Investigating the use of semantic technologies in spatial mapping applications

Semantic Web Technologies are ideally suited to build context-aware information retrieval applications. However, the geospatial aspect of context awareness presents unique challenges such as the semantic modelling of geographical references for efficient handling of spatial queries, the reconciliation of the heterogeneity at the semantic and geo-representation levels, maintaining the quality of service and scalability of communicating, and the efficient rendering of the spatial queries' results. In this paper, we describe the modelling decisions taken to solve these challenges by analysing our implementation of an intelligent planning and recommendation tool that provides location-aware advice for a specific application domain. This paper contributes to the methodology of integrating heterogeneous geo-referenced data into semantic knowledgebases, and also proposes mechanisms for efficient spatial interrogation of the semantic knowledgebase and optimising the rendering of the dynamically retrieved context-relevant information on a web frontend

Approaches to visualising linked data: a survey

The uptake and consumption of Linked Data is currently restricted almost entirely to the Semantic Web community. While the utility of Linked Data to non-tech savvy web users is evident, the lack of technical knowledge and an understanding of the intricacies of the semantic technology stack limit such users in their ability to interpret and make use of the Web of Data. A key solution in overcoming this hurdle is to visualise Linked Data in a coherent and legible manner, allowing non-domain and non-technical audiences to obtain a good understanding of its structure, and therefore implicitly compose queries, identify links between resources and intuitively discover new pieces of information. In this paper we describe key requirements which the visualisation of Linked Data must fulfil in order to lower the technical barrier and make the Web of Data accessible for all. We provide an extensive survey of current efforts in the Semantic Web community with respect to our requirements, and identify the potential for visual support to lead to more effective, intuitive interaction of the end user with Linked Data. We conclude with the conclusions drawn from our survey and analysis, and present proposals for advancing current Linked Data visualisation efforts

BESDUI: A Benchmark for End-User Structured Data User Interfaces

The Semantic Web Community has invested significant research efforts in developing systems for Semantic Web search and exploration. But while it has been easy to assess the systems’ computational efficiency, it has been much harder to assess how well different semantic systems’ user interfaces help their users. In this article, we propose and demonstrate the use of a benchmark for evaluating such user interfaces, similar to the TREC benchmark for evaluating traditional search engines. Our benchmark includes a set of typical user tasks and a well-defined procedure for assigning a measure of performance on those tasks to a semantic system. We demonstrate its application to two such system, Virtuoso and Rhizomer. We intend for this work to initiate a community conversation that will lead to a generally accepted framework for comparing systems and for measuring, and thus encouraging, progress towards better semantic search and exploration tools

Exploring user and system requirements of linked data visualization through a visual dashboard approach

One of the open problems in SemanticWeb research is which tools should be provided to users to explore linked data. This is even more urgent now that massive amount of linked data is being released by governments worldwide. The development of single dedicated visualization applications is increasing, but the problem of exploring unknown linked data to gain a good understanding of what is contained is still open. An effective generic solution must take into account the user’s point of view, their tasks and interaction, as well as the system’s capabilities and the technical constraints the technology imposes. This paper is a first step in understanding the implications of both, user and system by evaluating our dashboard-based approach. Though we observe a high user acceptance of the dashboard approach, our paper also highlights technical challenges arising out of complexities involving current infrastructure that need to be addressed while visualising linked data. In light of the findings, guidelines for the development of linked data visualization (and manipulation) are provided

User interfaces supporting entity search for linked data

One of the main goals of semantic search is to retrieve and connect information related to queries, offering users rich structured information about a topic instead of a set of documents relevant to the topic. Previous work reports that searching for information about individual entities such as persons, places and organisations is the most common form of Web search. Since the Semantic Web was first proposed, the amount of structured data on the Web has increased dramatically. This is particularly the case for what is known as Linked Data, information that has been published using Semantic Web standards such as RDF and OWL. Such structured data opens up new possibilities for improving entity search on the Web, integrating facts from independent sources, and presenting users with contextually-rich information about entities. This research focuses on entity search of Linked Data in terms of three different forms of search: structured queries, where users can use the SPARQL query language for manipulating data sources; exploratory search, where users can browse from one entity to another; and focused search, where users can input an entity query as a free text keyword search. We undertake a comparative study between two distinct information architectures for structured querying to manipulate Linked Data over the Web. Specifically, we evaluate some of the main operators in SPARQL using several datasets of Linked Data. We introduce a framework of five criteria to evaluate 15 current state-of-the-art semantic tools available for exploratory search of Linked Data, in order to establish how well these browsers make available the benefits of Linked Data and entity search for human users. We also use the criteria to determine the browsers that are best suited to entity exploration. Further, we propose a new model, the Attribute Importance Model, for entity-aggregated search, with the purpose of improving user experience when finding information about entities. The model develops three techniques: (1) presenting entity type-based query suggestions; (2) clustering aggregated attributes; and (3) ranking attributes based on their importance to a given query. Together these constitute a model for developing more informative views and enhancing users’ understanding of entity descriptions on the Web. We then use our model to provide an interactive approach, with the Information Visualisation toolkit InfoVis, that enables users to visualise entity clusters generated by our Attribute Importance Model. Thus this thesis addresses two challenges of searching Linked Data. The first challenge concerns the specific issue of information resolution during the search: the reduction of query ambiguity and redundant results that contain irrelevant descriptions when searching for information about an entity. The second challenge concerns the more general problem of technical complexity, and addresses to the limited adoption of Linked Data that we ascribe to the lack of understanding of Semantic Web technologies and data structures among general users. These technologies pose new design problems for human interaction such as overloading data, navigation styles, and browsing mechanisms. The Attribute Importance Model addresses both these challenges

Initiating organizational memories using ontology network analysis

One of the important problems in organizational memories is their initial set-up. It is difficult to choose the right information to include in an organizational memory, and the right information is also a prerequisite for maximizing the uptake and relevance of the memory content. To tackle this problem, most developers adopt heavy-weight solutions and rely on a faithful continuous interaction with users to create and improve its content. In this paper, we explore the use of an automatic, light-weight solution, drawn from the underlying ingredients of an organizational memory: ontologies. We have developed an ontology-based network analysis method which we applied to tackle the problem of identifying communities of practice in an organization. We use ontology-based network analysis as a means to provide content automatically for the initial set up of an organizational memory

Application of Semantics to Solve Problems in Life Sciences

Fecha de lectura de Tesis: 10 de diciembre de 2018La cantidad de información que se genera en la Web se ha incrementado en los últimos años. La mayor parte de esta información se encuentra accesible en texto, siendo el ser humano el principal usuario de la Web. Sin embargo, a pesar de todos los avances producidos en el área del procesamiento del lenguaje natural, los ordenadores tienen problemas para procesar esta información textual. En este cotexto, existen dominios de aplicación en los que se están publicando grandes cantidades de información disponible como datos estructurados como en el área de las Ciencias de la Vida. El análisis de estos datos es de vital importancia no sólo para el avance de la ciencia, sino para producir avances en el ámbito de la salud. Sin embargo, estos datos están localizados en diferentes repositorios y almacenados en diferentes formatos que hacen difícil su integración. En este contexto, el paradigma de los Datos Vinculados como una tecnología que incluye la aplicación de algunos estándares propuestos por la comunidad W3C tales como HTTP URIs, los estándares RDF y OWL. Haciendo uso de esta tecnología, se ha desarrollado esta tesis doctoral basada en cubrir los siguientes objetivos principales: 1) promover el uso de los datos vinculados por parte de la comunidad de usuarios del ámbito de las Ciencias de la Vida 2) facilitar el diseño de consultas SPARQL mediante el descubrimiento del modelo subyacente en los repositorios RDF 3) crear un entorno colaborativo que facilite el consumo de Datos Vinculados por usuarios finales, 4) desarrollar un algoritmo que, de forma automática, permita descubrir el modelo semántico en OWL de un repositorio RDF, 5) desarrollar una representación en OWL de ICD-10-CM llamada Dione que ofrezca una metodología automática para la clasificación de enfermedades de pacientes y su posterior validación haciendo uso de un razonador OWL