The Web and its Semantic extension (i.e. Linked Open Data) contain open global-scale knowledge and make it available to potentially intelligent machines that want to benefit from it. Nevertheless, most of Linked Open Data lack ontological distinctions and have sparse axiomatisation. For example, distinctions such as whether an entity is inherently a class or an individual, or whether it is a physical object or not, are hardly expressed in the data, although they have been largely studied and formalised by foundational ontologies (e.g. DOLCE, SUMO). These distinctions belong to common sense too, which is relevant for many artificial intelligence tasks such as natural language understanding, scene recognition, and the like. There is a gap between foundational ontologies, that often formalise or are inspired by pre-existing philosophical theories and are developed with a top-down approach, and Linked Open Data that mostly derive from existing databases or crowd-based effort (e.g. DBpedia, Wikidata). We investigate whether machines can learn foundational distinctions over Linked Open Data entities, and if they match common sense. We want to answer questions such as "does the DBpedia entity for dog refer to a class or to an instance?". We report on a set of experiments based on machine learning and crowdsourcing that show promising results.

When ontologies cover overlapping topics, the overlap can be represented using ontology alignments. These alignments need to be continuously adapted to changing ontologies. Especially for large ontologies this is a costly task often consisting of manual work. Finding changes that do not lead to an adaption of the alignment can potentially make this process significantly easier. This work presents an approach to finding these changes based on RDF embeddings and common classification techniques. To examine the feasibility of this approach, an evaluation on a real-world dataset is presented. In this evaluation, the best classifiers reached a precision of 0.8.

This paper reconstructs the Freebase data dumps to understand the underlying ontology behind Google's semantic search feature. The Freebase knowledge base was a major Semantic Web and linked data technology that was acquired by Google in 2010 to support the Google Knowledge Graph, the backend for Google search results that include structured answers to queries instead of a series of links to external resources. After its shutdown in 2016, Freebase is contained in a data dump of 1.9 billion Resource Description Format (RDF) triples. A recomposition of the Freebase ontology will be analyzed in relation to concepts and insights from the literature on classification by Bowker and Star. This paper will explore how the Freebase ontology is shaped by many of the forces that also shape classification systems through a deep dive into the ontology and a small correlational study. These findings will provide a glimpse into the proprietary blackbox Knowledge Graph and what is meant by Google's mission to "organize the world's information and make it universally accessible and useful".

In the classical database management systems (DBMS) setting, a query Q is evaluated against a database D. However, it has come to the attention of the database community the necessity to also include ontological reasoning and description logics (DLs) along with standard database techniques (Calvanese et al. 2007). As such, the ontological database management systems (ODBMS) has arised. In ODBMS, the classical database is enhanced with an ontology (Baader et al. 2016) in the form of logical assertions that generate new intensional knowledge. An expressive form of such logical assertions is the so-called tuplegenerating dependencies (TGDs), i.e., Horn rules extended by allowing existential quantifiers to appear in the rule heads (Cabibbo 1998; Patel-Schneider and Horrocks 2007; Calì, Gottlob, and Lukasiewicz 2009). Queries are evaluated against a database D and set of TGDs Σ (i.e., D Σ) rather than just D, as in the classical setting. Since for a given database D, a set Σ of TGDs, and a conjunctive query Q, the problem of determining if D Σ Q, i.e., the conjunctive query answering (CQ-Ans) problem, is undecidable in general (Beeri and Vardi 1981; Baget et al. 2011; Rosati 2011; Calì, Gottlob, and Pieris 2012; Calì, Gottlob, and Kifer 2013), a major research effort has been put forth to identifying syntactic conditions on TGDs for which CQ-Ans is decidable.

We introduce the Florida Annotated Corpus for Translational Science (FACTS), which currently consists of 20 case reports about hypertension that we annotated with Vital Sign Ontology (VSO) classes. We describe the annotation method, the annotation results, interannotator agreement measure, and the availability of the corpus and supporting tools for annotating corpora with OWL ontologies. We also discuss issues and limitations of VSO for annotating vital sign data in case reports.

Nowadays, it is well accepted that formal ontologies are commonly used as support for the axiomatisation of the knowledge describing a domain of interest. In particular, for domains in the engineering area where concepts are well mastered by the different stakeholders, ontologies play a major role for knowledge exchange and heterogeneity reduction. Meanwhile, we observe that defining a formal framework for integrating both ontologies represented by knowledge models and design models of particular systems did not draw the attention of many researchers in system engineering. Approaches like those of [3][4][5][7][9][12] supporting the integration of both ontologies and design models contribute to strengthen these design models by offering the capability to design models to borrow knowledge from ontologies, using a particular annotation relationship. As a consequence, the design models are enriched and strengthened with axioms, theorems or invariants issued from the used ontologies. This paper presents a summary of the work achieved in the context of the French ANR IMPEX research project. Ontologies are formalised as theories with axioms, theorems and reasoning rules. Event-B [1] has been chosen as the ground formal modelling technique for all our developments.

Microbiology research has access to a very large amount of public information on the habitats of microorganisms. Many areas of microbiology research uses this information, primarily in biodiversity studies. However the habitat information is expressed in unstructured natural language form, which hinders its exploitation at large-scale. It is very common for similar habitats to be described by different terms, which makes them hard to compare automatically, e.g. intestine and gut. The use of a common reference to standardize these habitat descriptions as claimed by (Ivana et al., 2010) is a necessity. We propose the ontology called OntoBiotope that we have been developing since 2010. The OntoBiotope ontology is in a formal machine-readable representation that enables indexing of information as well as conceptualization and reasoning.

The need for a massive symbolic artificial intelligence project of this ilk was born in the early 1980s out of a large number of experiences early AI researchers had, in the previous 25 years, wherein their AI programs would generate encouraging early results but then fail to "scale up"--fail to cope with novel situations and problems outside the narrow area they were conceived and engineered to cope with. Douglas Lenat and Alan Kay publicized this need,[1][2][3] and organized a meeting at Stanford in 1983 to consider the problem; the back-of-the-envelope calculations by them and colleagues including Marvin Minsky, Allen Newell, Edward Feigenbaum, and John McCarthy indicated that that effort would require between 1000 and 3000 person-years of effort, hence not fit into the standard academic project model. Fortuitously, events within a year of that meeting enabled that Manhattan-Project-sized effort to get underway. The project was started in July,1984 as the flagship project of the 400-person Microelectronics and Computer Technology Corporation, a research consortium started by two dozen large United States based corporations "to counter a then ominous Japanese effort in AI, the so-called "fifth-generation" project."[4] The US Government reacted to the Fifth Generation threat by passing the National Cooperative Research Act of 1984, which for the first time allowed US companies to "collude" on long-term high-risk high-payoff research, and MCC and Sematech sprang up to take advantage of that ten-year opportunity.

Conceptual formalism supported by typical ontologies may not be sufficient to represent uncertainty information which is caused due to the lack of clear cut boundaries between concepts of a domain. Fuzzy ontologies are proposed to offer a way to deal with this uncertainty. This paper describes the state of the art in developing fuzzy ontologies. The survey is produced by studying about 35 works on developing fuzzy ontologies from a batch of 100 articles in the field of fuzzy ontologies. 1. Introduction Ontology is an explicit, formal specification of a shared conceptualization in a human understandable, machinereadable format. Ontologies are the knowledge backbone for many intelligent and knowledge based systems [1, 2].

We present a consequence-based calculus for concept subsumption and classification in the description logic ALCHOIQ, which extends ALC with role hierarchies, inverse roles, number restrictions, and nominals. By using standard transformations, our calculus extends to SROIQ, which covers all of OWL 2 DL except for datatypes. A key feature of our calculus is its pay-as-you-go behaviour: unlike existing algorithms, our calculus is worst-case optimal for all the well-known proper fragments of ALCHOIQ, albeit not for the full logic.