I am a Machine, Let Me Understand Web Media!

  title={I am a Machine, Let Me Understand Web Media!},
  author={Magnus Knuth and J{\"o}rg Waitelonis and Harald Sack},
The majority of web assets cannot be understood by machines, because of the lack of available explicit and machine readable semantics. By enabling machines to understand the meaning of web media, fully automated discovery, processing, and linking become feasible. Semantic Web technologies offer the possibility to enhance web resources with explicit semantics via linking to ontologies encoded in RDF. We demand to make the content of every web asset explicit for machines with the least possible… 

Optimizing B-tree search performance of big data sets / Mohsen Marjani

The results of the experimental analysis show that the new proposed search method decreases the execution time of the search tasks and it improves the search performance several times better than B-tree search performance for same query and same dataset.

FAIR geovisualizations: definitions, challenges, and the road ahead

  • Auriol Degbelo
  • Computer Science
    International Journal of Geographical Information Science
  • 2021
The framework for FAIR geovisualizations proposed, and the open questions identified are relevant to researchers working on findable, accessible, interoperable, and reusable online visualizations of geographic information.



COMM: Designing a Well-Founded Multimedia Ontology for the Web

This work derives a number of requirements for specifying a formal multimedia ontology before presenting the developed ontology, COMM, and evaluating it with respect to its requirements.

Survey of Semantic Media Annotation Tools for the Web: Towards New Media Applications with Linked Media

The Linked Media principles are outlined which can help form a consensus on media annotation approaches, current media annotation tools against these principles are surveyed, and two emerging toolsets which can support Linked media conformant annotation are presented.

Open Annotation Data Model

The Open Annotation Core Data Model specifies an interoperable framework for creating associations between related resources, annotations, using a methodology that conforms to the Architecture of the

Integrating NLP Using Linked Data

It is argued that simplifying the interoperability of different NLP tools performing similar but also complementary tasks will facilitate the comparability of results and the creation of sophisticated NLP applications.

DBpedia Commons: Structured Multimedia Metadata from the Wikimedia Commons

This paper describes the creation of the DBpedia Commons (DBc) dataset, which was achieved by an extension of the Extraction Framework to support knowledge extraction from Wikimedia Commons as a media repository and is the first complete RDFization of the Wikimedia Commons and the largest media metadata RDF database in the LOD cloud.

Designing the W3C open annotation data model

This paper presents the W3C Open Annotation Community Group specification and the rationale behind the scoping and technical decisions that were made, and motivates interoperable Annotations via use cases, and provides a brief analysis of the advantages over previous specifications.

GERBIL: General Entity Annotator Benchmarking Framework

GERBIL aims to become a focal point for the state of the art, driving the research agenda of the community by presenting comparable objective evaluation results.

DCMI Abstract Model

This document specifies an abstract model for DCMI metadata [DCMI], to provide a reference model against which particular DC encoding guidelines can be compared and facilitates the development of better mappings and translations between different syntaxes.

Multipurpose Internet Mail Extensions (MIME) Part One: Format of Internet Message Bodies

This set of documents, collectively called the Multipurpose Internet Mail Extensions, or MIME, redefines the format of messages to allow for MIME to be used in e-mail.