Mejorando la calidad de los scatterplots con sugerencias automáticas durante su creación: un caso de estudio de visualización basada en semántica

The process of creating a visualization is a very complex exploration activity and, even for skilled users, it can be difficult to produce an effective visualization. Since the result of such a process depends on the user’s decisions along it, one way to improve the probability of achieving a useful outcome is to assist the user in the configuration and preparation of the visualization. Our proposal consists in live suggestions on how to improve the visualization. These live suggestions are based on the user decisions, and are achieved by the integration of semantic reasoning into the visualization process. In this paper, we present a case study for scatterplots visualization that combines ontologies with a semantic reasoner and helps the user in the generation of an effective visualization.El proceso de creación de una visualización es una actividad de exploración muy compleja e, incluso para usuarios expertos, puede ser difícil obtener como resultado una visualización efectiva. Dado que este resultado depende de las decisiones que el usuario toma a lo largo del proceso de visualización, una forma de mejorar la probabilidad de lograr un buen resultado es ayudar al usuario en la configuración y preparación de la visualización. Nuestra propuesta consiste en ofrecer sugerencias sobre cómo mejorar la visualización mientras el usuario la está creando. Estas sugerencias se basan en las decisiones que el usuario tomó y se logran mediante la integración del razonamiento semántico al proceso de visualización. En este artículo, presentamos un caso de estudio para la visualización de scatterplots (gráficos de dispersión), en el cual se muestra como se asiste al usuario, mediante la combinación de ontologías con un razonador semántico, para logar una visualización efectiva.Facultad de Informátic

Semantic Web and Interactive Knowledge Graphs as an Educational Technology

Technologies of knowledge representation, inductive reasoning, and semantic annotation methods are considered in relation to knowledge graphs that are focused on the domain of nuclear physics and nuclear power engineering. Interactive visual navigation and inductive reasoning in knowledge graphs are performed using special search widgets and an intelligent RDF browser. As a toolkit for ontologies refinement and enrichment, a software agent for the context-sensitive searching for new knowledge in the WWW is presented. In order to evaluate the measure of compliance of the found content with respect to a specific domain, the binary Pareto relation and Levenshtein metrics are used. The proposed semantic annotation methods allow the knowledge engineer to calculate the measure of the proximity of an arbitrary network resource in relation to classes and objects of specific knowledge graphs. Operations with remote semantic repositories are implemented on cloud platforms using SPARQL queries and RESTful services. The proposed software solutions are based on cloud computing using DBaaS and PaaS service models to ensure scalability of data warehouses and network services. Examples of using the proposed technologies and software are given

Mejorando la calidad de los scatterplots con sugerencias automáticas durante su creación: un caso de estudio de visualización basada en semántica

The process of creating a visualization is a very complex exploration activity and, even for skilled users, it can be difficult to produce an effective visualization. Since the result of such a process depends on the user’s decisions along it, one way to improve the probability of achieving a useful outcome is to assist the user in the configuration and preparation of the visualization. Our proposal consists in live suggestions on how to improve the visualization. These live suggestions are based on the user decisions, and are achieved by the integration of semantic reasoning into the visualization process. In this paper, we present a case study for scatterplots visualization that combines ontologies with a semantic reasoner and helps the user in the generation of an effective visualization.El proceso de creación de una visualización es una actividad de exploración muy compleja e, incluso para usuarios expertos, puede ser difícil obtener como resultado una visualización efectiva. Dado que este resultado depende de las decisiones que el usuario toma a lo largo del proceso de visualización, una forma de mejorar la probabilidad de lograr un buen resultado es ayudar al usuario en la configuración y preparación de la visualización. Nuestra propuesta consiste en ofrecer sugerencias sobre cómo mejorar la visualización mientras el usuario la está creando. Estas sugerencias se basan en las decisiones que el usuario tomó y se logran mediante la integración del razonamiento semántico al proceso de visualización. En este artículo, presentamos un caso de estudio para la visualización de scatterplots (gráficos de dispersión), en el cual se muestra como se asiste al usuario, mediante la combinación de ontologías con un razonador semántico, para logar una visualización efectiva.Facultad de Informátic

Evaluating Knowledge Anchors in Data Graphs against Basic Level Objects

The growing number of available data graphs in the form of RDF Linked Da-ta enables the development of semantic exploration applications in many domains. Often, the users are not domain experts and are therefore unaware of the complex knowledge structures represented in the data graphs they in-teract with. This hinders users’ experience and effectiveness. Our research concerns intelligent support to facilitate the exploration of data graphs by us-ers who are not domain experts. We propose a new navigation support ap-proach underpinned by the subsumption theory of meaningful learning, which postulates that new concepts are grasped by starting from familiar concepts which serve as knowledge anchors from where links to new knowledge are made. Our earlier work has developed several metrics and the corresponding algorithms for identifying knowledge anchors in data graphs. In this paper, we assess the performance of these algorithms by considering the user perspective and application context. The paper address the challenge of aligning basic level objects that represent familiar concepts in human cog-nitive structures with automatically derived knowledge anchors in data graphs. We present a systematic approach that adapts experimental methods from Cognitive Science to derive basic level objects underpinned by a data graph. This is used to evaluate knowledge anchors in data graphs in two ap-plication domains - semantic browsing (Music) and semantic search (Ca-reers). The evaluation validates the algorithms, which enables their adoption over different domains and application contexts

Community detection applied on big linked data

The Linked Open Data (LOD) Cloud has more than tripled its sources in just six years (from 295 sources in 2011 to 1163 datasets in 2017). The actual Web of Data contains more then 150 Billions of triples. We are assisting at a staggering growth in the production and consumption of LOD and the generation of increasingly large datasets. In this scenario, providing researchers, domain experts, but also businessmen and citizens with visual representations and intuitive interactions can significantly aid the exploration and understanding of the domains and knowledge represented by Linked Data. Various tools and web applications have been developed to enable the navigation, and browsing of the Web of Data. However, these tools lack in producing high level representations for large datasets, and in supporting users in the exploration and querying of these big sources. Following this trend, we devised a new method and a tool called H-BOLD (High level visualizations on Big Open Linked Data). H-BOLD enables the exploratory search and multilevel analysis of Linked Open Data. It offers different levels of abstraction on Big Linked Data. Through the user interaction and the dynamic adaptation of the graph representing the dataset, it will be possible to perform an effective exploration of the dataset, starting from a set of few classes and adding new ones. Performance and portability of H-BOLD have been evaluated on the SPARQL endpoint listed on SPARQL ENDPOINT STATUS. The effectiveness of H-BOLD as a visualization tool is described through a user study

A Conceptual Modelling Approach to Visualising Linked Data

Increasing numbers of Linked Open Datasets are being published, and many possible data visualisations may be appropriate for a user's given exploration or analysis task over a dataset. Users may therefore find it difficult to identify visualisations that meet their data exploration or analyses needs. We propose an approach that creates conceptual models of groups of commonly used data visualisations, which can be used to analyse the data and users' queries so as to automatically generate recommendations of possible visualisations. To our knowledge, this is the first work to propose a conceptual modelling approach to recommending visualisations for Linked Data