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In the age of advanced information systems powering fast-paced knowledge economies that face global societal challenges, it is no longer adequate to express scholarly information - an essential resource for modern economies - primarily as... more
In the age of advanced information systems powering fast-paced knowledge economies that face global societal challenges, it is no longer adequate to express scholarly information - an essential resource for modern economies - primarily as article narratives in document form. Despite being a well-established tradition in scholarly communication, PDF-based text publishing is hindering scientific progress as it buries scholarly information into non-machine-readable formats. The key objective of SKG4EOSC is to improve science productivity through development and implementation of services for text and data conversion, and production, curation, and re-use of FAIR scholarly information. This will be achieved by (1) establishing the Open Research Knowledge Graph (ORKG, orkg.org), a service operated by the SKG4EOSC coordinator, as a Hub for access to FAIR scholarly information in the EOSC; (2) lifting to EOSC of numerous and heterogeneous domain-specific research infrastructures through the...
Background Eating disorders affect an increasing number of people. Social networks provide information that can help. Objective We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders... more
Background Eating disorders affect an increasing number of people. Social networks provide information that can help. Objective We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain. Methods We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model. Results A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer–based models had the best score among the machine learning a...
Developments in the context of Open, Big, and Linked Data have led to an enormous growth of structured data on the Web. To keep up with the pace of efficient consumption and management of the data at this rate, many data Management... more
Developments in the context of Open, Big, and Linked Data have led to an enormous growth of structured data on the Web. To keep up with the pace of efficient consumption and management of the data at this rate, many data Management solutions have been developed for specific tasks and applications. We present LITMUS, a framework for benchmarking data management solutions. LITMUS goes beyond classical storage benchmarking frameworks by allowing for analysing the performance of frameworks across query languages. In this position paper we present the conceptual architecture of LITMUS as well as the considerations that led to this architecture.
This report documents the program and the outcomes of Dagstuhl Seminar 17262 "Federated Semantic Data Management" (FSDM). The purpose of the seminar was to gather experts from the Semantic Web and Database communities, together... more
This report documents the program and the outcomes of Dagstuhl Seminar 17262 "Federated Semantic Data Management" (FSDM). The purpose of the seminar was to gather experts from the Semantic Web and Database communities, together with experts from application areas, to discuss in-depth open issues that have impeded FSDM approaches to be used on a large scale. The discussions were centered around the following four themes, each of which was the focus of a separate working group: i) graph data models, ii) federated query processing, iii) access control and privacy, and iv) use cases and applications. The main outcome of the seminar is a deeper understanding of the state of the art and of the open challenges of FSDM.
AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime, entailing concerns about potential human rights... more
AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multi-disciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful Machine Learning (ML) algorithms. If otherwise not specified, we use the general term bias to describe problems r...
RESUMEN Este trabajo describe una plataforma que permite automatizar el proceso de anotacion semantica sobre imagenes medicas, sin depender de la ontologia utilizada. Las anotaciones automaticas se realizan mediante: (a) un proceso de... more
RESUMEN Este trabajo describe una plataforma que permite automatizar el proceso de anotacion semantica sobre imagenes medicas, sin depender de la ontologia utilizada. Las anotaciones automaticas se realizan mediante: (a) un proceso de conversion de imagenes medicas DICOM (RDF-izacion) al formato RDF; (b) la integracion de diferentes ontologias biomedicas, a traves de la correspondencia de distintas ontologias biomedicas a los datos DICOM; haciendo la herramienta independiente de la ontologia; (c) la segmentacion y visualizacion de los datos anotados, se utiliza ademas para generar nuevas anotaciones de acuerdo al conocimiento del experto, permitiendo asi validar las anotaciones. Aplicando ademas tecnicas de recuperacion de imagenes basadas en su contenido visual, hace posible la recuperacion de imagenes medicas por similitud de caracteristicas inherentes a las imagenes. Esta plataforma esta siendo construida sobre una arquitectura distribuida, la cual permite optimizar la forma de c...
Tailoring personalized treatments demands the analysis of a patient’s characteristics, which may be scattered over a wide variety of sources. These features include family history, life habits, comorbidities, and potential treatment side... more
Tailoring personalized treatments demands the analysis of a patient’s characteristics, which may be scattered over a wide variety of sources. These features include family history, life habits, comorbidities, and potential treatment side effects. Moreover, the analysis of the services visited the most by a patient before a new diagnosis, as well as the type of requested tests, may uncover patterns that contribute to earlier disease detection and treatment effectiveness. Built on knowledge-driven ecosystems, we devise DE4LungCancer, a health data ecosystem of data sources for lung cancer. In this data ecosystem, knowledge extracted from heterogeneous sources, e.g., clinical records, scientific publications, and pharmacological data, is integrated into knowledge graphs. Ontologies describe the meaning of the combined data, and mapping rules enable the declarative definition of the transformation and integration processes. DE4LungCancer is assessed regarding the methods followed for da...
Social networks have become information dissemination channels, where announcements are posted frequently; they also serve as frameworks for debates in various areas (e.g., scientific, political, and social). In particular, in the health... more
Social networks have become information dissemination channels, where announcements are posted frequently; they also serve as frameworks for debates in various areas (e.g., scientific, political, and social). In particular, in the health area, social networks represent a channel to communicate and disseminate novel treatments’ success; they also allow ordinary people to express their concerns about a disease or disorder. The Artificial Intelligence (AI) community has developed analytical methods to uncover and predict patterns from posts that enable it to explain news about a particular topic, e.g., mental disorders expressed as eating disorders or depression. Albeit potentially rich while expressing an idea or concern, posts are presented as short texts, preventing, thus, AI models from accurately encoding these posts’ contextual knowledge. We propose a hybrid approach where knowledge encoded in community-maintained knowledge graphs (e.g., Wikidata) is combined with deep learning t...
Background Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mainstream diagnostic tests and screening tools, such as CAMCOG and MMSE, often fail to... more
Background Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mainstream diagnostic tests and screening tools, such as CAMCOG and MMSE, often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information in the patients’ Electronic Medical Records, they represent promising options to identify the presence and severity of dementia more precisely. Methods The methods presented in this paper aim to address two problems related to dementia: (a) Basic diagnosis: identifying the presence of dementia in individuals, and (b) Severity diagnosis: predicting the presence of dementia, as well as the severity of the disease. We formulate these two tasks as classification problems and address them using machine learning models based on random forests and decision tree, analysing structured clinical data f...
This booklet aims to tackle this problem by providing a practical introduction to the practice of peer reviewing. Although it mainly focuses on paper reviewing for scientific events in the domain of computer science and (business)... more
This booklet aims to tackle this problem by providing a practical introduction to the practice of peer reviewing. Although it mainly focuses on paper reviewing for scientific events in the domain of computer science and (business) informatics, many of the principles, tips, tricks, and examples are generalizable to journal reviewing and other scientific domains. Some of the principles and tips can also be applied when reviewing proposals for research projects or grants. In addition, many aspects of this booklet will also benefit authors of scientific papers (even outside computer science) as they will gain more insight into how papers are reviewed and hence where they have to pay attention to when writing their papers.
Path-based systems to guide scientists in the maze of biological data sources Sarah Cohen-Boulakia
This paper presents ARTEMIS, a control system for autonomous robots or software agents. ARTEMIS can create human-like artificial emotions during interactions with their environment. We describe the underlying mechanisms for this. The... more
This paper presents ARTEMIS, a control system for autonomous robots or software agents. ARTEMIS can create human-like artificial emotions during interactions with their environment. We describe the underlying mechanisms for this. The control system also captures its past artificial emotions. A specific interpretation of a knowledge graph, called an Agent Knowledge Graph, stores these artificial emotions. ARTEMIS then utilizes current and stored emotions to adapt decision making and planning processes. As proof of concept, we realize a concrete software agent based on the ARTEMIS control system. This software agent acts as a user assistant and executes their orders and instructions. The environment of this user assistant consists of several other autonomous agents that offer their services. The execution of a user’s orders requires interactions of the user assistant with these autonomous service agents. These interactions lead to the creation of artificial emotions within the user as...
In Computer Science, properties of formal theories that model real-world phenomena can be formally demonstrated using logic formal systems, e.g., given a proof of the best case complexity of a problem, or a demonstration of the soundness... more
In Computer Science, properties of formal theories that model real-world phenomena can be formally demonstrated using logic formal systems, e.g., given a proof of the best case complexity of a problem, or a demonstration of the soundness and completeness of a solution. Additionally, as in other Natural Sciences, characteristics of a theory can be empirically evaluated following the scientific method which provides procedures to systematically conduct experiments and to test hypotheses about these characteristics. Formally proven properties or empirically confirmed hypotheses can be accepted as accounting of known facts, while falsifiable statements that cannot be validated correspond to negative and inconclusive results. In this talk, we first discuss the different types of negative results that can be obtained during the formal and empirical validation of Computer Science approaches, e.g., contra-examples of theorems, intractability and undecidability of a problem, or statistically...
Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or... more
Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent trea...
Important questions about the scientific community, e.g., what authors are the experts in a certain field, or are actively engaged in international collaborations, can be answered using publicly available datasets. However, data required... more
Important questions about the scientific community, e.g., what authors are the experts in a certain field, or are actively engaged in international collaborations, can be answered using publicly available datasets. However, data required to answer such questions is often scattered over multiple isolated datasets. Recently, the Knowledge Graph (KG) concept has been identified as a means for interweaving heterogeneous datasets and enhancing answer completeness and soundness. We present a pipeline for creating high quality knowledge graphs that comprise data collected from multiple isolated structured datasets. As proof of concept, we illustrate the different steps in the construction of a knowledge graph in the domain of scholarly communication metadata (SCM-KG). Particularly, we demonstrate the benefits of exploiting semantic web technology to reconcile data about authors, papers, and conferences. We conducted an experimental study on an SCM-KG that merges scientific research metadata from the DBLP bibliographic source and the Microsoft Academic Graph. The observed results provide evidence that queries are processed more effectively on top of the SCM-KG than over the isolated datasets, while execution time is not negatively affected.
Industry 4.0 (I4.0) standards and standardization frameworks provide a unified way to describe smart factories. Standards specify the main components, systems, and processes inside a smart factory and the interaction among all of them.... more
Industry 4.0 (I4.0) standards and standardization frameworks provide a unified way to describe smart factories. Standards specify the main components, systems, and processes inside a smart factory and the interaction among all of them. Furthermore, standardization frameworks classify standards according to their functions into layers and dimensions. Albeit informative, frameworks can categorize similar standards differently. As a result, interoperability conflicts are generated whenever smart factories are described with miss-classified standards. Approaches like ontologies and knowledge graphs enable the integration of standards and frameworks in a structured way. They also encode the meaning of the standards, known relations among them, as well as their classification according to existing frameworks. This structured modeling of the I4.0 landscape using a graph data model provides the basis for graph-based analytical methods to uncover alignments among standards. This paper contri...

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