INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Field-programmable gate array & Control theory. The organization has 932 authors who have published 2618 publications receiving 37658 citations.

POPmine: Tracking Political Opinion on the Web

Abstract: The automatic content analysis of mass media in the social sciences has become necessary and possible with the raise of social media and computational power. One particularly promising avenue of research concerns the use of opinion mining. We design and implement the POPmine system which is able to collect texts from web-based conventional media (news items in mainstream media sites) and social media (blogs and Twitter) and to process those texts, recognizing topics and political actors, analyzing relevant linguistic units, and generating indicators of both frequency of mention and polarity (positivity/negativity) of mentions to political actors across sources, types of sources, and across time.

A Modular Ontology for the Enterprise Architecture Domain

Abstract: Enterprise architecture supports the analysis, design and engineering of business-oriented systems through multiple views. Each view expresses the elements and relationships of a system from the perspective of specific system concerns relevant to one or more of its stakeholders. As a result, each view needs to expressed in the architecture description language that best suits its concerns. Therefore, an enterprise architecture may be described using a set of different languages. However, current enterprise architecture modelling languages display two issues in this setting. First, they lack mechanisms to integrate multiple architecture description languages. This issue hinders the specification of views using different languages. Second, enterprise architecture models lack quantitative analysis support. This paper describes an ontology-based approach in order to have a modular ontology for the enterprise architecture domain, to specify and integrate multiple architecture modelling languages and to analyse the resulting integrated models. The approach relies on transformations between an upper-domain ontology based on the ArchiMate language and on a set of domain-specific ontologies to deal with the specific architecture modelling languages. The resulting models are quantifiable in the sense they provide the means to assess the consistency of the enterprise architecture models and to analyse their structure. The applicability of the approach is shown through a case study and the correctness of the ontology is shown by a set of competency questions.

Fairy Tale Corpus Organization Using Latent Semantic Mapping and an Item-to-item Top-n Recommendation Algorithm.

Abstract: In this paper we present a fairy tale corpus that was semantically organized and tagged. The proposed method uses latent semantic mapping to represent the stories and a top-n item-to-item recommendation algorithm to define clusters of similar stories. Each story can be placed in more than one cluster and stories in the same cluster are related to the same concepts. The results were manually evaluated regarding the groupings as perceived by human judges. The evaluation resulted in a precision of 0.81, a recall of 0.69, and an f-measure of 0.75 when using tf*idf for word frequency. Our method is topic- and language-independent, and, contrary to traditional clustering methods, automatically defines the number of clusters based on the set of documents. This method can be used as a setup for traditional clustering or classification. The resulting corpus will be used for recommendation purposes, although it can also be used for emotion extraction, semantic role extraction, meaning extraction, text classification, among others.

Machine learning in incident categorization automation

Abstract: IT incident management process requires a correct categorization to attribute incident tickets to the right resolution group and obtain an operational system as quickly as possible, having the lowest possible impact on the business and costumers. In this work, we introduce a module to automatically categorize incident tickets, turning the responsible teams for incident management more productive. This module can be integrated as an extension into an incident ticket system (ITS), which contributes to reduce the time wasted on incident ticket route and reduce the amount of errors on incident categorization. To automate the classification, we use a support vector machine (SVM), obtaining an accuracy of 89%, approximately, on a dataset of real-world incident tickets.

Children's peer assessment and self-disclosure in the presence of an educational robot

Abstract: Research in education has long established how children mutually influence and support each other's learning trajectories, eventually leading to the development and widespread use of learning methods based on peer activities. In order to explore children's learning behavior in the presence of a robotic facilitator during a collaborative writing activity, we investigated how they assess their peers in two specific group learning situations: peer-tutoring and peer-learning. Our scenario comprises of a pair of children performing a collaborative activity involving the act of writing a word/letter on a tactile tablet. In the peer-tutoring condition, one child acts as the teacher and the other as the learner, while in the peer-learning condition, both children are learners without the attribution of any specific role. Our experiment includes 40 children in total (between 6 and 8 years old) over the two conditions, each time in the presence of a robot facilitator. Our results suggest that the peer-tutoring situation leads to significantly more corrective feedback being provided, as well as the children more disposed to self-disclosure to the robot.