429 research outputs found
Recommending on graphs: a comprehensive review from a data perspective
Recent advances in graph-based learning approaches have demonstrated their
effectiveness in modelling users' preferences and items' characteristics for
Recommender Systems (RSS). Most of the data in RSS can be organized into graphs
where various objects (e.g., users, items, and attributes) are explicitly or
implicitly connected and influence each other via various relations. Such a
graph-based organization brings benefits to exploiting potential properties in
graph learning (e.g., random walk and network embedding) techniques to enrich
the representations of the user and item nodes, which is an essential factor
for successful recommendations. In this paper, we provide a comprehensive
survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we
start from a data-driven perspective to systematically categorize various
graphs in GLRSs and analyze their characteristics. Then, we discuss the
state-of-the-art frameworks with a focus on the graph learning module and how
they address practical recommendation challenges such as scalability, fairness,
diversity, explainability and so on. Finally, we share some potential research
directions in this rapidly growing area.Comment: Accepted by UMUA
Vers l’élaboration d’un système d’organisation des connaissances en allergologie : l’analyse des documents et des pratiques informationnelles des acteurs
The aim of the present thesis, funded by the Occitanie Region (2019-2023), is to construct a knowledge organization system (KOS) for the Allergy Unit of the Montpellier University Hospital (France), to represent and organize the complexity of allergy knowledge. Currently, there is no KOS that might be used by allergy professionals and researchers for their information processing and seeking activities. Allergy knowledge, produced by different actors, is abundant and heterogeneous, and keeps developing in parallel with the massification of health data. To allow and provide access to this knowledge, it is crucial to identify and characterize it, first by focusing on what might be useful for professionals’ daily activities and then by structuring it in a system of organization and documentary representation possibly linking the different ways of representing knowledge by different actors in this domain. Therefore, we propose a KOS in allergy, reached through a contextualized approach that relies, on one hand, on the analysis of the context of use of specialized knowledge, by the study of the informational practices of professionals who seek, produce, and mobilize knowledge in the domain; and on the other hand, on the analysis of a corpus of documents that professionals use in their daily activities. Through our work, we perform an epistemological reflection within the Information & Communication Sciences, by showing how the analysis of informational practices contributes to the construction of a KOS for a medical domain. Moreover, we try to answer a methodological question, linked with the development of a KOS in allergy, and evaluate if our conception method, oriented by a contextualized approach, allows to propose a useful KOS for the practices of the actors in this domain
The Imitation Game: Detecting Human and AI-Generated Texts in the Era of Large Language Models
The potential of artificial intelligence (AI)-based large language models
(LLMs) holds considerable promise in revolutionizing education, research, and
practice. However, distinguishing between human-written and AI-generated text
has become a significant task. This paper presents a comparative study,
introducing a novel dataset of human-written and LLM-generated texts in
different genres: essays, stories, poetry, and Python code. We employ several
machine learning models to classify the texts. Results demonstrate the efficacy
of these models in discerning between human and AI-generated text, despite the
dataset's limited sample size. However, the task becomes more challenging when
classifying GPT-generated text, particularly in story writing. The results
indicate that the models exhibit superior performance in binary classification
tasks, such as distinguishing human-generated text from a specific LLM,
compared to the more complex multiclass tasks that involve discerning among
human-generated and multiple LLMs. Our findings provide insightful implications
for AI text detection while our dataset paves the way for future research in
this evolving area
The Role of Vocabularies in the Age of Data: The Question of Research Data
Objective: This paper discusses the role of vocabularies in addressing the issues associated
with Big Data.
Methodology: The materials used are definitions of Big Data found in literature, standards,
and technologies used in the Semantic Web and Linked Open Data, as well as the use case
of a research dataset; we use the conceptual bases of semiotics and ontology to analyze the
role of vocabularies in knowledge organization (KO) in assigning subjects to documents as a
special, limited, use case that may be expanded within such context.
Results: We develop and expand the conception of data as an artificial, intentional
construction that represents a property of an entity within a specific domain and serving as
the essential component of the Big Data. We present a comprehensive conceptualization of
semantic expressivity and use it to classify the different vocabularies. We suggest and
specify features to vocabularies that may be used within the context of the Semantic Web
and the Linked Open Data to assign machine-processable semantics to Big Data. We
identify computational ontologies as a type of knowledge organization system with a higher
degree of semantic expressivity. It is suggested that such themes should be incorporated into
professional qualifications in KO
Digital Twins in Industry
Digital Twins in Industry is a compilation of works by authors with specific emphasis on industrial applications. Much of the research on digital twins has been conducted by the academia in both theoretical considerations and laboratory-based prototypes. Industry, while taking the lead on larger scale implementations of Digital Twins (DT) using sophisticated software, is concentrating on dedicated solutions that are not within the reach of the average-sized industries. This book covers 11 chapters of various implementations of DT. It provides an insight for companies who are contemplating the adaption of the DT technology, as well as researchers and senior students in exploring the potential of DT and its associated technologies
ECLAP 2012 Conference on Information Technologies for Performing Arts, Media Access and Entertainment
It has been a long history of Information Technology innovations within the Cultural Heritage areas. The Performing arts has also been enforced with a number of new innovations which unveil a range of synergies and possibilities. Most of the technologies and innovations produced for digital libraries, media entertainment and education can be exploited in the field of performing arts, with adaptation and repurposing. Performing arts offer many interesting challenges and opportunities for research and innovations and exploitation of cutting edge research results from interdisciplinary areas. For these reasons, the ECLAP 2012 can be regarded as a continuation of past conferences such as AXMEDIS and WEDELMUSIC (both pressed by IEEE and FUP). ECLAP is an European Commission project to create a social network and media access service for performing arts institutions in Europe, to create the e-library of performing arts, exploiting innovative solutions coming from the ICT
Knowledge Representation in Engineering 4.0
This dissertation was developed in the context of the BMBF and EU/ECSEL funded
projects GENIAL! and Arrowhead Tools. In these projects the chair examines methods
of specifications and cooperations in the automotive value chain from OEM-Tier1-Tier2.
Goal of the projects is to improve communication and collaborative planning, especially
in early development stages. Besides SysML, the use of agreed vocabularies and on-
tologies for modeling requirements, overall context, variants, and many other items, is
targeted. This thesis proposes a web database, where data from the collaborative requirements elicitation is combined with an ontology-based approach that uses reasoning
capabilities.
For this purpose, state-of-the-art ontologies have been investigated and integrated that
entail domains like hardware/software, roadmapping, IoT, context, innovation and oth-
ers. New ontologies have been designed like a HW / SW allocation ontology and a
domain-specific "eFuse ontology" as well as some prototypes. The result is a modular
ontology suite and the GENIAL! Basic Ontology that allows us to model automotive
and microelectronic functions, components, properties and dependencies based on the
ISO26262 standard among these elements. Furthermore, context knowledge that influences design decisions such as future trends in legislation, society, environment, etc. is
included. These knowledge bases are integrated in a novel tool that allows for collabo-
rative innovation planning and requirements communication along the automotive value
chain. To start off the work of the project, an architecture and prototype tool was developed. Designing ontologies and knowing how to use them proved to be a non-trivial
task, requiring a lot of context and background knowledge. Some of this background
knowledge has been selected for presentation and was utilized either in designing models
or for later immersion. Examples are basic foundations like design guidelines for ontologies, ontology categories and a continuum of expressiveness of languages and advanced
content like multi-level theory, foundational ontologies and reasoning.
Finally, at the end, we demonstrate the overall framework, and show the ontology with
reasoning, database and APPEL/SysMD (AGILA ProPErty and Dependency Descrip-
tion Language / System MarkDown) and constraints of the hardware / software knowledge base. There, by example, we explore and solve roadmap constraints that are coupled
with a car model through a constraint solver.Diese Dissertation wurde im Kontext des von BMBF und EU / ECSEL gefördertem
Projektes GENIAL! und Arrowhead Tools entwickelt. In diesen Projekten untersucht der
Lehrstuhl Methoden zur Spezifikationen und Kooperation in der Automotive Wertschöp-
fungskette, von OEM zu Tier1 und Tier2. Ziel der Arbeit ist es die Kommunikation
und gemeinsame Planung, speziell in den frĂĽhen Entwicklungsphasen zu verbessern.
Neben SysML ist die Benutzung von vereinbarten Vokabularen und Ontologien in der
Modellierung von Requirements, des Gesamtkontextes, Varianten und vielen anderen
Elementen angezielt. Ontologien sind dabei eine Möglichkeit, um das Vermeiden von
Missverständnissen und Fehlplanungen zu unterstützen. Dieser Ansatz schlägt eine Web-
datenbank vor, wobei Ontologien das Teilen von Wissen und das logische Schlussfolgern
von implizitem Wissen und Regeln unterstĂĽtzen.
Diese Arbeit beschreibt Ontologien für die Domäne des Engineering 4.0, oder spezifischer,
für die Domäne, die für das deutsche Projekt GENIAL! benötigt wurde. Dies betrifft
Domänen, wie Hardware und Software, Roadmapping, Kontext, Innovation, IoT und
andere. Neue Ontologien wurden entworfen, wie beispielsweise die Hardware-Software
Allokations-Ontologie und eine domänen-spezifische "eFuse Ontologie". Das Ergebnis war
eine modulare Ontologie-Bibliothek mit der GENIAL! Basic Ontology, die es erlaubt, automotive und mikroelektronische Komponenten, Funktionen, Eigenschaften und deren
Abhängigkeiten basierend auf dem ISO26262 Standard zu entwerfen. Des weiteren ist
Kontextwissen, welches Entwurfsentscheidungen beinflusst, inkludiert. Diese Wissensbasen sind in einem neuartigen Tool integriert, dass es ermöglicht, Roadmapwissen und
Anforderungen durch die Automobil- Wertschöpfungskette hinweg auszutauschen. On
tologien zu entwerfen und zu wissen, wie man diese benutzt, war dabei keine triviale
Aufgabe und benötigte viel Hintergrund- und Kontextwissen. Ausgewählte Grundlagen
hierfĂĽr sind Richtlinien, wie man Ontologien entwirft, Ontologiekategorien, sowie das
Spektrum an Sprachen und Formen von Wissensrepresentationen. Des weiteren sind fort-
geschrittene Methoden erläutert, z.B wie man mit Ontologien Schlußfolgerungen trifft.
Am Schluss wird das Overall Framework demonstriert, und die Ontologie mit Reason-
ing, Datenbank und APPEL/SysMD (AGILA ProPErty and Dependency Description
Language / System MarkDown) und Constraints der Hardware / Software Wissensbasis
gezeigt. Dabei werden exemplarisch Roadmap Constraints mit dem Automodell verbunden und durch den Constraint Solver gelöst und exploriert
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