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Comprehensive reviews of veterinary syndromic surveillance have pointed out the hindrances to integration and interoperability among systems using different data sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that the solution needed is an ontology. Here we describe the advantages of the approach, and the methodological steps planned around the launch, in 2017, of a community of developers and users of the Animal Health Surveillance Ontology (AHSO). Development is expected to be community driven and the final product will be open-access.

Knowledge Sharing is a key enabler of development of the ru- ral poor. ICTs can play a critical role, providing for instance market data or weather information to sustenance farmers, or education to children in remote areas. While advanced knowledge technology has proven its use in many applications in the so-called developed world most of the tools cannot be easily applied in developing countries, because of restricted infrastructure, unsuitable modes of communication or ignorance of the local context. In the K4D tutorial at EKAW 2014 we argued that a new of kind of research in Knowledge Engineering is needed in order to make knowledge technology useful outside privileged developed countries. This research will have to include existing social and economic structures as fundamental requirements in order to be successful. Finally, we claim that this holds for a broader spectrum of subdisciplines of Computer Science, and not just for Knowledge Engineering, which lets us advocate Development Informatics: a joint forum for CS researchers who try to make their research relevant for the developing world as well. 1

This paper provides a summary of the Social Media and Linked Data for Emergency Response (SMILE) workshop, co-located with the Extended Semantic Web Conference, at Montpellier, France, 2013. Following paper presentations and question answering sessions, an extensive discussion and roadmapping session was organised which involved the workshop chairs and attendees. Three main topics guided the discussion - challenges, opportunities and showstoppers. In this paper, we present our roadmap towards effectively exploiting social media and semantic web techniques for emergency response and crisis management.

The lightweight ontology language OWL RL is used for reasoning with large amounts of data. To this end, the W3C standard provides a simple system of deduction rules, which operate directly on the RDF syntax of OWL. Several similar systems have been studied. However, these approaches are usually complete for instance retrieval only. This paper asks if and how such methods could also be used for computing entailed subclass relationships. Checking entailment for arbitrary OWL RL class subsumptions is co-NP-hard, but tractable rule-based reasoning is possible when restricting to subsumptions between atomic classes. Surprisingly, however, this cannot be achieved in any RDF-based rule system, i.e., the W3C calculus cannot be extended to compute all atomic class subsumptions. We identify syntactic restrictions to mitigate this problem, and propose a rule system that is sound and complete for many OWL RL ontologies. © 2012 Springer-Verlag Berlin Heidelberg.

We describe an optimised consequence-based procedure for classification of ontologies expressed in a polynomial fragment ℰℒℋ ℛ+ of the OWL 2 EL profile. A distinguishing property of our procedure is that it can take advantage of multiple processors/cores, which increasingly prevail in computer systems. Our solution is based on a variant of the 'given clause' saturation algorithm for first-order theorem proving, where we assign derived axioms to 'contexts' within which they can be used and which can be processed independently. We describe an implementation of our procedure within the Java-based reasoner ELK. Our implementation is light-weight in the sense that an overhead of managing concurrent computations is minimal. This is achieved by employing lock-free data structures and operations such as 'compare-and-swap'. We report on preliminary experimental results demonstrating a substantial speedup of ontology classification on multi-core systems. In particular, one of the largest and widely-used medical ontologies SNOMED CT can be classified in as little as 5 seconds. © 2011 Springer-Verlag.

This paper introduces the MetaLex Document Server (MDS), an ongoing project to improve access to legal sources (regulations, court rulings) by means of a generic legal XML syntax (CEN MetaLex) and Linked Data. The MDS defines a generic conversion mechanism from legacy legal XML syntaxes to CEN MetaLex, RDF and Pajek network files, and discloses content by means of HTTP-based content negotiation, a SPARQL endpoint and a basic search interface. MDS combines a transparent (versioned) and opaque (content-based) naming scheme for URIs of parts of legal texts, allowing for tracking of version information at the URI-level, as well as reverse engineering of versioned metadata from sources that provide only partial information, such as many web-based legal content services. The MDS hosts all 28k national regulations of the Netherlands available since May 2011, comprising some 100M triples.

When ontological knowledge is acquired automatically, quality control is essential. We consider the tightest possible approach - an exhaustive manual inspection of the acquired data. By using automated reasoning, we partially automate the process: after each expert decision, axioms that are entailed by the already approved statements are automatically approved, whereas axioms that would lead to an inconsistency are declined. Adequate axiom ranking strategies are essential in this setting to minimize the amount of expert decisions. In this paper, we present a generalization of the previously proposed ranking techniques which works well for arbitrary validity ratios - the proportion of valid statements within a dataset - whereas the previously described ranking functions were either tailored towards validity ratios of exactly 100% and 0% or were optimizing the worst case. The validity ratio - generally not known a priori - is continuously estimated over the course of the inspection process. We further employ partitioning techniques to significantly reduce the computational effort. We provide an implementation supporting all these optimizations as well as featuring a user front-end for successive axiom evaluation, thereby making our proposed strategy applicable to practical scenarios. This is witnessed by our evaluation showing that the novel parameterized ranking function almost achieves the maximum possible automation and that the computation time needed for each reasoning-based, automatic decision is reduced to less than one second on average for our test dataset of over 25,000 statements. © 2011 Springer-Verlag.

The need for scalable query answering often forces Semantic Web applications to use incomplete OWL 2 reasoners, which in some cases fail to derive all answers to a query. This is clearly undesirable, and in some applications may even be unacceptable. To address this problem, we investigate the problem of 'repairing' an ontology T - that is, computing an ontology R such that a reasoner that is incomplete for T becomes complete when used with T ∪ R. We identify conditions on T and the reasoner that make this possible, present a practical algorithm for computing R, and present a preliminary evaluation which shows that, in some realistic cases, repairs are feasible to compute, reasonable in size, and do not significantly affect reasoner performance. © 2011 Springer-Verlag.

Metadata vocabularies provide various semantic relations between concepts. For content-based recommender systems, these relations enable a wide range of concepts to be recommended. However, not all semantically related concepts are interesting for end users. In this paper, we identified a number of semantic relations, which are within one vocabulary (e.g. a concept has a broader/narrower concept) and across multiple vocabularies (e.g. an artist is associated to an art style). Our goal is to investigate which semantic relations are useful for recommendations of art concepts and to look at the combined use of artwork features and semantic relations in sequence. These sequences of ratings allow us to derive some navigation patterns from users, which might enhance the accuracy of recommendations and be reused for other recommender systems in similar domains. We tested the CHIP demonstrator, called the Art Recommender with end users by recommending both semanticallyrelated concepts and artworks features (e.g.creator, material, subject).

In 1985 and 1986, a SW Finnish archipelago area was monitored for fish (using gillnets and seine), in order to investigate potential effects of salmonid fish farming on natural fish communities (population- and community level, growth and food choice). The results indicate some structural change eg. in total abundance and biomass values (increasing), on species composition (towards moore cyprinid-dominated communities), on growth rate (increasing) and food choice (increased proportions of fish consumed) of perch, and a general shift towards communities dominated by juveniles and small fish in areas influenced by fish farms.

The coastal and archipelago waters of the northern Baltic Sea are influenced by increasing eutrophication (significantly increasing temporal trends in nutrient availability). Increased nutrient levels and pelagic primary production have led to decreased transparency in the water body, increased plankton biomass, and increased amounts of filamentous algae. Large-scale and long-term (15-30 years) comparisons have shown that zoobenthic communities have changed significantly (altered species diversity, increased total abundance and increased biomass). The stress in the ecosystem is also illustrated at the population level (Macoma balthica). An increasing trend is shown for a near-shore fish community, where the increase in biomass has been linear over the past twenty years. Significant predictability of the regression has been shown for 1989-1994. Some areas show signs of community recovery, or changes attributable to natural variation, whereas other nearby areas may display opposite trends. The paper illustrates: (a) overall long-term changes (abiotic: nutrients and turbidity; and biotic: primary production, zoobenthos and fish); (b) site- specific long-term dynamics (infaunal and fish communities); and (c) cases of alternating long-term recovery and deterioration (water quality, zoobenthos).

Much information is conveyed within tables, which can be semantically annotated by humans or (semi)automatic approaches. Nevertheless, many applications cannot take full advantage of semantic annotations because of the low quality. A few methodologies exist for the quality assessment of semantic annotation of tabular data, but they do not automatically assess the quality as a multidimensional concept through different quality dimensions. The quality dimensions are implemented in STILTool 2, a web application to automate the quality assessment of the annotations. The evaluation is carried out by comparing the quality of semantic annotations with gold standards. The work presented here has been applied to at least three use cases. The results show that our approach can give us hints about the quality issues and how to address them.

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs., Comment: Revision from v5: Correcting errata from previous version for entailment/models, and some other minor typos

A new transparency model with more and better corporate data is necessary to promote sustainable economic growth. In particular, there is a need to link factors regarding non-nancial performance of corporations - such as social and environmental impacts, both positive and negative - into decision-making processes of investors and other stakeholders. To do this, we need to develop better ways to access and analyse corporate social, environmental and nancial performance information, and to link together insights from these dierent sources. Such sources are already on the web in non-structured and structured data formats, a big part of them in XBRL (Extensible Business Reporting Language). This study is about promoting solutions to drive eective transparency for a sustainable economy, given the current adoption of XBRL, and the new opportunities that Linked Data can oer. We present (1) a methodology to formalise XBRL as RDF using Linked data principles and (2) demonstrate its usefulness through a use case connecting and making the data accessible.

Smart Contracts have emerged as a novel way to automate the execution of contracts in a decentralised and secure environment, minimising the risk of breach or non-compliance. However, recent research points out that the same measures that secure Smart Contracts against disruption with the purpose of breach makes altering the terms, rights and conditions of contracts difficult. The same research proposes a set of standards inspired in paper-contract law that Smart Contract platforms should implement to enable Smart Contract Undo and Alteration. This paper is about preliminary work on describing terms, rights, and conditions of Smart Contracts as RDF documents linked to them, levering Semantic Web tools enabling: (i) Definition and checking of complex rights and conditions (ii) Separation of the terms of the contract from its execution logic. (iii) Querying and Updating via SPARQL (iv) Alteration of terms that were not initially considered as modifiable.

Lean kernels (LKs) are an effective optimization for deriving the causes of unsatisfiability of a propositional formula. Interestingly, no analogous notion exists for explaining consequences of description logic (DL) ontologies. We introduce LKs for DLs using a general notion of consequence-based methods, and provide an algorithm for computing them which incurs in only a linear time overhead. As an example, we instantiate our framework to the DL \({\mathcal {ALC}}\). We prove formally and empirically that LKs provide a tighter approximation of the set of relevant axioms for a consequence than syntactic locality-based modules.

Inspired by insights from theories of human reasoning and language, we propose additions to the Manchester OWL Syntax to improve comprehensibility. These additions cover: functional and inverse functional properties, negated conjunction, the definition of exceptions, and existential and universal restrictions. By means of an empirical study, we demonstrate the effectiveness of a number of these additions, in particular: the use of solely to clarify the uniqueness of the object in a functional property; the replacement of and with intersection in conjunction, which was particularly beneficial in negated conjunction; the use of except as a substitute for and not; and the replacement of some with including and only with noneOrOnly, which helped in certain situations to clarify the nature of these restrictions.

We describe an optimised consequence-based procedure for classification of ontologies expressed in a polynomial fragment ℰℒℋ ℛ+ of the OWL 2 EL profile. A distinguishing property of our procedure is that it can take advantage of multiple processors/cores, which increasingly prevail in computer systems. Our solution is based on a variant of the 'given clause' saturation algorithm for first-order theorem proving, where we assign derived axioms to 'contexts' within which they can be used and which can be processed independently. We describe an implementation of our procedure within the Java-based reasoner ELK. Our implementation is light-weight in the sense that an overhead of managing concurrent computations is minimal. This is achieved by employing lock-free data structures and operations such as 'compare-and-swap'. We report on preliminary experimental results demonstrating a substantial speedup of ontology classification on multi-core systems. In particular, one of the largest and widely-used medical ontologies SNOMED CT can be classified in as little as 5 seconds. © 2011 Springer-Verlag.