Estevam R. Hruschka, Jr.

Also published as: Estevam R. Hruschka Jr.


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Merging knowledge bases in different languages
Jerónimo Hernández-González | Estevam R. Hruschka Jr. | Tom M. Mitchell
Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing

Recently, different systems which learn to populate and extend a knowledge base (KB) from the web in different languages have been presented. Although a large set of concepts should be learnt independently from the language used to read, there are facts which are expected to be more easily gathered in local language (e.g., culture or geography). A system that merges KBs learnt in different languages will benefit from the complementary information as long as common beliefs are identified, as well as from redundancy present in web pages written in different languages. In this paper, we deal with the problem of identifying equivalent beliefs (or concepts) across language specific KBs, assuming that they share the same ontology of categories and relations. In a case study with two KBs independently learnt from different inputs, namely web pages written in English and web pages written in Portuguese respectively, we report on the results of two methodologies: an approach based on personalized PageRank and an inference technique to find out common relevant paths through the KBs. The proposed inference technique efficiently identifies relevant paths, outperforming the baseline (a dictionary-based classifier) in the vast majority of tested categories.


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Minimização do Impacto do Problema de Desvio de Conceito por Meio de Acoplamento em Ambiente de Aprendizado Sem Fim (Minimizing the Impact of the Concept Drift Problem by Using a Framework of Endless Learning) [in Portuguese]
Maisa Cristina Duarte | Estevam R. Hruschka Jr. | Maria do Carmo Nicoletti
Proceedings of the 8th Brazilian Symposium in Information and Human Language Technology