Lazaros Ioannidis

NLP Data Cleansing Based on Linguistic Ontology Constraints

Linked Data comprises of an unprecedented volume of structured data on the Web and is adopted from an increasing number of domains. However, the varying quality of published data forms a barrier for further adoption, especially for Linked Data consumers. In this paper, we extend a previously developed methodology of Linked Data quality assessment, which is inspired by test-driven software development. Specifically, we enrich it with ontological support and different levels of result reporting and describe how the method is applied in the Natural Language Processing (NLP) area. NLP is – compared to other domains, such as biology – a late Linked Data adopter. However, it has seen a steep rise of activity in the creation of data and ontologies. NLP data quality assessment has become an important need for NLP datasets. In our study, we analysed 11 datasets using the lemon and NIF vocabularies in 277 test cases and point out common quality issues.

Virtual Patients on the Semantic Web: A Proof-of-Application Study

Background Virtual patients are interactive computer simulations that are increasingly used as learning activities in modern health care education, especially in teaching clinical decision making. A key challenge is how to retrieve and repurpose virtual patients as unique types of educational resources between different platforms because of the lack of standardized content-retrieving and repurposing mechanisms. Semantic Web technologies provide the capability, through structured information, for easy retrieval, reuse, repurposing, and exchange of virtual patients between different systems. Objective An attempt to address this challenge has been made through the mEducator Best Practice Network, which provisioned frameworks for the discovery, retrieval, sharing, and reuse of medical educational resources. We have extended the OpenLabyrinth virtual patient authoring and deployment platform to facilitate the repurposing and retrieval of existing virtual patient material. Methods A standalone Web distribution and Web interface, which contains an extension for the OpenLabyrinth virtual patient authoring system, was implemented. This extension was designed to semantically annotate virtual patients to facilitate intelligent searches, complex queries, and easy exchange between institutions. The OpenLabyrinth extension enables OpenLabyrinth authors to integrate and share virtual patient case metadata within the mEducator3.0 network. Evaluation included 3 successive steps: (1) expert reviews; (2) evaluation of the ability of health care professionals and medical students to create, share, and exchange virtual patients through specific scenarios in extended OpenLabyrinth (OLabX); and (3) evaluation of the repurposed learning objects that emerged from the procedure. Results We evaluated 30 repurposed virtual patient cases. The evaluation, with a total of 98 participants, demonstrated the system’s main strength: the core repurposing capacity. The extensive metadata schema presentation facilitated user exploration and filtering of resources. Usability weaknesses were primarily related to standard computer applications’ ease of use provisions. Most evaluators provided positive feedback regarding educational experiences on both content and system usability. Evaluation results replicated across several independent evaluation events. Conclusions The OpenLabyrinth extension, as part of the semantic mEducator3.0 approach, is a virtual patient sharing approach that builds on a collection of Semantic Web services and federates existing sources of clinical and educational data. It is an effective sharing tool for virtual patients and has been merged into the next version of the app (OpenLabyrinth 3.3). Such tool extensions may enhance the medical education arsenal with capacities of creating simulation/game-based learning episodes, massive open online courses, curricular transformations, and a future robust infrastructure for enabling mobile learning.

mEducator 3.0: Combining Semantic and Social Web Approaches in Sharing and Retrieving Medical Education Resources

Sharing of educational resources over the web has been a key development for both educators and learners in recent years. Pivotal roles in these developments have been played by following principles of the social/collaborative web, and more recently by exploiting advances in the semantic web front. In this paper an architecture that allows the sharing and retrieving of educational resources is proposed. In contrast to similar past attempts, this is now done without the need of copying or storing metadata in a centralised dataset/repository. Linked Data principles were used for exploring the metadata, while collaborative techniques were used in the systems that take advance of this architecture in order to enhance the end user experience, thereby offering a contemporary way to achieve resource adaptation and personalisation.

Implementing Collaborative e-Learning Techniques in Collaborative Virtual Environments: The Case of Second Life

This paper reviews and compares the most promising collaborative virtual environment platforms, which have been used or proposed for supporting educational activities in terms of their potential to support collaborative e-learning. The most promising environment according to the results of this review is Second Life. Second Life is further examined by validating the platforms features, philosophy and policies against some basic design principles for collaborative virtual learning environments in order to better assess its design adequacy for online learning. Furthermore, this paper presents the features that we have implemented within the Second Life platform, in order to facilitate the jigsaw collaborative e-learning scenario. Finally, we present a case study concerning the evaluation of Second Life by undergraduate students in order to assess its potential to support collaborative e-learning techniques.

The code lists case: Identifying and linking the key parts of fiscal datasets

Today, there is a growing need within citizens, journalists and many public bodies to explore and search for valuable information and knowledge in budget data, that are widely published by governments and municipalities across Europe. A significant element of these fiscal datasets are code lists, which serve not only for the coding and the simplicity of representation of budget concepts, but can also act as linking entities between budget datasets of different countries, bodies, years and structure, in order to facilitate their comparison. To this end, we made an extensive survey of code lists that refer or are related to fiscal or other concepts that are included in budget datasets, recording totally 239 international and national classifications. We selected some of these classifications to represent them semantically with SKOS and then we examined their linking possibilities. While manual and automated methods are insufficient for linking large code lists, tools using semi-automated methods, like Alignment seem to be more suitable for this specific task.

Rudolf: An HTTP API for exposing semantically represented fiscal OLAP cubes

OpenSpending is the worlds largest repository of open fiscal datasets. As of now it only supported loading datasets from CSV files, leaving out more heavily-structured formats like RDF. Rudolf, an RDF backend has been developed to expose Data Cube RDF triples and OLAP operations in JSON format through an OpenSpending-compatible HTTP API. This API can be directly utilized by the OpenSpending Viewer application to transparently offer aggregate visualizations for RDF fiscal datasets out of the box.

OSCase: Data Schemes, Architecture and Implementation details of Virtual Patient repurposing in Multi User Virtual Environments.

Experiential game based learning has proven effective at engaging and enabling learners especially in medical education where the volume of the curriculum is as severe as its criticality. A mature and promising ICT tool for medical education is the Virtual Patient (VP). Web-based virtual patients have been established for quite some time, while efforts have been made to create content in multi user virtual environments (MUVEs). What is still missing is a streamlined method for transferring VPs from Web-based authoring and deployment platforms to MUVEs like OpenSim. This work describes the existing implementation context for repurposing VPs in the OS MUVE. Then it focuses on a novel OpenSim Case Datascheme and a supporting framework that facilitates this streamlined transfer according to previous repurposing strategies and efforts. Finally the future directions and the place of this work in the wider context of contemporary Virtual Learning Environments are explored.