Combining knowledge discovery, ontologies, annotations, and semantic wikis

Semantic Wikis provide an original and operational infrastructure for efficiently com- bining semantic technologies and collaborative design activities. This text presents: a running example and its context (organization of the collections in a museum); concepts of wikis as a tool to allow computer supported cooperative work (cscw); concepts of se- mantic technologies and knowledge representation; concepts and examples of semantic wikis; anatomy of a semantic wiki (reasoning tools, storage, querying); and research directions.Laboratorio de Investigación y Formación en Informática Avanzad

Agro-Knowledge Integration: Developing a FAIR data science approach for adding value to the agricultural supply chain

Farms are the engine to support rural employment making a considerable contribution to territorial development. Even though they have always been considered a cornerstone of agricultural activity in the European Union (EU) and in Latin America, this sector most often suffers from very low efficiency and effectiveness, sensitivity to weather, market disruptions and other external factors. Two different problems in knowledge sharing are present in this domain. First, the various interoperability regulations between the countries. Although some efforts are done to bypass this problem, like the EU-Mercosur signed in the summer of 2019, the different process semantics implemented in each region are a serious threat to the fulfillment of the process interoperability. Another problem is that in most of the cases, the knowledge transferred from generation to generation is paramount from a cultural point of view, but most of the time, it does not answer to the needs nor the requirements of the agri-food value chain. We aim at creating the core technology for a knowledge hub that integrates and aligns international regulations in agricultural activities, such as FAO's best practices, and possibly the last-born EU-Mercosur regulations with the local restrictions, such as national policies, allowing the small farmers to access, in an easy way, a wider market through the certification of the practices and products. In order to develop this core technology, we propose to deploy various methodologies and tools working on the domains of knowledge formalization, domain alignment and visualization. The domain of formal representation allows for the semantic alignment of rules and restrictions from different institutional regulation bodies. Simultaneously, we will propose a model for incoherence detection letting us to highlight contradictory regulations. Those knowledge atoms and constructs will be represented through some visualization information interfaces according to the users’ needs. The methods and tools that will be employed are at the same time the pillars from the multi relational data mining (MRDM), the artificial intelligence (AI), the knowledge formalization (KF) domains, but will extend the interoperability properties of those domains to become a new interesting and valuable tool for the presented problem. This abstract is issued from an accepted Stic-AmSud project that wad elaborated during the secondments of the RUC-APS project

Contributions to indexing and retrieval using Formal Concept Analysis

Un des premiers modèles d'indexation de documents qui utilise des termes comme descripteurs était une structure de treillis, cela une vingtaine d'années avant l'arrivée de l'analyse formelle de concepts (FCA pour "Formal Concept Analysis"), qui s'affirme maintenant comme un formalisme théorique important et solide pour l'analyse de données et la découverte de connaissances. Actuellement, la communauté en recherche d'information (RI) s'intéresse particulièrement à des techniques avancées pour la recherche des documents qui relèvent des probabilités et des statistiques. En parallèle, l'intérêt de la communauté FCA au développement de techniques qui font avancer l'état de l'art en RI tout en offrant des fonctionnalités sémantiques lui est toujours bien vivant. Dans cette thèse, nous présentons un ensemble de contributions sur ce que nous avons appelé les systèmes FCA de recherche d'information ("FCA-based IR systems''). Nous avons divisé nos contributions en deux parties, à savoir l'extraction et l'indexation. Pour la récupération, nous proposons une nouvelle technique qui exploite les relations sémantiques entre les descripteurs dans un corpus de documents. Pour l'indexation, nous proposons un nouveau modèle qui permet de mettre en oeuvre un modèle vectoriel d'indexation des documents s'appuyant sur un treillis de concepts (ou treillis de Galois). En outre, nous proposons un modèle perfectionné pour l'indexation hétérogène dans lequel nous combinons le modèle vectoriel et le modèle de recherche booléen. Finalement, nous présentons une technique de fouille de données inspiré de l'indexation des documents, à savoir un modèle d'énumération exhaustive des biclusters en utilisant la FCA. Le biclustering est une nouvelle technique d'analyse de données dans laquelle les objets sont liés via la similitude dans certains attributs de l'espace de description, et non pas par tous les attributs comme dans le "clustering'' standard. En traduisant ce problème en termes d'analyse formelle de concepts, nous pouvons exploiter l'algorithmique associée à la FCA pour développer une technique d'extraction de biclusters de valeurs similaires. Nous montrons le très bon comportement de notre technique, qui fonctionne mieux que les techniques actuelles de biclustering avec énumération exhaustiveOne of the first models ever to be considered as an index for documents using terms as descriptors, was a lattice structure, a couple of decades before the arrival of Formal Concept Analysis (FCA) as a solid theory for data mining and knowledge discovery.While the Information Retrieval (IR) community has shifted to more advanced techniques for document retrieval, like probabilistic and statistic paradigms, the interest of the FCA community on developing techniques that would improve the state-of-the-art in IR while providing relevance feedback and semantic based features, never decayed. In this thesis we present a set of contributions on what we call FCA-based IR systems. We have divided our contributions in two sets, namely retrieval and indexing. For retrieval, we propose a novel technique that exploits semantic relations among descriptors in a document corpus and a new concept lattice navigation strategy (called cousin concepts), enabling us to support classification-based reasoning to provide better results compared with state-of-the-art retrieval techniques. The basic notion in our strategy is supporting query modification using "term replacements'' using the lattice structure and semantic similarity. For indexing, we propose a new model that allows supporting the vector space model of retrieval using concept lattices. One of the main limitations of current FCA-based IR systems is related to the binary nature of the input data required for FCA to generate a concept lattice. We propose the use of pattern structures, an extension of FCA to deal with complex object descriptions, in order to support more advanced retrieval paradigms like the vector space model. In addition, we propose an advanced model for heterogeneous indexing through which we can combine the vector space model and the Boolean retrieval model. The main advantage of this approach is the ability of supporting indexing of convex regions in an arbitrary vectorial space built from a document collection. Finally, we move forward to a mining model associated with document indexing, namely exhaustive bicluster enumeration using FCA. Biclustering is an emerging data analysis technique in which objects are related by similarity under certain attributes of the description space, instead of the whole description space like in standard clustering. By translating this problem to the framework of FCA, we are able to exploit the robust machinery associated with the computation of concept lattices to provide an algorithm for mining biclusters based on similar values. We show how our technique performs better than current exhaustive enumeration biclustering techniques

Semantic querying of data guided by Formal Concept Analysis

Abstract. In this paper we present a novel approach to handle querying over a concept lattice of documents and annotations. We focus on the problem of “nonmatching documents”, which are those that, despite being semantically relevant to the user query, do not contain the query’s elements and hence cannot be retrieved by typical string matching approaches. In order to find these documents, we modify the initial user query using the concept lattice as a guide. We achieve this by identifying in the lattice a formal concept that represents the user query and then by finding potentially relevant concepts, identified as such through the proposed notion of cousin concepts. Finally, we use a concept semantic similarity metric to order and present retrieved documents. The main contribution of this paper is the introduction of the notion of cousin concepts of a given formal concept followed by a discussion on how this notion is useful for lattice-based information indexing and retrieval.

Eficacia de un programa piloto de mejoramiento del rendimiento académico en estudiantes de pregrado de la universidad de Chile

The purpose of this research was to determine the effectiveness of a pilot program for the improvement of academic performance of failing undergraduate students from the School of Chemical Science and Pharmacy at the University of Chile, who were studying subjects that were considered to be critical, i.e. General Chemistry I and II, Physics II, and Mathematics I and II, of the Chemistry, Chemistry and Pharmacy, Food and Biochemistry Engineering BA programs. For this purpose, a pre-test/post-test quasi-experimental design was used, with a non-equivalent control group. The sample was constituted by 154 students, divided into 4 groups: G1 (n=29) was the one that received the whole program; G2 (n=18) was the one that only received psychological support; G3 (n=61) was the group that exclusively received academic tutoring in the critical subjects; and finally G4 (n=46), the control group that did not received any type of support. The obtained results show that the program produced a significant improvement of the academic performance of the students, which was reflected in the passing grades of previously failed subjects in comparison to the ones obtained by the control group. This evidence is highly robust, especially in the case of the group of students that participated in the academic tutoring (G3), as well as those that complemented such tutoring with psychological support (G1).O objetivo desta pesquisa foi determinar a eficácia de um programa de melhoria do desempenho académico de alunos repetentes da Faculdade de Química e Ciências Farmacêuticas da Universidade do Chile, que estavam estudando assuntos considerados críticos, como sejam, Química Geral I e II, física II e Matemática I e II, das licenciaturas de Química, Química e Farmácia, Engenharia de Alimentos e Bioquímica. Para esta finalidade foi utilizada uma metodologia de design cuasi-experimental de pré-teste e pós-teste com grupo controle não equivalente. A amostra foi composta por 154 alunos, divididos em 4 grupos: G1 (n = 29), o grupo optou por seguir todo o programa, G2 (n = 18), que apenas recebeu apoio psicológico, G3 (n = 61) o grupo participou unicamente em tutoriais académicos sobre algumas questões críticas, G4 (n = 46) grupo controle, que não optou por qualquer apoio. Os resultados indicam que o programa promove uma melhoria significativa no desempenho académico, considerando-se as classificações de aprovação nas disciplinas anteriormente reprovadas em comparação com o grupo controle. Esta evidência é altamente robusta, para o grupo de estudantes que participaram do tutoriais académicos (G3), bem como para o grupo que complementou estes tutoriais, com apoio psicológico (G1).El propósito de esta investigación fue determinar la eficacia de un programa piloto de mejoramiento del rendimiento académico en estudiantes repitentes de la Facultad de Ciencias Químicas y Farmacéuticas de la Universidad de Chile, que cursaban asignaturas consideradas como críticas, a saber, Química General I y II, Física II y Matemáticas I y II, de las carreras de: Química, Química y Farmacia, Ingeniería en Alimentos y Bioquímica. Para este propósito se utilizó un diseño cuasi-experimental pretest-postest con grupo de control no equivalente. La muestra estuvo constituida por 154 estudiantes, divididos en 4 grupos: G1 (n=29), grupo que optó por seguir todo el programa, G2 (n=18), que recibió únicamente apoyo psicológico, G3 (n=61) grupo que participó exclusivamente de las tutorías académicas en las materias críticas, G4 (n=46) grupo control, que no optó a ningún tipo de apoyo. Los resultados obtenidos indican que el programa promueve una mejora significativa en el rendimiento académico, si se consideran las calificaciones de aprobación de las asignaturas reprobadas anteriormente, en comparación al grupo control. Esta evidencia es altamente robusta, para el grupo de estudiantes que participó de las tutorías académicas (G3), así como también, para el grupo que complementó dichas tutorías, con el apoyo psicológico (G1)

Combining knowledge discovery, ontologies, annotations, and semantic wikis

Semantic Wikis provide an original and operational infrastructure for efficiently combining semantic technologies and collaborative design activities. This text presents: a running example and its context (organization of the collections in a museum); concepts of wikis as a tool to allow computer supported cooperative work (cscw); concepts of semantic technologies and knowledge representation; concepts and examples of semantic wikis; anatomy of a semantic wiki (reasoning tools, storage, querying); and research directions. The evolution of the Web has demanded research efforts in several areas, from support to building applications [12] to the evaluation of user interfaces [18]; from investigating the application of information retrieval in general [21] to the building of efficient metasearch engines [27], web clustering engines [10], and machine learning techniques for web tex