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There were 7 papers submitted for peer-review to this workshop. Out of these, 4 papers were accepted for this volume.

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2020 campaign offered 12 tracks with 36 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign.

Once the conceptual overview, in terms of a somewhat informal class diagram, has been designed in the course of engineering an ontology, the process of adding many of the appropriate logical axioms is mostly a routine task. We provide a Protege plugin which supports this task, together with a visual user interface, based on established methods for ontology design pattern modeling., Poster in ISWC 2016

In our experience, some ontology users find it much easier to convey logical statements using rules rather than OWL (or description logic) axioms. Based on recent theoretical developments on transformations between rules and description logics, we develop ROWL, a Protege plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule., Comment: Accepted at ISWC 2016

We propose a novel method for reasoning in the description logic . After a satisfiability preserving transformation from to the description logic , the obtained Tbox is converted into an ordered binary decision diagram (OBDD) which represents a canonical model for . This OBDD is turned into a disjunctive datalog program that can be used for Abox reasoning. The algorithm is worst-case optimal w.r.t. data complexity, and admits easy extensions with DL-safe rules and ground conjunctive queries. © 2008 Springer Berlin Heidelberg.

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.

The OWL 2 profiles are fragments of the ontology language OWL 2 for which standard reasoning tasks are feasible in polynomial time. Many OWL ontologies, however, contain a typically small number of out-of-profile axioms, which may have little or no influence on reasoning outcomes. We investigate techniques for rewriting axioms into the EL and RL profiles of OWL 2. We have tested our techniques on both classification and data reasoning tasks with encouraging results.

We investigate cases where an ontology expressed in a seemingly hard DL can be polynomially reduced to one in a simpler logic, while preserving reasoning outcomes for classification and fact entailment. Our transformations target the elimination of inverse roles, universal and existential restrictions, and in the best case allow us to rewrite the given ontology into one of the OWL 2 profiles. Even if an ontology cannot be fully rewritten into a profile, in many cases our transformations allow us to exploit further optimisation techniques. Moreover, the elimination of some out-of-profile axioms can improve the performance of modular reasoners, such as MORe. We have tested our techniques on both classification and data reasoning tasks with encouraging results.

The goal of neural-symbolic computation is to integrate robust connectionist learning and sound symbolic reasoning. With the recent advances in connectionist learning, in particular deep neural networks, forms of representation learning have emerged. However, such representations have not become useful for reasoning. Results from neural-symbolic computation have shown to offer powerful alternatives for knowledge representation, learning and reasoning in neural computation. This paper recalls the main contributions and discusses key challenges for neural-symbolic integration which have been identified at a recent Dagstuhl seminar.

The role that ontologies play or can play in designing and employing semantic technologies has been widely acknowledged by the Semantic Web and Linked Data communities. But the level of collaboration between these communities and the Applied Ontology community has been much less than expected. And ontologies and ontological techniques appear to be of marginal use in Big Data and its applications. To understand this situation and foster greater collaboration, Ontology Summit 2014 brought together representatives from the Semantic Web, Linked Data, Big Data and Applied Ontology communities, to address three basic problems involving applied ontology and these communities: (1) The role of ontologies [in these communities], (2) Current uses of ontologies in these communities, and (3) Engineering of ontologies and semantic integration. The intent was to identify and understand: (a) causes and challenges (e.g. scalability) that hinder reuse of ontologies in SW and LD, (b) solutions that can reduce the differences between ontologies on and off line, and (c) solutions to overcome engineering bottlenecks in current Semantic Web and Big Data applications. Over the past four months, presentations from, and discussions with, representatives of the Semantic Web, Linked Data, and Applied Ontology communities have taken place across four tracks. Each Track focused on different aspects of this year?s Summit topic: (Track A) Investigation of sharable and reusable ontologies; (Track B) Tools, services and techniques for a comprehensive and effective use of ontologies; (Track C) Investigation of the engineering bottlenecks and the ways to prevent and overcome them; (Track D) Enquiry on the variety problem in Big Data. In addition to the four Tracks? activities there was a Hackathon. Six different Hackathon projects took place, all available at their individual project public repositories. An online Community Library and an online Ontology Repository have been created as freely accessible Community resources. This Ontology Summit 2014 Communique presents a summary of the results, original in its attempt both to merge different communities? discourses and to achieve consensus across the Summit participants with respect to open problems and recommendations to address them. Fil: Obrst, Leo. The MITRE Corporation; Estados Unidos Fil: Gruninger, Michael. University Of Toronto; Canadá Fil: Baclawski, Ken. Northeastern University; Estados Unidos Fil: Bennett, Mike. Hypercube Ltd.; Reino Unido Fil: Brickley, Dan. Google; Reino Unido Fil: Berg Cross, Gary. Knowledge Strategies; Estados Unidos Fil: Hitzler, Pascal. Wright State University; Estados Unidos Fil: Janowicz, Krzysztof. University of California; Estados Unidos Fil: Kapp, Christine. Hypercube Ltd; Estados Unidos Fil: Kutz, Oliver. Otto von Guericke University Magdeburg; Alemania Fil: Lange, Christoph. University of Bonn; Alemania Fil: Levenchuk, Anatoly. TechInvestLab.ru; Rusia Fil: Quattri, Francesca. The Hong Kong Polytechnic University; Hong Kong Fil: Rector, Alan. University Of Manchester; Reino Unido Fil: Schneider, Todd. PDS, Inc.; Estados Unidos Fil: Spero, Simon. University Of North Carolina; Estados Unidos Fil: Thessen, Anne. Arizona State University; Estados Unidos Fil: Vegetti, Maria Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina Fil: Vizedom, Amanda. Criticollab; Estados Unidos Fil: Westerinen, Andrea. Nine Points Solutions; Estados Unidos Fil: West, Matthew. Information Junction; Reino Unido Fil: Yim, Peter. CIM Engineering, Inc.; Estados Unidos

Introduction: Standard straight stems have been recognized as a gold standard implant in the field of hip replacement surgery. However, lately uncemented bone-preserving short stems started to gain more and more popularity. This was reflected in the increasing variety of available models. Up till now, short and mid-term results are available. Patients and methods: In 2002, the cementless short stemmed GHEs was introduced. 380 patients were included in our study between 2002 and 2008. Only GHEs short stems were implanted. The clinical and radiological evaluations were performed in the Orthopaedic Department, Leipzig University Hospitals, on the average of 24 months (3 to 60 months) postoperatively. Results: 365 primary implantations and 15 revision implantations were carried out. Average age 60 years. Favourable clinical and radiological outcome was seen in 361/380 patients (95%). Postoperative complications were seen in 19/380 patients (5%): 8 fissures/fractures (2.1%), 5 infections (1.3%), 4 aseptic loosenings (1.1%), 2 dislocations (0.5%). Conclusions: Short stem implants, including our own experience with GHEs model, are satisfying and promising. They represent a valuable supplementation of the treatment modalities in hip replacement surgery. However, long term results are still awaited., GMS Interdisciplinary Plastic and Reconstructive Surgery DGPW; 2:Doc16; ISSN 2193-8091

Identity relations are at the foundation of the Linked Open Data initiative and on the Semantic Web in general. They allow the interlinking of alternative descriptions of the same thing. However, many practical uses of owl:sameAs are known to violate its formal semantics. We propose a method that assigns meaning to (the subrelations of) an identity relation using the predicates of the dataset schema. Applications of this approach include automated suggestions for asserting/retracting identity pairs and quality assessment. We also describe an experimental design for this approach.

The AAAI-12 Workshop program was held Sunday and Monday, July 22-23, 2012, at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada. The AAAI-12 workshop program included nine workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages, Cognitive Robotics, Grounding Language for Physical Systems, Human Computation, Intelligent Techniques for Web Personalization and Recommendation Systems, Multiagent Pathfinding, Neural-Symbolic Learning and Reasoning, Problem Solving Using Classical Planners, and Semantic Cities. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence.

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 an extension of SROIQ with nominal schemas which can be used like "variable nominal concepts" within axioms. This feature allows us to express arbitrary DL-safe rules in description logic syntax. We show that adding nominal schemas to SROIQ does not increase its worst-case reasoning complexity, and we identify a family of tractable DLs SROELVn that allow for restricted use of nominal schemas.

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).

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.

Conceptual modelling tools allow users to construct formal representations of their conceptualisations. These models are typically developed in isolation, unrelated to other user models, thus losing the opportunity of incorporating knowledge from other existing models or ontologies that might enrich the modelling process. We propose to apply Semantic Web techniques to the context of conceptual modelling (more particularly to the domain of qualitative reasoning), to smoothly interconnect conceptual models created by different users, thus facilitating the global sharing of scientific data contained in such models and creating new learning opportunities for people who start modelling. This paper describes how semantic grounding techniques can be used during the creation of qualitative reasoning models, to bridge the gap between the imprecise user terminology and a well defined external common vocabulary. We also explore the application of ontology matching techniques between models, which can provide valuable feedback during the model construction process.

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.

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.

In this paper we present an approach based on genetic algorithms for determining optimal RDF query paths. The performance of this approach is benchmarked against the performance of a two-phase optimization algorithm. For more complex queries, the genetic algorithm RDFGA generally outperforms two-phase optimization in solution quality, execution time needed, and consistency in performance. Setting a time limit improves the overall performance of RDFGA compared to two-phase optimization even more.

We propose a novel method for reasoning in the description logic . After a satisfiability preserving transformation from to the description logic , the obtained Tbox is converted into an ordered binary decision diagram (OBDD) which represents a canonical model for . This OBDD is turned into a disjunctive datalog program that can be used for Abox reasoning. The algorithm is worst-case optimal w.r.t. data complexity, and admits easy extensions with DL-safe rules and ground conjunctive queries. © 2008 Springer Berlin Heidelberg.

We investigate the possibility of incorporating Boolean role constructors on simple roles into some of today's most popular description logics, focussing on cases where those extensions do not increase complexity of reasoning. We show that the expressive DLs and , serving as the logical underpinning of OWL and the forthcoming OWL 2, can accommodate arbitrary Boolean expressions. The prominent OWL-fragment can be safely extended by safe role expressions, and the tractable fragments and DLP retain tractability if extended by conjunction on roles, where in the case of DLP the restriction on role simplicity can even be discarded. © 2008 Springer-Verlag Berlin Heidelberg.

We introduce as a decidable fragment of the Semantic Web Rule Language (SWRL) that admits reasoning in polynomial time. is based on the tractable description logic , and encompasses an extended notion of the recently proposed DL rules for that logic. Thus extends with a number of features introduced by the forthcoming OWL 2, such as disjoint roles, local reflexivity, certain range restrictions, and the universal role. We present a reasoning algorithm based on a translation of to Datalog, and this translation also enables the seamless integration of DL-safe rules into . While reasoning with DL-safe rules as such is already highly intractable, we show that DL-safe rules based on the Description Logic Programming (DLP) fragment of OWL 2 can be admitted in without losing tractability. © 2008 Springer Berlin Heidelberg.

Conceptual Structures is a field of research which shares abstract concepts and interests with recent work on knowledge representation for the Semantic Web. However, while the latter is an area of research and development which is rapidly expanding in recent years, the former fails to participate in these developments on a large scale. In this paper, we attempt to stimulate the Conceptual Structures community to catch the Semantic Web train. © Springer-Verlag Berlin Heidelberg 2007.

Despite the success of the Web Ontology Language OWL, the development of expressive means for querying OWL knowledge bases is still an open issue. In this paper, we investigate how a very natural and desirable form of queries-namely conjunctive ones-can be used in conjunction with OWL such that one of the major design criteria of the latter-namely decidability-can be retained. More precisely, we show that querying the tractable fragment of OWL 1.1 is decidable. We also provide a complexity analysis and show that querying unrestricted is undecidable. © 2008 Springer-Verlag Berlin Heidelberg.

We report on efficiency evaluations concerning two different approaches to using logic programming for OWL [1] reasoning and show, how the two approaches can be combined. © Springer-Verlag Berlin Heidelberg 2007.

Knowledge based artificial networks networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes it is not obvious at all how neural symbolic systems should look like such that they are truly connectionist and allow for a declarative reading at the same time. The core method aims at such an integration. It is a method for connectionist model generation using recurrent networks with feed-forward core. After an introduction to the core method, this paper will focus on possible connectionist representations of structured objects and their use in structure-sensitive reasoning tasks.

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.

Morphisms constitute a general tool for modelling complex relationships between mathematical objects in a disciplined fashion. In Formal Concept Analysis (FCA), morphisms can be used for the study of structural properties of knowledge represented in formal contexts, with applications to data transformation and merging. In this paper we present a comprehensive treatment of some of the most important morphisms in FCA and their relationships, including dual bonds, scale measures, infomorphisms, and their respective relations to Galois connections. We summarize our results in a concept lattice that cumulates the relationships among the considered morphisms. The purpose of this work is to lay a foundation for applications of FCA in ontology research and similar areas, where morphisms help formalize the interplay among distributed knowledge bases. © Springer-Verlag Berlin Heidelberg 2005.

Formal concept analysis has grown from a new branch of the mathematical field of lattice theory to a widely recognized tool in Computer Science and elsewhere. In order to fully benefit from this theory, we believe that it can be enriched with notions such as approximation by computation or representability. The latter are commonly studied in denotational semantics and domain theory and captured most prominently by the notion of algebraicity, e.g. of lattices. In this paper, we explore the notion of algebraicity in formal concept analysis from a category-theoretical perspective. To this end, we build on the the notion of approximable concept with a suitable category and show that the latter is equivalent to the category of algebraic lattices. At the same time, the paper provides a relatively comprehensive account of the representation theory of algebraic lattices in the framework of Stone duality, relating well-known structures such as Scott information systems with further formalisms from logic, topology, domains and lattice theory., 36 pages

Horn description logics (Horn-DLs) have recently started to attract attention due to the fact that their (worst-case) data complexities are in general lower than their overall (i.e. combined) complexities, which makes them attractive for reasoning with large ABoxes. However, the natural question whether Horn-DLs also provide advantages for TBox reasoning has hardly been addressed so far. In this paper, we therefore provide a thorough and comprehensive analysis of the combined complexities of Horn-DLs. While the combined complexity for many Horn-DLs turns out to be the same as for their non-Horn counterparts, we identify subboolean DLs where Hornness simplifies reasoning. Copyright ©2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

zimmermann2006a; International audience; An ontology alignment is the expression of relations between different ontologies. In order to view alignments independently from the language expressing ontologies and from the techniques used for finding the alignments, we use a category-theoretical model in which ontologies are the objects. We introduce a categorical structure, called V-alignment, made of a pair of morphisms with a common domain having the ontologies as codomain. This structure serves to design an algebra that describes formally what are ontology merging, alignment compo- sition, union and intersection using categorical constructions. This enables combining alignments of various provenance. Although the desirable properties of this algebra make such abstract manipulation of V-alignments very simple, it is practically not well fitted for expressing complex alignments: expressing subsumption between entities of two different ontologies demands the definition of non-standard categories of ontologies. We consider two approaches to solve this problem. The first one extends the notion of V-alignments to a more complex structure called W-alignments: a formalization of alignments relying on "bridge axioms". The second one relies on an elaborate concrete category of ontologies that offers high expressive power. We show that these two extensions have different advantages that may be exploited in different contexts (vi

We propose an extension of SROIQ with nominal schemas which can be used like "variable nominal concepts" within axioms. This feature allows us to express arbitrary DL-safe rules in description logic syntax. We show that adding nominal schemas to SROIQ does not increase its worst-case reasoning complexity, and we identify a family of tractable DLs SROELVn that allow for restricted use of nominal schemas.

The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains. In this paper, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept.