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Logic programming has always been a major ontology modeling paradigm, and is frequently being used in large research projects and industrial applications, e.g., by means of the F-Logic reasoning engine OntoBroker or the TRIPLE query, inference, and transformation language and system. At the same time, the Web Ontology Language OWL has been recommended by the W3Cfor modeling ontologies for the web. Naturally, it is desirable to investigate the interoperability between both paradigms. In this paper, we do so by studying an expressive fragement of OWL DL for which reasoning can be reduced to the evaluation of Horn logic programs. Building on the KAON 2 algorithms for transforming OWL DL into disjunctive Datalog, we give a detailed account of how and to what extent OWL DL can be employed in standard logic programming systems. En route, we derive a novel, simplified characterization of the supported fragment of OWL DL. © 2006 IEEE.

In this paper the so-called Event Cube is introduced, a multidimensional data structure that can hold information about all business dimensions. Like the data cubes of online analytic processing (OLAP) systems, the Event Cube can be used to improve the business analysis quality by providing immediate results under different levels of abstraction. An exploratory analysis of the application of process mining on multidimensional process data is the focus of this paper. The feasibility and potential of this approach is demonstrated through some practical examples.

We propose a description-logic style extension of OWL 2 with nominal schemas which can be used like "variable nominal classes"within axioms. This feature allows ontology languages to express arbitrary DL-safe rules (as expressible in SWRL or RIF) in their native syntax. We show that adding nominal schemas to OWL 2 does not increase the worst-case reasoning complexity, and we identify a novel tractable language SROELV3(?,×) that is versatile enough to capture the lightweight languages OWL EL and OWL RL. Copyright © 2011 by the Association for Computing Machinery, Inc. (ACM).

Symbolic Systems in Artificial Intelligence which are based on formal logic and deductive reasoning are fundamentally different from Artificial Intelligence systems based on artificial neural networks, such as deep learning approaches. The difference is not only in their inner workings and general approach, but also with respect to capabilities. Neural-symbolic Integration, as a field of study, aims to bridge between the two paradigms. In this paper, we will discuss neural-symbolic integration in its relation to the Semantic Web field, with a focus on promises and possible benefits for both, and report on some current research on the topic.

Neuro-symbolic integration is a current field of investigation in which symbolic approaches are combined with deep learning ones. In this work we start from simple non-relational knowledge that can be extracted from text by considering the co-occurrence of entities inside textual corpora; we show that we can easily integrate this knowledge with Logic Tensor Networks (LTNs), a neuro-symbolic model. Using LTNs it is possible to integrate axioms and facts with commonsense knowledge represented in a sub-symbolic form in one single model performing well in reasoning tasks. In spite of some current limitations, we show that results are promising.

Nowadays, a website is used to disseminate information about an event (e.g., location, dates, time). In the academic world, it is common to develop a website for an event, such as workshops or conferences. Aligning with the “Web of data”, its dissemination should also happen by publishing the information of the event as a knowledge graph, e.g., via RDF that is available through a SPARQL endpoint or a Triple Patterns Fragment server. However, the RDF generation and website development is not always straightforward and can be time-consuming. In this demo, we present the Semantic Academic-event Dissemination (SAD) Generator for generating RDF and websites for academic events. The generator allows to (i) annotate CSV files that contain academic event data and use the annotations to generate a knowledge graph and (ii) generate a website with the information for the event querying the knowledge graph. We used our generator to generate the RDF and website of a real workshop, the KGB workshop. It can be easily reused by organizers of other academic events by simply providing the event’s information in CSV files.

In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, as well as empirical research. Despite a large number of emerging scientific studies to address the problem, existing methods are limited in several ways, such as the lack of comparative evaluations which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and long short term memory networks, and conducts an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available datasets to date. We show that our proposed method outperforms state of the art on 6 out of 7 datasets by between 0.2 and 13.8 points in F1. We also carry out further analysis using automatic feature selection to understand the impact of the conventional manual feature engineering process that distinguishes most methods in this field. Our findings challenge the existing perception of the importance of feature engineering, as we show that: the automatic feature selection algorithm drastically reduces the original feature space by over 90% and selects predominantly generic features from datasets; nevertheless, machine learning algorithms perform better using automatically selected features than the original features.

Ontology classification - the computation of subsumption hierarchies for classes and properties - is one of the most important tasks for OWL reasoners. Based on the algorithm by Shearer and Horrocks [9], we present a new classification procedure that addresses several open issues of the original algorithm, and that uses several novel optimisations in order to achieve superior performance. We also consider the classification of (object and data) properties. We show that algorithms commonly used to implement that task are incomplete even for relatively weak ontology languages. Furthermore, we show how to reduce the property classification problem into a standard (class) classification problem, which allows reasoners to classify properties using our optimised procedure. We have implemented our algorithms in the OWL HermiT reasoner, and we present the results of a performance evaluation. © 2010 Springer-Verlag.

Recent studies showed how a seamless integration of formal concept analysis (FCA), logic of domains, and answer set programming (ASP) can be achieved. Based on these results for combining hierarchical knowledge with classical rule-based formalisms, we introduce an expressive common-sense query language for formal contexts. Although this approach is conceptually based on order-theoretic paradigms, we show how it can be implemented on top of standard ASP systems. Advanced features, such as default negation and disjunctive rules, thus become practically available for processing contextual data. © Springer-Verlag Berlin Heidelberg 2006.

Translations to (first-order) datalog have been used in a number of inferencing techniques for description logics (DLs), yet the relationship between the semantic expressivities of function-free Horn logic and DL is understood only poorly. Although Description Logic Programs (DLP) have been described as DLs in the "expressive intersection" of DL and datalog, it is unclear what an intersection of two syntactically incomparable logics is, even if both have a first-order logic semantics. In this work, we offer a characterisation for DL fragments that can be expressed, in a concrete sense, in datalog. We then determine the largest such fragment for the DL , and provide an outlook on the extension of our methods to more expressive DLs. © 2010 Springer-Verlag Berlin Heidelberg.

We extend the Semantic Web query language SPARQL by defining the semantics of SPARQL queries under the entailment regimes of RDF, RDFS, and OWL. The proposed extensions are part of the SPARQL 1.1 Entailment Regimes working draft which is currently being developed as part of the W3C standardization process of SPARQL 1.1. We review the conditions that SPARQL imposes on such extensions, discuss the practical difficulties of this task, and explicate the design choices underlying our proposals. In addition, we include an overview of current implementations and their underlying techniques. © 2010 Springer-Verlag.