The Evolution of the Concept of Semantic Web in the Context of Wikipedia: an Exploratory Approach to Study the Collective Concep
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Social Factory Architecture: Social Networking Services And
Social Factory Architecture: Social Networking Services and Production Scenarios Through the Social Internet of Things, Services and People for the Social Operator 4.0 David Romero, Thorsten Wuest, Johan Stahre, Dominic Gorecky To cite this version: David Romero, Thorsten Wuest, Johan Stahre, Dominic Gorecky. Social Factory Architecture: Social Networking Services and Production Scenarios Through the Social Internet of Things, Services and People for the Social Operator 4.0. IFIP International Conference on Advances in Production Manage- ment Systems (APMS), Sep 2017, Hamburg, Germany. pp.265-273, 10.1007/978-3-319-66923-6_31. hal-01666177 HAL Id: hal-01666177 https://hal.inria.fr/hal-01666177 Submitted on 18 Dec 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Social Factory Architecture: Social Networking Services and Production Scenarios through the Social Internet of Things, Services and People for the Social Operator 4.0 David Romero1-2, Thorsten Wuest3, Johan Stahre4, Dominic Gorecky5 1 Tecnológico de Monterrey, Mexico 2 Griffith University, Australia [email protected] 3 West Virginia University, USA [email protected] 4 Chalmers University of Technology, Sweden [email protected] 5 Switzerland Innovation, Switzerland [email protected] Abstract. -
Ontologies and Semantic Web for the Internet of Things - a Survey
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/312113565 Ontologies and Semantic Web for the Internet of Things - a survey Conference Paper · October 2016 DOI: 10.1109/IECON.2016.7793744 CITATIONS READS 5 256 2 authors: Ioan Szilagyi Patrice Wira Université de Haute-Alsace Université de Haute-Alsace 10 PUBLICATIONS 17 CITATIONS 122 PUBLICATIONS 679 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: Physics of Solar Cells and Systems View project Artificial intelligence for renewable power generation and management: Application to wind and photovoltaic systems View project All content following this page was uploaded by Patrice Wira on 08 January 2018. The user has requested enhancement of the downloaded file. Ontologies and Semantic Web for the Internet of Things – A Survey Ioan Szilagyi, Patrice Wira MIPS Laboratory, University of Haute-Alsace, Mulhouse, France {ioan.szilagyi; patrice.wira}@uha.fr Abstract—The reality of Internet of Things (IoT), with its one of the most important task in an IoT system [6]. Providing growing number of devices and their diversity is challenging interoperability among the things is “one of the most current approaches and technologies for a smarter integration of fundamental requirements to support object addressing, their data, applications and services. While the Web is seen as a tracking and discovery as well as information representation, convenient platform for integrating things, the Semantic Web can storage, and exchange” [4]. further improve its capacity to understand things’ data and facilitate their interoperability. In this paper we present an There is consensus that Semantic Technologies is the overview of some of the Semantic Web technologies used in IoT appropriate tool to address the diversity of Things [4], [7]–[9]. -
Rdfa in XHTML: Syntax and Processing Rdfa in XHTML: Syntax and Processing
RDFa in XHTML: Syntax and Processing RDFa in XHTML: Syntax and Processing RDFa in XHTML: Syntax and Processing A collection of attributes and processing rules for extending XHTML to support RDF W3C Recommendation 14 October 2008 This version: http://www.w3.org/TR/2008/REC-rdfa-syntax-20081014 Latest version: http://www.w3.org/TR/rdfa-syntax Previous version: http://www.w3.org/TR/2008/PR-rdfa-syntax-20080904 Diff from previous version: rdfa-syntax-diff.html Editors: Ben Adida, Creative Commons [email protected] Mark Birbeck, webBackplane [email protected] Shane McCarron, Applied Testing and Technology, Inc. [email protected] Steven Pemberton, CWI Please refer to the errata for this document, which may include some normative corrections. This document is also available in these non-normative formats: PostScript version, PDF version, ZIP archive, and Gzip’d TAR archive. The English version of this specification is the only normative version. Non-normative translations may also be available. Copyright © 2007-2008 W3C® (MIT, ERCIM, Keio), All Rights Reserved. W3C liability, trademark and document use rules apply. Abstract The current Web is primarily made up of an enormous number of documents that have been created using HTML. These documents contain significant amounts of structured data, which is largely unavailable to tools and applications. When publishers can express this data more completely, and when tools can read it, a new world of user functionality becomes available, letting users transfer structured data between applications and web sites, and allowing browsing applications to improve the user experience: an event on a web page can be directly imported - 1 - How to Read this Document RDFa in XHTML: Syntax and Processing into a user’s desktop calendar; a license on a document can be detected so that users can be informed of their rights automatically; a photo’s creator, camera setting information, resolution, location and topic can be published as easily as the original photo itself, enabling structured search and sharing. -
Social Machines the Coming Collision of Artificial Intelligence, Social Networking, and Humanity
apress.com Computer Science : Artificial Intelligence Hendler, James, Mulvehill, Alice M. Social Machines The Coming Collision of Artificial Intelligence, Social Networking, and Humanity What the concept of a social machine is and how the activities of non- programmers are contributing to machine intelligence How modern artificial intelligence technologies, such as Watson, are evolving and how they process knowledge from both carefully produced information (such as Wikipedia and journal articles) and from big data collections The fundamentals of neuromorphic computing, knowledge graph search, and linked data, as well as the basic technology concepts that underlie networking applications such as Facebook and Twitter How the change in attitudes towards cooperative work on the Web, especially in the younger demographic, is critical to the future of Web applications Apress Will your next doctor be a human being—or a machine? Will you have a choice? If you do, 1st ed., XVI, 174 p. 51 illus., what should you know before making it? This book introduces the reader to the pitfalls and 1st 46 illus. in color. edition promises of artificial intelligence (AI) in its modern incarnation and the growing trend of systems to "reach off the Web" into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of Printed book computing but of our world and species. AI experts and researchers James Hendler—co- Softcover originator of the Semantic Web (Web 3.0)—and Alice Mulvehill—developer of AI-based Printed book operational systems for DARPA, the Air Force, and NASA—explore the social implications of AI Softcover systems in the context of a close examination of the technologies that make them possible. -
The Application of Semantic Web Technologies to Content Analysis in Sociology
THEAPPLICATIONOFSEMANTICWEBTECHNOLOGIESTO CONTENTANALYSISINSOCIOLOGY MASTER THESIS tabea tietz Matrikelnummer: 749153 Faculty of Economics and Social Science University of Potsdam Erstgutachter: Alexander Knoth, M.A. Zweitgutachter: Prof. Dr. rer. nat. Harald Sack Potsdam, August 2018 Tabea Tietz: The Application of Semantic Web Technologies to Content Analysis in Soci- ology, , © August 2018 ABSTRACT In sociology, texts are understood as social phenomena and provide means to an- alyze social reality. Throughout the years, a broad range of techniques evolved to perform such analysis, qualitative and quantitative approaches as well as com- pletely manual analyses and computer-assisted methods. The development of the World Wide Web and social media as well as technical developments like optical character recognition and automated speech recognition contributed to the enor- mous increase of text available for analysis. This also led sociologists to rely more on computer-assisted approaches for their text analysis and included statistical Natural Language Processing (NLP) techniques. A variety of techniques, tools and use cases developed, which lack an overall uniform way of standardizing these approaches. Furthermore, this problem is coupled with a lack of standards for reporting studies with regards to text analysis in sociology. Semantic Web and Linked Data provide a variety of standards to represent information and knowl- edge. Numerous applications make use of these standards, including possibilities to publish data and to perform Named Entity Linking, a specific branch of NLP. This thesis attempts to discuss the question to which extend the standards and tools provided by the Semantic Web and Linked Data community may support computer-assisted text analysis in sociology. First, these said tools and standards will be briefly introduced and then applied to the use case of constitutional texts of the Netherlands from 1884 to 2016. -
The Semantic Web: the Origins of Artificial Intelligence Redux
The Semantic Web: The Origins of Artificial Intelligence Redux Harry Halpin ICCS, School of Informatics University of Edinburgh 2 Buccleuch Place Edinburgh EH8 9LW Scotland UK Fax:+44 (0) 131 650 458 E-mail:[email protected] Corresponding author is Harry Halpin. For further information please contact him. This is the tear-off page. To facilitate blind review. Title:The Semantic Web: The Origins of AI Redux working process managed to both halt the fragmentation of Submission for HPLMC-04 the Web and create accepted Web standards through its con- sensus process and its own research team. The W3C set three long-term goals for itself: universal access, Semantic Web, and a web of trust, and since its creation these three goals 1 Introduction have driven a large portion of development of the Web(W3C, 1999) The World Wide Web is considered by many to be the most significant computational phenomenon yet, although even by One comparable program is the Hilbert Program in mathe- the standards of computer science its development has been matics, which set out to prove all of mathematics follows chaotic. While the promise of artificial intelligence to give us from a finite system of axioms and that such an axiom system machines capable of genuine human-level intelligence seems is consistent(Hilbert, 1922). It was through both force of per- nearly as distant as it was during the heyday of the field, the sonality and merit as a mathematician that Hilbert was able ubiquity of the World Wide Web is unquestionable. If any- to set the research program and his challenge led many of the thing it is the Web, not artificial intelligence as traditionally greatest mathematical minds to work. -
Semantic Computing
SEMANTIC COMPUTING Lecture 7: Introduction to Distributional and Distributed Semantics Dagmar Gromann International Center For Computational Logic TU Dresden, 30 November 2018 Overview • Distributional Semantics • Distributed Semantics – Word Embeddings Dagmar Gromann, 30 November 2018 Semantic Computing 2 Distributional Semantics Dagmar Gromann, 30 November 2018 Semantic Computing 3 Distributional Semantics Definition • meaning of a word is the set of contexts in which it occurs • no other information is used than the corpus-derived information about word distribution in contexts (co-occurrence information of words) • semantic similarity can be inferred from proximity in contexts • At the very core: Distributional Hypothesis Distributional Hypothesis “similarity of meaning correlates with similarity of distribution” - Harris Z. S. (1954) “Distributional structure". Word, Vol. 10, No. 2-3, pp. 146-162 meaning = use = distribution in context => semantic distance Dagmar Gromann, 30 November 2018 Semantic Computing 4 Remember Lecture 1? Types of Word Meaning • Encyclopaedic meaning: words provide access to a large inventory of structured knowledge (world knowledge) • Denotational meaning: reference of a word to object/concept or its “dictionary definition” (signifier <-> signified) • Connotative meaning: word meaning is understood by its cultural or emotional association (positive, negative, neutral conntation; e.g. “She’s a dragon” in Chinese and English) • Conceptual meaning: word meaning is associated with the mental concepts it gives access to (e.g. prototype theory) • Distributional meaning: “You shall know a word by the company it keeps” (J.R. Firth 1957: 11) John Rupert Firth (1957). "A synopsis of linguistic theory 1930-1955." In Special Volume of the Philological Society. Oxford: Oxford University Press. Dagmar Gromann, 30 November 2018 Semantic Computing 5 Distributional Hypothesis in Practice Study by McDonald and Ramscar (2001): • The man poured from a balack into a handleless cup. -
Transformer Networks of Human Conceptual Knowledge Sudeep Bhatia and Russell Richie University of Pennsylvania March 1, 2021 Se
TRANSFORMER NETWORKS OF CONCEPTUAL KNOWLEDGE 1 Transformer Networks of Human Conceptual Knowledge Sudeep Bhatia and Russell Richie University of Pennsylvania March 1, 2021 Send correspondence to Sudeep Bhatia, Department of Psychology, University of Pennsylvania, Philadelphia, PA. Email: [email protected]. Funding was received from the National Science Foundation grant SES-1847794. TRANSFORMER NETWORKS OF CONCEPTUAL KNOWLEDGE 2 Abstract We present a computational model capable of simulating aspects of human knowledge for thousands of real-world concepts. Our approach involves fine-tuning a transformer network for natural language processing on participant-generated feature norms. We show that such a model can successfully extrapolate from its training dataset, and predict human knowledge for novel concepts and features. We also apply our model to stimuli from twenty-three previous experiments in semantic cognition research, and show that it reproduces fifteen classic findings involving semantic verification, concept typicality, feature distribution, and semantic similarity. We interpret these results using established properties of classic connectionist networks. The success of our approach shows how the combination of natural language data and psychological data can be used to build cognitive models with rich world knowledge. Such models can be used in the service of new psychological applications, such as the cognitive process modeling of naturalistic semantic verification and knowledge retrieval, as well as the modeling of real-world categorization, decision making, and reasoning. Keywords: Conceptual knowledge; Semantic cognition; Distributional semantics; Connectionist modeling; Transformer networks TRANSFORMER NETWORKS OF CONCEPTUAL KNOWLEDGE 3 Introduction Knowledge of concepts and their features is one of the fundamental topics of inquiry in cognitive science (Murphy, 2004; Rips et al., 2012). -
Scholarly Social Machines
Scholarly Social Machines David De Roure1, 1Oxford e-Research Centre, University of Oxford, Oxford, UK [email protected] Identifier: http://www.oerc.ox.ac.uk/sites/default/files/users/user384 /scholarly-social-machines.html In Reply To: https://linkedresearch.org/calls Despite many attempts to perturb a scholarly publishing system that is over 350 years old, it feels pretty much like business as usual.[1] Here I question whether we have become trapped inside the machine, and argue that if we want to change anything in an informed way then we need to step outside and take a look. How do we do this? First I describe what I mean by a social ma‐ chine, and the “scholarly social machines ecosystem”. The article closes with a list of questions that I believe we need to be asking. The evolutionary growth of new social engines Once upon a time, interacting with digital content was an option, as was turn‐ ing to social networking sites to communicate with friends and colleagues. To‐ day our lives are mandatorily mediated by technology that enables academic, social, economic and cultural interactions at scale. Our widespread adoption of Web, laptop and smartphone, with many more devices still to come, means we find ourselves living in interleaved physical and virtual worlds. The design and analysis of these socio-technical systems has attracted much academic attention, exploring both social science and computer science per‐ spectives. Here we focus on one model in particular, because it is an abstrac‐ tion that underpins the Web—it is the Social Machine. -
Prototype Theory and Emotion Semantic Change Aotao Xu ([email protected]) Department of Computer Science, University of Toronto
Prototype theory and emotion semantic change Aotao Xu ([email protected]) Department of Computer Science, University of Toronto Jennifer Stellar ([email protected]) Department of Psychology, University of Toronto Yang Xu ([email protected]) Department of Computer Science, Cognitive Science Program, University of Toronto Abstract provided evidence for this prototype view using a variety An elaborate repertoire of emotions is one feature that dis- of stimuli ranging from emotion words (Storm & Storm, tinguishes humans from animals. Language offers a critical 1987), videos (Cowen & Keltner, 2017), and facial expres- form of emotion expression. However, it is unclear whether sions (Russell & Bullock, 1986; Ekman, 1992). Prototype the meaning of an emotion word remains stable, and what fac- tors may underlie changes in emotion meaning. We hypothe- theory provides a synchronic account of the mental represen- size that emotion word meanings have changed over time and tation of emotion terms, but how this view extends or relates that the prototypicality of an emotion term drives this change to the diachronic development of emotion words is an open beyond general factors such as word frequency. We develop a vector-space representation of emotion and show that this problem that forms the basis of our inquiry. model replicates empirical findings on prototypicality judg- ments and basic categories of emotion. We provide evidence Theories of semantic change that more prototypical emotion words have undergone less change in meaning than peripheral emotion words over the past Our work also draws on an independent line of research in century, and that this trend holds within each family of emo- historical semantic change. -
Exploiting Semantic Web Knowledge Graphs in Data Mining
Exploiting Semantic Web Knowledge Graphs in Data Mining Inauguraldissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften der Universit¨atMannheim presented by Petar Ristoski Mannheim, 2017 ii Dekan: Dr. Bernd Lübcke, Universität Mannheim Referent: Professor Dr. Heiko Paulheim, Universität Mannheim Korreferent: Professor Dr. Simone Paolo Ponzetto, Universität Mannheim Tag der mündlichen Prüfung: 15 Januar 2018 Abstract Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. Semantic Web knowledge graphs are a backbone of many in- formation systems that require access to structured knowledge. Such knowledge graphs contain factual knowledge about real word entities and the relations be- tween them, which can be utilized in various natural language processing, infor- mation retrieval, and any data mining applications. Following the principles of the Semantic Web, Semantic Web knowledge graphs are publicly available as Linked Open Data. Linked Open Data is an open, interlinked collection of datasets in machine-interpretable form, covering most of the real world domains. In this thesis, we investigate the hypothesis if Semantic Web knowledge graphs can be exploited as background knowledge in different steps of the knowledge discovery process, and different data mining tasks. More precisely, we aim to show that Semantic Web knowledge graphs can be utilized for generating valuable data mining features that can be used in various data mining tasks. -
A Bayesian Approach to 'Overinformative' Referring Expressions
RUNNING HEAD: USEFULLY REDUNDANT REFERRING EXPRESSIONS 1 When redundancy is useful: A Bayesian approach to `overinformative' referring expressions Judith Degen•, Robert X.D. Hawkins•, Caroline Graf., Elisa Kreiss• and Noah D. Goodman• •Stanford University .Freie Universit¨atBerlin December 11, 2019 Author note: The earliest precursor of this work (the core idea of a continuous semantics RSA model and Exp. 1) was presented as a talk at the RefNet Round Table Event in 2016 and at AMLaP 2016. Exp. 2 and the corresponding model were presented as a talk at the CUNY Conference on Sentence Processing in 2017 and as a poster at the Experimental Pragmatics (XPrag) Conference in 2017. An earlier version of Exp. 3 and an earlier version of the corresponding model appeared in the Proceedings of the 38th Conference of the Cognitive Science Society. All experiments and models have been presented by the first author in various invited talks at workshops and colloquia in Linguistics, Psychology, Philosophy, and Cognitive Science arXiv:1903.08237v3 [cs.CL] 10 Dec 2019 since 2016. We are grateful to many audiences and individuals for discussions that have made this paper better, in particular Sarah Brown-Schmidt, Herb Clark, Michael Franke, Gerhard J¨ager,Florian Jaeger, Emiel Krahmer, Timo Roettger, Paula Rubio-Fern´andez,Greg Scontras, Mike Tanenhaus, and our anonymous reviewers; audiences at the RefNet Round Table Event 2015, CogSci 2016, AMLaP 2016, CUNY 2017, XPrag 2017, the UC Davis Perception, Cognition, and Cognitive Neuroscience Brown Bag, the Bay Area Philosophy of Science Group, the IMBS Quantitative Approaches to Language Workshop 2018; and audiences at colloquium talks given by the first author at Berkeley Psychology, CMU Philosophy, Dartmouth Linguistics, McGill Linguistics, OSU Linguistics, Stanford Linguistics, T¨ubingenLinguistics, and UC Davis Linguistics.