Tracing the origins of the semantic web

Guns, Raf

doi:10.1002/asi.22907

Cited by 20 publications

(14 citation statements)

References 30 publications

(28 reference statements)

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“…The challenge of making systems interoperate is leading some researchers to seek inspiration in the field of artificial intelligence, in particular the semantic web. However, in doing so, it is important to consider the literature discussing when such approaches might be unfruitful (Uschold, 2001;Haikonen, 2009;Ekbia, 2010;Guns, 2013). In the fields of artificial intelligence and cognitive science, the representations chosen are commonly considered to determine which types of tasks the system can handle (Churchland & Sejnowski, 1992;Valiant, 2000;Gärdenfors, 2004;Stewart & Eliasmith, 2012;Doumas & Hummel, 2012).…”

Section: Alternativementioning

confidence: 98%

“…The names at the end of each arrow are the statement's objects (Operator ID, Working day/Non-working day and On/Off) and the names that label the arrows are the predicates (used, in and state). Semantic descriptions of this type enable design and integration of heterogenous systems (Baresi et al, 2013), but are ambiguous and can be misinterpreted (Fisher, 2006;Ekbia, 2010;Guns, 2013).…”

Section: Machinementioning

confidence: 99%

“…Therefore, semantic descriptions can be misinterpreted (Fisher, 2006Section 3.7). Several decades of artificial intelligence research suggests (Ekbia, 2010;Guns, 2013) that it is difficult to make machines that automatically and correctly make use of such non-grounded information (Harnad, 1990;Barsalou, 2008). In general, the conventional approach to designing large-scale heterogeneous systems appears to have the following limitations:…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vector space architecture for emergent interoperability of systems by learning from demonstration

Emruli

Sandin

Delsing

2015

Biologically Inspired Cognitive Architectures

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The rapid integration of physical systems with cyberspace infrastructure, the so-called Internet of Things, is likely to have a significant effect on how people interact with the physical environment and design information and communication systems. Internet-connected systems are expected to vastly outnumber people on the planet in the near future, leading to grand challenges in software engineering and automation in application domains involving complex and evolving systems. Several decades of artificial intelligence research suggests that conventional approaches to making such systems automatically interoperable using handcrafted ''semantic'' descriptions of services and information are difficult to apply. In this paper we outline a bioinspired learning approach to creating interoperable systems, which does not require handcrafted semantic descriptions and rules. Instead, the idea is that a functioning system (of systems) can emerge from an initial pseudorandom state through learning from examples, provided that each component conforms to a set of information coding rules. We combine a binary vector symbolic architecture (VSA) with an associative memory known as sparse distributed memory (SDM) to model context-dependent prediction by learning from examples. We present simulation results demonstrating that the proposed architecture can enable system interoperability by learning, for example by human demonstration. ª 2015 Published by Elsevier B.V.

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Section: Alternativementioning

confidence: 98%

Section: Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vector space architecture for emergent interoperability of systems by learning from demonstration

Emruli

Sandin

Delsing

2015

Biologically Inspired Cognitive Architectures

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“…Their designation is very important in the context of the Semantic Web because the atomic level is the node, i.e. a URI, and "One node represents one object" [6]. Ultimately, discovering whether a KOS concept can be decomposed into smaller constituents would contribute both to an assessment of the suitability of its migration to a SKOS concept and designate potential applications in KOS editing in order for it to be more suitable for SKOS in the context of the Semantic Web.The rest of the study is organized as follows: Section 2 presents the background analysis of the field consisting of information about SKOS, the representation of concepts within certain types of KOSs as well as information about natural language in terms of grammatical and syntactic rules.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

SKOS concepts and natural language concepts: An analysis of latent relationships in KOSs

Mastora

Peponakis

Kapidakis

2016

Journal of Information Science

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The vehicle to represent Knowledge Organization Systems (KOSs) in the environment of the Semantic Web and linked data is the Simple Knowledge Organization System (SKOS). SKOS provides a way to assign a URI to each concept, and this URI functions as a surrogate for the concept. This fact makes of main concern the need to clarify the URIs' ontological meaning. The aim of this study is to investigate the relation between the ontological substance of KOS concepts and concepts revealed through the grammatical and syntactic formalisms of natural language. For this purpose, we examined the dividableness of concepts in specific KOSs (i.e. a thesaurus, a subject headings system and a classification scheme) by applying Natural Language Processing (NLP) techniques (i.e. morphosyntactic analysis) to the lexical representations (i.e. RDF literals) of SKOS concepts. The results of the comparative analysis reveal that, despite the use of multi-word units, thesauri tend to represent concepts in a way that can hardly be further divided conceptually, while Subject Headings and Classification Schemes -to a certain extent -comprise terms that can be decomposed into more conceptual constituents. Consequently, SKOS concepts deriving from thesauri are more likely to represent atomic conceptual units and thus be more appropriate tools for inference and reasoning. Since identifiers represent the meaning of a concept, complex concepts are neither the most appropriate nor the most efficient way of modelling a KOS for the Semantic Web.

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One schema to rule them all: How Schema.org models the world of search

Iliadis

Acker

Stevens

et al. 2023

Asso for Info Science & Tech

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Several industry‐specific metadata initiatives have historically facilitated structured data modeling for the web in domains such as commerce, publishing, social media, and so forth. The metadata vocabularies produced by these initiatives allow developers to “wrap” information on the web to provide machine‐readable signals for search engines, advertisers, and user‐facing content on apps and websites, thus assisting with surfacing facts about people, places, and products. A universal iteration of such a project called Schema.org started in 2011, resulting from a partnership between Google, Microsoft, Yahoo, and Yandex to collaborate on a single structured data model across domains. Yet, few studies have explored the metadata vocabulary terms in this significant web resource. What terms are included, upon what subject domains do they focus, and how does Schema.org represent knowledge in its conceptual model? This article presents findings from our extraction and analysis of the documented release history and complete hierarchy on Schema.org's developer pages. We provide a semantic network visualization of Schema.org, including an analysis of its modularity and domains, and discuss its global significance concerning fact‐checking and COVID‐19. We end by theorizing Schema.org as a gatekeeper of data on the web that authors vocabulary that everyday web users encounter in their searches.

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Tracing the origins of the semantic web

Cited by 20 publications

References 30 publications

Vector space architecture for emergent interoperability of systems by learning from demonstration

Vector space architecture for emergent interoperability of systems by learning from demonstration

SKOS concepts and natural language concepts: An analysis of latent relationships in KOSs

One schema to rule them all: How Schema.org models the world of search

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