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Aline Paes

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KER Journal 2021 Journal Article

Learning multiple concepts in description logic through three perspectives

  • Raphael Melo
  • Kate Revoredo
  • Aline Paes

Abstract An ontology formalises a number of dependent and related concepts in a domain, encapsulated as a terminology. Manually defining such terminologies is a complex, time-consuming and error-prone task. Thus, there is great interest for strategies to learn terminologies automatically. However, most of the existing approaches induce a single concept definition at a time, disregarding dependencies that may exist among the concepts. As a consequence, terminologies that are difficult to interpret may be induced. Thus, systems capable of learning all concepts within a single task, respecting their dependency, are essential for reaching concise and readable ontologies. In this paper, we tackle this issue presenting three terminology learning strategies that aim at finding dependencies among concepts, before, during or after they have been defined. Experimental results show the advantages of regarding the dependencies among the concepts to achieve readable and concise terminologies, compared to a system that learns a single concept at a time. Moreover, the three strategies are compared and analysed towards discussing the strong and weak points of each one.

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AAAI Conference 2021 Short Paper

Screening for Depressed Individuals by Using Multimodal Social Media Data

  • Paulo Mann
  • Aline Paes
  • Elton H. Matsushima

Depression has increased at alarming rates in the worldwide population. One alternative to finding depressed individuals is using social media data to train machine learning (ML) models to identify depressed cases automatically. Previous works have already relied on ML to solve this task with reasonably good F-measure scores. Still, several limitations prevent the full potential of these models. In this work, we show that the depression identification task through social media is better modeled as a Multiple Instance Learning (MIL) problem that can exploit the temporal dependencies between posts.

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