Azucena Montes

An iterative clustering method for the XML-mining task of the INEX 2010

In this paper we propose two iterative clustering methods for grouping Wikipedia documents of a given huge collection into clusters. The recursive method clusters iteratively subsets of the complete collection. In each iteration, we select representative items for each group, which are then used for the next stage of clustering. The presented approaches are scalable algorithms which may be used with huge collections that in other way (for instance, using the classic clustering methods) would be computationally expensive of being clustered. The obtained results outperformed the random baseline presented in the INEX 2010 clustering task of the XML-Mining track.

Use of Lexico-Syntactic Patterns for the Evaluation of Taxonomic Relations

In this paper we present an approach for the evaluation of taxonomic relations of restricted domain ontologies. We use the evidence found in corpora associated to the ontology domain for determining the validity of the taxonomic relations. Our approach employs lexico-syntactic patterns for evaluating taxonomic relations in which the concepts are totally different, and it uses a particular technique based on subsumption for those relations in which one concept is completely included in the other one. The integration of these two techniques has allowed to automatically evaluate taxonomic relations for two ontologies of restricted domain. The performance obtained was about 70% for one ontology of the e-learning domain, whereas we obtained around 88% for the ontology associated to the artificial intelligence domain.

An Approach Based in LSA for Evaluation of Ontological Relations on Domain Corpora

In this paper we present an approach for the automatic evaluation of relations in ontologies of restricted domain. We use the evidence found in a corpus associated to the same domain of the ontology for determining the validity of the ontological relations. Our approach employs Latent Semantic Analysis, a technique based on the principle that the words in a same context tend to have semantic relationships. The approach uses two variants for evaluating the semantic relations and concepts of the target ontologies. The performance obtained was about 70% for class-inclusion relations and 78% for non-taxonomic relations.

Learning Discourse Relations from News Reports: An Event-driven Approach

Nowadays, technologies allows us to store large volumes of information in different formats. It represents a challenge due to the lack of semantic in retrieval and extraction process of information efficiently. A possible strategy is to transform unstructured information into structured data. In recent years, ontologies have been widely used as an alternative to represent structured data from texts. This paper presents a new approach based on linguistic markers for ontology learning and population by considering cognitive aspects in order identify discourse relations between events from news reports. The main idea is to find concepts (event type), discourse relations (ontological relations) between events and class instances (real events). Our approach shows promising results for learning discourse relations in terms of F-measure.

Patterns Used to Identify Relations in Corpus Using Formal Concept Analysis

In this paper we present an approach for the automatic identification of relations in ontologies of restricted domain. We use the evidence found in a corpus associated to the same domain of the ontology for determining the validity of the ontological relations. Our approach employs formal concept analysis, a method used for the analysis of data, but in this case used for relations discovery in a corpus of restricted domain. The approach uses two variants for filling the incidence matrix that this method employs. The formal concepts are used for evaluating the ontological relations of two ontologies. The performance obtained was about 96i¾?for taxonomic relations and 100i¾?% for non-taxonomic relations, in the first ontology. In the second it was about 92i¾?% for taxonomic relations and 98i¾?% for non-taxonomic relations.

Determining the Degree of Semantic Similarity Using Prototype Vectors

Measuring the degree of semantic similarity for word pairs is very challenging task that has been addressed by the computational linguistics community in the recent years. In this paper, we propose a method for evaluating input word pairs in order to measure the degree of semantic similarity. This unsupervised method uses a prototype vector calculated on the basis of word pair representative vectors which are contructed by using snippets automatically gathered from the world wide web.