Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in
We propose a novel attention network for document annotation with user-generated tags. The network is designed according to the human reading and annotation behaviour. Usually, users try to digest the title and obtain a rough idea about the topic first, and then read the content of the document. Present research shows that the title metadata could largely affect the social annotation. To better utilise this information, we design a framework that separates the title from the content of a document and apply a title-guided attention mechanism over each sentence in the content. We also propose two semanticbased loss regularisers that enforce the output of the network to conform to label semantics, i.e. similarity and subsumption. We analyse each part of the proposed system with two real-world open datasets on publication and question annotation. The integrated approach, Joint Multi-label Attention Network (JMAN), significantly outperformed the Bidirectional Gated Recurrent Unit (Bi-GRU) by around 13%-26% and the Hierarchical Attention Network (HAN) by around 4%-12% on both datasets, with around 10%-30% reduction of training time.
What is Semantic Similarity? Definition of Semantic Similarity: A concept whereby a set of documents or terms within term lists are assigned a metric based on the likeness of their meaning/semantic content. ( Wikipedia, 2012e ).
Inrupt’s dedicated team of developers, designers and business people have been working with a core of Solid experts and members of the open-source community to ensure it’s becoming robust, feature-rich and increasingly ready for wide-scale adoption.
Solid (derived from "social linked data") is a proposed set of conventions and tools for building decentralized social applications based on Linked Data principles. Solid is modular and extensible and it relies as much as possible on existing W3C standards and protocols.
What is the Semantic Web? Read on for a brief introduction to the Semantic Web, how to get started using it, and to understand why we should invest in making our content semantic.
Ontotext GraphDB is a highly-efficient and robust graph database with RDF and SPARQL support. This documentation is a comprehensive guide, which explains every feature of GraphDB as well as topics such as setting up a repository, loading and working with data, tuning its performance, scaling, etc.
TDB is a component of Jena for RDF storage and query. It support the full range of Jena APIs. TDB can be used as a high performance RDF store on a single machine. This documentation describes the latest version, unless otherwise noted.
An Enterprise Knowledge Graph platform. Stardog’s semantic graphs, data modeling, and deep reasoning make it fast and easy to turn data into knowledge without writing code. With Stardog you can unify, query, search, and analyze all your data. Say goodbye to data silos forever.
The Semantic Web is the extension of the World Wide Web that enables people to share content beyond the boundaries of applications and websites. It has been described in rather different ways: as a utopic vision, as a web of data, or merely as a natural paradigm shift in our daily use of the Web.
he W3C Web Ontology Language (OWL) is a Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things.
The OWL 2 Web Ontology Language, informally OWL 2, is an ontology language for the Semantic Web with formally defined meaning. OWL 2 ontologies provide classes, properties, individuals, and data values and are stored as Semantic Web documents. OWL 2 ontologies can be used along with information written in RDF, and OWL 2 ontologies themselves are primarily exchanged as RDF documents.
The term “Semantic Web” refers to W3C’s vision of the Web of linked data. Semantic Web technologies enable people to create data stores on the Web, build vocabularies, and write rules for handling data. Linked data are empowered by technologies such as RDF, SPARQL, OWL, and SKOS.
OWL lets you say much more about your data model, it shows you how to work efficiently with database queries and automatic reasoners, and it provides useful annotations for bringing your data models into the real world.
s a lightweight Linked Data format. It is easy for humans to read and write. It is based on the already successful JSON format and provides a way to help JSON data interoperate at Web-scale. JSON-LD is an ideal data format for programming environments, REST Web services, and unstructured databases such as CouchDB and MongoDB.
In the Beginning... ...there was no inheritance and no composition, only code. And the code was unwieldy, repetitive, blocky, unhappy, verbose, and tired. Copy and Paste were the primary mechanisms of code reuse. Procedures and functions were rare, newfangled gadgets viewed with suspicion. Calling a procedure was expensive! Separating pieces of code from the main logic caused confusion! It was a Dark Time.
The desire for better Web APIs is what motivated the creation of JSON-LD, not the Semantic Web. If you want to make the Semantic Web a reality, stop making the case for it and spend your time doing something more useful, like actually making machines smarter or helping people publish data in a way that’s useful to them.
Semantic UI React is the official React integration for Semantic UI: jQuery Free; Declarative API; Augmentation; Shorthand Props; Sub Components; Auto Controlled State.
T. Tietz, J. Waitelonis, K. Zhou, P. Felgentreff, N. Meyer, A. Weber, and H. Sack. Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems co-located with 15th International Conference on Semantic Systems (SEMANTiCS 2019), Karlsruhe, Germany, September 9th - to - 12th, 2019, volume 2451 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)
G. Gesese, M. Alam, and H. Sack. Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2020) co-located with the 17th Extended Semantic Web Conference 2020 (ESWC 2020), Heraklion, Greece, June 02, 2020 - moved online, volume 2635 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)
M. Alam, H. Birkholz, D. Dess\`ı, C. Eberl, H. Fliegl, P. Gumbsch, P. von Hartrott, L. Mädler, M. Niebel, H. Sack and 1 other author(s). Joint Proceedings of the Semantics co-located events: Poster&Demo track and Workshop on Ontology-Driven Conceptual Modelling of Digital Twins co-located with Semantics 2021, Amsterdam and Online, September 6-9, 2021, volume 2941 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)
T. Dong, and M. Khosla. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), page 1835-1842. Los Alamitos, CA, USA, IEEE Computer Society, (December 2020)