Weaving a Web of linked resources

2.1.From V to S in data management

The so called “Vs” of big data (velocity, variety, volume, veracity) translate to many “S” of Semantic Web: scalability, storage, search, semantics, security, streaming, etc. In terms of data management, scalability of storage and efficiency of querying are active research and development domains to improve RDF store performances. In terms of data access, many approaches now exist on a continuum from HTTP gets, to, Linked Data Fragments, Linked Data Platform REST approach, and SPARQL services, protocol and language. Moreover, to provide reliable, persistent, and trustworthy Linked Data services, topics such as access control, version management, long term preservation are of utmost importance. But again efficiency, federation and hybridization remain hot research questions.

2.2.Formal knowledge and artificial intelligence

On top of these core topics of data management and access, the Semantic Web community has always been interested in providing intelligent processing of the linked data of the Web, starting with reasoning. This remains a challenging and important topic with hard problems in scaling, approximating and distributing reasoning. In addition to classical logical derivation, many other artificially intelligent behaviours are studied in the community including machine learning and data mining, induction of knowledge, deontic reasoning on data licences, etc. In particular, hybrid approaches investigating whether and how techniques of description logic and reasoning can be combined with statistical machine learning are in the focus of current research with the promise to further improve artificial intelligence beyond the current state-of-the-art.

2.3.Heterogeneity of graph types and life cycles

The initial graph of linked pages of the Web has been extended by a growing number of other graphs including: sociograms capturing social network structures, workflows specifying decision paths to be followed, browsing logs capturing trails of navigation, automata of service compositions specifying distributed processing, linked open data from distant datasets, etc. Moreover, these graphs are distributed over many different sources with very different characteristics. Some sub-graphs are public (e.g. DBpedia), while others are private (e.g. semantic intrawebs). Some sub-graphs are small and local (e.g., a user’s profile on a device), and some are huge and hosted on clusters (e.g., Wikipedia). Some are largely stable (e.g., a thesaurus for Latin), some change several times per second (e.g., sensor data in a smart city), etc. And each type of graph of the Web is not an isolated island. Graphs interact with each other: the networks of communities influence the message flows, their subjects and types, the semantic links between terms interact with the links between sites and vice versa, the small changing graphs of sensors are joined to the large stable geographical graphs that position them, etc. Not only do we need methods to represent and analyse each kind of graph, we also require the means to combine them and to perform multi-criteria analyses on their combinations.

2.4.A truly open-world assumption

One of the major changes when trying to port database and knowledge base approaches to the Web is the open world assumption (OWA) principle that underlies Semantic Web technologies. Many existing results from more established research domains, such as databases, knowledge based systems or model-based engineering, have to be revisited to account for this open-world assumption. But in a broader sense, the open world of linked data on the Web also adds additional challenges to address uncertainty, data quality, data and processing traceability in order to be able to provide reasons for the users to trust the systems and their results.

2.5.Human–machine partnership

This expanding Web of data, together with the schemas, ontologies and vocabularies used to structure and link it, form a formal Semantic Web with which we have to design new interaction means to support the next generation of Web applications. The Semantic Web has a role to play in addressing challenges at the intersection of knowledge-based interactions and Web-augmented interactions [11]: there is not only a need for Human Computer Interaction (HCI) to provide methods to design human-data interaction applied to linked data on the Web, but also inversely a need for the Semantic Web community to investigate how linked data and the intelligent inferences they support can improve human-machine interactions. On the Web, large-scale interactions also create many challenges, and in particular the ongoing need to reconcile the formal semantics of computer science (logics, ontologies, typing systems, etc.) on which the Web architecture is built, with the soft semantics of people (posts, tags, status, and so on) through which a lot of the Web content is created. And as the Web becomes a ubiquitous infrastructure reflecting all the objects of our world, we witness ever-increasing frictions between formal semantics and social semantics. A promising research avenue to span the gap between formal and social semantics is the use of Human Computation and Crowdsourcing techniques to involve large, distributed groups of users in the various stages of the knowledge engineering life-cycle, from ontology modeling and verification to annotation, data curation and entity linking [15].

2.6.Conquering new application domains

The benefits of Semantic Web technologies had been firstly apparent for information-intensive domains such as medicine, library science or cultural heritage where taxonomy structures were already well accepted. With the advent of Linked Data technologies, our community increasingly addressed large-scale data integration problems with enterprises knowledge graphs. Recent years have seen the uptake of Semantic Web technologies in application domains that span the borders of the digital and physical world. As a result we explored the use of our technologies as integral part of cyber-physical systems such as adaptive traffic control systems in smart cities [8] or flexible assembly lines in smart factories [7].

3.1.Dynamics of research topics in Semantic Web

The previous topics are only a taste of the many important topics now addressed at international venues such as ISWC, ESWC and WWW conference series. As shown in Fig. 3 these topics were born, grew and went mainstream very dynamically over the past 14 years as we moved from solved to new challenges. Starting out with a small number of key topics focused on representation languages (rdf, web ontology language), query answering, ontology engineering and learning primarily, Semanting Web research has been extended with further topics including: semantic web services (with a peak of activity between 2002–2009), linked data (which represents an major research share since 2007) as well as data management topics such as ontology matching and SPARQL.

Fig. 3.

Rexplore showing the evolution of major topics and keywords in the Semantic Web community over the last 14 years [14].

This special issue is based on the top articles selected from the 12th ESWC in 2015. Besides having a main focus on advances in Semantic Web research and technologies, we, the Chairs of ESWC 2015, decided to broaden the scope to span other relevant research areas as well. In particular, as a response to the emerging human-machine partnership challenge, the core tracks of the research conference were complemented with new tracks focusing on linking machine and human computation at Web scale (Cognition and Semantic Web, Human Computation and Crowdsourcing). The current special issues contains two extended papers from the Reasoning track and one extended paper from the new track on Cognition:

3.2.Multidisciplinary approaches to support diversity

The participatory nature of the Web makes it emerge as an openly co-constructed global and inherently heterogeneous artifact. The “world-wide way” of deploying the Web everywhere and for everything implies that, as the Web is spreading into the world, the world is spreading into the Web. The resulting world “wild” Web that is being created and is evolving every day is affected by, and at the same time reflects, the complexity of our world. From a Semantic Web point of view, as soon as we want to model, analyse and combine these many facets of one Web, we face the general challenge of its diversity and span. This complexity implies that a huge challenge for Web development in general, and for the Semantic Web community in particular, is the resulting need for large-scale multidisciplinary cooperation: the three ‘W’s of the World Wide Web call for the three ‘M’s of a Massively Multidisciplinary Methodology [9], and the Semantic Web is no exception to this.