Semi-Automated Ontology Generation Process from Industrial Product Data Standards

Ontology development has become an important research area for manufacture industries. Ontologies are one of the most popular methods to achieve semantic interoperability between information systems. In previous works, an ontology network that reuses ontological and non-ontological re-sources have been proposed in order to reach semantic interoperability. Howev-er, processing non-ontological resources to build an ontology is a great time-consuming task. Therefore, this work presents a framework and a prototype tool to support the reuse of the non-ontological resources involved in the develop-ment of the ontology network.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Semi-Automated Ontology Generation Process from Industrial Product Data Standards

Ontology development has become an important research area for manufacture industries. Ontologies are one of the most popular methods to achieve semantic interoperability between information systems. In previous works, an ontology network that reuses ontological and non-ontological re-sources have been proposed in order to reach semantic interoperability. Howev-er, processing non-ontological resources to build an ontology is a great time-consuming task. Therefore, this work presents a framework and a prototype tool to support the reuse of the non-ontological resources involved in the develop-ment of the ontology network.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Word Sense Disambiguation for Ontology Learning

Ontology learning aims to automatically extract ontological concepts and relationships from related text repositories and is expected to be more efficient and scalable than manual ontology development. One of the challenging issues associated with ontology learning is word sense disambiguation (WSD). Most WSD research employs resources such as WordNet, text corpora, or a hybrid approach. Motivated by the large volume and richness of user-generated content in social media, this research explores the role of social media in ontology learning. Specifically, our approach exploits social media as a dynamic context rich data source for WSD. This paper presents a method and preliminary evidence for the efficacy of our proposed method for WSD. The research is in progress toward conducting a formal evaluation of the social media based method for WSD, and plans to incorporate the WSD routine into an ontology learning system in the future

Inter-Coder Agreement for Computational Linguistics

This article is a survey of methods for measuring agreement among corpus annotators. It exposes the mathematics and underlying assumptions of agreement coefficients, covering Krippendorff's alpha as well as Scott's pi and Cohen's kappa; discusses the use of coefficients in several annotation tasks; and argues that weighted, alpha-like coefficients, traditionally less used than kappa-like measures in computational linguistics, may be more appropriate for many corpus annotation tasks—but that their use makes the interpretation of the value of the coefficient even harder. </jats:p

Semi-Automated Ontology Generation Process from Industrial Product Data Standards

Ontology development has become an important research area for manufacture industries. Ontologies are one of the most popular methods to achieve semantic interoperability between information systems. In previous works, an ontology network that reuses ontological and non-ontological re-sources have been proposed in order to reach semantic interoperability. Howev-er, processing non-ontological resources to build an ontology is a great time-consuming task. Therefore, this work presents a framework and a prototype tool to support the reuse of the non-ontological resources involved in the develop-ment of the ontology network.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Leveraging the Wisdom of the Crowd to Address Societal Challenges: Revisiting the Knowledge Reuse for Innovation Process through Analytics

Societal challenges can be addressed not only by experts but also by crowds. Crowdsourcing provides a way to engage a crowd to contribute to the solutions of some of the biggest challenges of our era: how to cut our carbon footprint, how to address worldwide epidemic of chronic disease, and how to achieve sustainable development. Isolated crowd-based solutions in online communities are not always creative and innovative. Hence, remixing has been developed as a way to enable idea evolution and integration, and to harness reusable innovative solutions. Understanding the generativity of remixing is essential to leveraging the wisdom of the crowd to solve societal challenges. At its best, remixing can promote online community engagement, as well as support comprehensive and innovative solution generation. Organizers can maintain an active online community, community members can collectively innovate and learn, and, as a result, society can find new ways to solve important problems. We address what affects the generativity of a remix by revisiting the knowledge reuse for innovation process model. We analyze the reuse of proposals in Climate CoLab, an online innovation community that aims to address global climate change issues. Our application of several analytical methods to study factors that may contribute to the generativity of a remix reveals that remixes that include prevalent topics and integration metaknowledge are more generative. We conclude by suggesting strategies and tools that can help online communities better harness collective intelligence for addressing societal challenges

Propuesta de aprendizaje ontológico a partir de datos textuales que aporte a la construcción del carácter adaptativo de una conceptualización unificadora y formal del dominio de liderazgo

El liderazgo es un área importante de investigación que ha sido abordada desde diferentes enfoques. Lo anterior ha generado múltiples explicaciones o interpretaciones, llegando a surgir superposición y ambigüedad en la información construida. En consecuencia, se ha dificultado la concepción de una conceptualización unificadora, adecuadamente construida, consisa y sin ambiguedades que brinde una comprensión global de esta área. Este trabajo presenta una propuesta de aprendizaje ontológico a partir de datos textuales para establecer vocabulario y conceptos del domino de liderazgo. De esta forma, aportar a la construcción del carácter adaptativo de una conceptualización unificadora y formal de este dominio, que permitaidentificar y expresar los cambios que se experimentan en este dominio.Leadership is an important area of research that has been approached from different perspectives. This has generated multiple explanations or interpretations, leading to overlapping and ambiguity in the constructed information. Consequently, the conception of a unifying, adequately constructed, concise and unambiguous conceptualization that provides a global understanding of this area has been difficult. This work presents an ontological learning proposal based on textual data to establish vocabulary, and concepts of the leadership domain. In this way, contribute to the construction of the adaptive character of a unifying and formal conceptualization of this domain, which allows identifying and expressing the changes that are experienced in this domain.MaestríaMagíster en Investigación Operativa y EstadísticaContenido Capítulo 1. Introducción.......................................................................................................... 9 Capítulo 2. Descripción del proyecto.................................................................................... 11 2.1. Planteamiento del problema de investigación y justificación ....................................... 11 Capítulo 3. Marco de referencia ........................................................................................... 17 3.1. Antecedentes y Estado del arte ..................................................................................... 17 3.1.1. Liderazgo................................................................................................................ 17 3.1.2. Esquematización del liderazgo............................................................................... 19 3.1.3. Aprendizaje ontológico .......................................................................................... 26 3.1.4. Modelos de tópicos.............................................................................................. 27 Capítulo 4. Objetivos ............................................................................................................. 31 Objetivo general:.................................................................................................................. 31 Objetivos específicos: .......................................................................................................... 31 Capítulo 5. Metodología......................................................................................................... 32 5.1. Fase I: Preparación de datos textuales .......................................................................... 33 5.1.1. Preprocesamiento de los documentos .................................................................... 33 5.2. Fase II: Evaluación del etiquetado del corpus............................................................... 34 5.3. Fase III: Construcción y evaluación de vocabulario de términos relevantes................ 34 5.3.1. Construcción y evaluación de vocabulario ............................................................ 34 5.3.2. Clasificación de Oraciones..................................................................................... 37 5.4. Fase IV: Conceptos....................................................................................................... 44 5.4.1. Modelos de tópicos ................................................................................................ 44 5.4.2. Evaluación basada en gold standard ...................................................................... 51 5.4.3. Topic coherence (TC) ............................................................................................ 51 5.4.4. Clasificación de documentos: ................................................................................ 51 Capítulo 6. Resultados y discusión ....................................................................................... 53 6.1. Fase I: Preprocesamiento .............................................................................................. 53 6.2. Fase II: Evaluación de las etiquetas: ............................................................................. 56 6.3. Fase III: Términos- Construcción y evaluación de vocabulario ................................... 57 6.3.1. Evaluación basada en gold standard ...................................................................... 57 6.3.2. Clasificación de oraciones...................................................................................... 59 6.4. Fase IV: Conceptos....................................................................................................... 62 6.4.1. Evaluación de estructuras ontológicas ................................................................... 63 Capítulo 7. Conclusiones ....................................................................................................... 73 Referencias.............................................................................................................................. 7