The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

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Linked Data - the story so far

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human-andmachine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.

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DBpedia: a nucleus for a web of open data

This collaboratively edited knowledgebase provides a common source of data for Wikipedia, and everyone else.

https://dl.acm.org/doi/pdf/10.1145/2629489

Wikidata: a free collaborative knowledgebase

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 general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

https://people.inf.elte.hu/kiss/11kor/cikkek/20%20DBpedia.pdf

DBpedia - A crystallization point for the Web of Data

Semantic Web conferences such as ESWC and ISWC offer prime opportunities to test and showcase semantic technologies Conference metadata about people, papers and talks is diverse in nature and neither too small to be uninteresting or too big to be unmanageable Many metadata-related challenges that may arise in the Semantic Web at large are also present here Metadata must be generated from sources which are often unstructured and hard to process, and may originate from many different players, therefore suitable workflows must be established Moreover, the generated metadata must use appropriate formats and vocabularies, and be served in a way that is consistent with the principles of linked data This paper reports on the metadata efforts from ESWC and ISWC, identifies specific issues and barriers encountered during the projects, and discusses how these were approached Recommendations are made as to how these may be addressed in the future, and we discuss how these solutions may generalize to metadata production for the Semantic Web at large

/pdf/recipes-for-semantic-web-dog-food-the-eswc-and-iswc-metadata-4cunhdpx0j.pdf

Recipes for semantic web dog food: the ESWC and ISWC metadata projects

will remember a wide-ranging discussion of the role that easily accessible data could play in endeavors from scholarly publishing [3] to the creation of canonical product catalogs [4]. The authors argued there and subsequently [5] that an effective and flexible licensing framework is needed in moving forward. Paradoxically, we argue that you need to actively and consciously assert your desire that third parties be able to use data you place online in order for those 'visible' and 'accessible' data sets to be utilized most effectively. Significant progress has been made in the past twelve months, with engagement [6] from Creative Commons [7, 8, 9] and others [10] resulting in a license [11] and notion of 'community norms' [12] upon which all can build.

/pdf/open-data-commons-a-license-for-open-data-2ep002wqji.pdf

Open Data Commons, a License for Open Data.

The Web is currently evolving from an information space of linked documents, to a Web of linked, machine-readable data. Perhaps counter-intuitively, this shift to publishing data for machine consumption raises many challenges for human-computer interaction. In this article I will discuss some of the implications of this trend for how we interact with the Web of data and consider how familiar tools such as the Web browser may need to develop.

/pdf/how-will-we-interact-with-the-web-of-data-5bsxlmwd3z.pdf

How Will We Interact with the Web of Data

Revyu: Linking reviews and ratings into the Web of Data

This paper introduces DataGraft (https://datagraft.net/) – a cloud-based platform for data transformation and publishing. DataGraft was developed to provide better and easier to use tools for data workers and developers (e.g. open data publishers, linked data developers, data scientists) who consider existing approaches to data transformation, hosting, and access too costly and technically complex. DataGraft offers an integrated, flexible, and reliable cloud-based solution for hosted open data management. Key features include flexible management of data transformations (e.g. interactive creation, execution, sharing, reuse) and reliable data hosting services. This paper provides an overview of DataGraft focusing on the rationale, key features and components, and evaluation.

Tom Heath

Papers

Recipes for semantic web dog food: the ESWC and ISWC metadata projects

Open Data Commons, a License for Open Data.

How Will We Interact with the Web of Data

Revyu: Linking reviews and ratings into the Web of Data

DataGraft: One-stop-shop for open data management