion of ontological knowledge in the corpus. All these spaces have been evaluated with three tests corresponding to a particular situation of retrieval of information.

### B. *Description of tests*

In the first test procedure, we compare the similarity between an abstract and a set of keywords list. The set of keyword lists is composed of lists of one hundred keywords each. In every set there is a target keywords list that actually corresponds to the abstract tested. We calculate the similarity between the vector corresponding to the abstract and the vectors corresponding to the keyword lists, assuming that the target keywords list should be more similar to the abstract tested than the other keyword lists.

In the second test procedure, we calculate the similarity between a keywords list and all the abstracts composing the data-base (more than 4000). We still have a target abstract that actually corresponds to the keyword list tested, and we postulate that the keywords list tested should be more similar to the target abstract than the other abstracts.

The third test procedure is close to the first experience but instead of calculating the similarity between an abstract and a set of keyword lists, we calculate the similarity between an abstract and a set of five hundred individual keywords. We calculate the similarity measure between the vector corresponding to the abstract and all the vectors corresponding to each individual keyword. Some of these keywords actually are the keywords attached to the abstract by its author. We assume that the keywords that actually corresponds to the abstract being tested should be more similar to the abstract than the others.

### C. *Description of experimental material*

We used two sets of research reports to do the test. The first set of research reports has been randomly extracted from the librarian database. The second set of texts has been chosen from the research reports of a research team with a precise research domain (networks). The comparison of the results for the different spaces allows (i) to evaluate the effect of lemmatization, (ii) to evaluate to effect of the subsumption information added in the corpora and (iii) to optimize the parameters of the spaces in both languages to make it possible to build a French-English semantic space.

As an illustration, we present the results of the third experience applied on the first set of research reports, that compare the performances of LSA using with different corpora to find the keywords given by the author for his research report.

The corpus tested are English with stop-words ("English Classic") vs English lemmatised ("English Lemma") vs English with ontological knowledge ("OntoLSA").

<footnote n="1">¹ https://hal.inria.fr/INRIA-SOPHIA/fr/</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 1. *Numbers of successes and failures in finding author's keywords for ten research reports using different LSA spaces.*</figcaption></figure>

The results show that the three systems are able to give relatively good performances to find the author's keywords. Lemmatisation gives the best performances in this specific task. The addition of ontological knowledge does not obviously gives the expected better performances for this task.

The experiment that has been developed raises natural queries and makes it possible to compare the performances of the different systems and to evaluate the interest of hybrid systems in the context of document retrieval.

## REFERENCES

[1] O. Corby, R. Dieng-Kuntz, C. Faron-Zucker, F. Gandon "Searching the Semantic Web: Approximate Query Processing based on Ontologie", *IEEE Intelligent Systems Journal*, 21 (1), 2006.

[2] Landauer, T. K., Dumais, S. T. "A solution to Plato's problem: the Latent Semantic Analysis theory of acquisition, induction and representation of knowledge", *Psychological Review*, 104(2), 211-240, 1997.

[3] G. Denhière, B. Lemaire, C. Bellissens, S. Jhean-Larose, "A semantic space for modeling children's semantic memory" In D. McNamara, T. Landauer, S. Dennis, W. Kintsch (Eds). *The handbook of Latent Semantic Analysis*. Mahwah: Lawrence Erlbaum Associates, 2007.

[4] S. Deerwester, S. Dumais, G. Furnas, T. K. Landauer, R. Harshman, "Indexing by latent semantic analysis", *Journal of the American Society for Information Science*, 41(6), 391-407, 1990.