146 research outputs found
A Semantic Grid Oriented to E-Tourism
With increasing complexity of tourism business models and tasks, there is a
clear need of the next generation e-Tourism infrastructure to support flexible
automation, integration, computation, storage, and collaboration. Currently
several enabling technologies such as semantic Web, Web service, agent and grid
computing have been applied in the different e-Tourism applications, however
there is no a unified framework to be able to integrate all of them. So this
paper presents a promising e-Tourism framework based on emerging semantic grid,
in which a number of key design issues are discussed including architecture,
ontologies structure, semantic reconciliation, service and resource discovery,
role based authorization and intelligent agent. The paper finally provides the
implementation of the framework.Comment: 12 PAGES, 7 Figure
Computational Controversy
Climate change, vaccination, abortion, Trump: Many topics are surrounded by
fierce controversies. The nature of such heated debates and their elements have
been studied extensively in the social science literature. More recently,
various computational approaches to controversy analysis have appeared, using
new data sources such as Wikipedia, which help us now better understand these
phenomena. However, compared to what social sciences have discovered about such
debates, the existing computational approaches mostly focus on just a few of
the many important aspects around the concept of controversies. In order to
link the two strands, we provide and evaluate here a controversy model that is
both, rooted in the findings of the social science literature and at the same
time strongly linked to computational methods. We show how this model can lead
to computational controversy analytics that have full coverage over all the
crucial aspects that make up a controversy.Comment: In Proceedings of the 9th International Conference on Social
Informatics (SocInfo) 201
Towards semantic web mining
Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on the other hand, for building up the Semantic Web. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable
User-Friendly MES Interfaces:Recommendations for an AI-Based Chatbot Assistance in Industry 4.0 Shop Floors
The purpose of this paper is to study an Industry 4.0 scenario of ‘technical assistance’ and use manufacturing execution systems (MES) to address the need for easy information extraction on the shop floor. We identify specific requirements for a user-friendly MES interface to develop (and test) an approach for technical assistance and introduce a chatbot with a prediction system as an interface layer for MES. The chatbot is aimed at production coordination by assisting the shop floor workforce and learn from their inputs, thus acting as an intelligent assistant. We programmed a prototype chatbot as a proof of concept, where the new interface layer provided live updates related to production in natural language and added predictive power to MES. The results indicate that the chatbot interface for MES is beneficial to the shop floor workforce and provides easy information extraction, compared to the traditional search techniques. The paper contributes to the manufacturing information systems field and demonstrates a human-AI collaboration system in a factory. In particular, this paper recommends the manner in which MES based technical assistance systems can be developed for the purpose of easy information retrieval
Information Flow within Relational Multi-context Systems
Publication in progressMulti-context systems (MCSs) are an important framework for heterogeneous combinations of systems within the Semantic Web.
In this paper, we propose generic constructions to achieve specific forms of interaction in a principled way, and systematize some useful techniques to work with ontologies within an MCS.
All these mechanisms are presented in the form of general-purpose design patterns.
Their study also suggests new ways in which this framework can be further extended.This work was partially supported by Fundação para a Ciência e Tecnologia under contract PEst- OE/EEI/UI0434/2011
A proposal for annotation, semantic similarity and classification of textual documents
The original publication is available at www.springerlink.comInternational audienceIn this paper, we present an approach for classifying documents based on the notion of a semantic similarity and the effective representation of the content of the documents. The content of a document is annotated and the resulting annotation is represented by a labeled tree whose nodes and edges are represented by concepts lying within a domain ontology. A reasoning process may be carried out on annotation trees, allowing the comparison of documents between each others, for classification or information retrieval purposes. An algorithm for classifying documents with respect to semantic similarity and a discussion conclude the paper
Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support
Computer Vision, and hence Artificial Intelligence-based extraction of
information from images, has increasingly received attention over the last
years, for instance in medical diagnostics. While the algorithms' complexity is
a reason for their increased performance, it also leads to the "black box"
problem, consequently decreasing trust towards AI. In this regard, "Explainable
Artificial Intelligence" (XAI) allows to open that black box and to improve the
degree of AI transparency. In this paper, we first discuss the theoretical
impact of explainability on trust towards AI, followed by showcasing how the
usage of XAI in a health-related setting can look like. More specifically, we
show how XAI can be applied to understand why Computer Vision, based on deep
learning, did or did not detect a disease (malaria) on image data (thin blood
smear slide images). Furthermore, we investigate, how XAI can be used to
compare the detection strategy of two different deep learning models often used
for Computer Vision: Convolutional Neural Network and Multi-Layer Perceptron.
Our empirical results show that i) the AI sometimes used questionable or
irrelevant data features of an image to detect malaria (even if correctly
predicted), and ii) that there may be significant discrepancies in how
different deep learning models explain the same prediction. Our theoretical
discussion highlights that XAI can support trust in Computer Vision systems,
and AI systems in general, especially through an increased understandability
and predictability
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