Ontology Module Extraction via Datalog Reasoning

Module extraction - the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature S - has found many applications in recent years. Extracting modules of minimal size is, however, computationally hard, and often algorithmically infeasible. Thus, practical techniques are based on approximations, where M provably captures the relevant entailments, but is not guaranteed to be minimal. Existing approximations, however, ensure that M preserves all second-order entailments of T w.r.t. S, which is stronger than is required in many applications, and may lead to large modules in practice. In this paper we propose a novel approach in which module extraction is reduced to a reasoning problem in datalog. Our approach not only generalises existing approximations in an elegant way, but it can also be tailored to preserve only specific kinds of entailments, which allows us to extract significantly smaller modules. An evaluation on widely-used ontologies has shown very encouraging results.Comment: 13 pages. To appear in AAAI-1

Modular Web Queries — From Rules to Stores

Even with all the progress in Semantic technology, accessing Web data remains a challenging issue with new Web query languages and approaches appearing regularly. Yet most of these languages, including W3C approaches such as XQuery and SPARQL, do little to cope with the explosion of the data size and schemata diversity and richness on the Web. In this paper we propose a straightforward step toward the improvement of this situation that is simple to realize and yet effective: Advanced module systems that make partitioning of (a) the evaluation and (b) the conceptual design of complex Web queries possible. They provide the query programmer with a powerful, but easy to use high-level abstraction for packaging, encapsulating, and reusing conceptually related parts (in our case, rules) of a Web query. The proposed module system combines ease of use thanks to a simple core concept, the partitioning of rules and their consequences in flexible “stores”, with ease of deployment thanks to a reduction semantics. We focus on extending the rule-based Semantic Web query language Xcerpt with such a module system though the same approach can be applied to other (rule-based) languages as well

Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming

Building rules on top of ontologies is the ultimate goal of the logical layer of the Semantic Web. To this aim an ad-hoc mark-up language for this layer is currently under discussion. It is intended to follow the tradition of hybrid knowledge representation and reasoning systems such as $\mathcal{AL}$-log that integrates the description logic $\mathcal{ALC}$ and the function-free Horn clausal language \textsc{Datalog}. In this paper we consider the problem of automating the acquisition of these rules for the Semantic Web. We propose a general framework for rule induction that adopts the methodological apparatus of Inductive Logic Programming and relies on the expressive and deductive power of $\mathcal{AL}$-log. The framework is valid whatever the scope of induction (description vs. prediction) is. Yet, for illustrative purposes, we also discuss an instantiation of the framework which aims at description and turns out to be useful in Ontology Refinement. Keywords: Inductive Logic Programming, Hybrid Knowledge Representation and Reasoning Systems, Ontologies, Semantic Web. Note: To appear in Theory and Practice of Logic Programming (TPLP)Comment: 30 pages, 6 figure

Reasoning over Ontologies with Hidden Content: The Import-by-Query Approach

There is currently a growing interest in techniques for hiding parts of the signature of an ontology Kh that is being reused by another ontology Kv. Towards this goal, in this paper we propose the import-by-query framework, which makes the content of Kh accessible through a limited query interface. If Kv reuses the symbols from Kh in a certain restricted way, one can reason over Kv U Kh by accessing only Kv and the query interface. We map out the landscape of the import-by-query problem. In particular, we outline the limitations of our framework and prove that certain restrictions on the expressivity of Kh and the way in which Kv reuses symbols from Kh are strictly necessary to enable reasoning in our setting. We also identify cases in which reasoning is possible and we present suitable import-by-query reasoning algorithms

05371 Abstracts Collection -- Principles and Practices of Semantic Web Reasoning

From 11.09.05 to 16.09.05, the Dagstuhl Seminar 05371 ``Principles and Practices of Semantic Web Reasoning\u27\u27 % generate automaticall was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Subset reasoning for event-based systems

In highly dynamic domains such as the Internet of Things (IoT), smart industries, smart manufacturing, pervasive health or social media, data is being continuously generated. By combining this generated data with background knowledge and performing expressive reasoning upon this combination, meaningful decisions can be made. Furthermore, this continuously generated data typically originates from multiple heterogeneous sources. Ontologies are ideal for modeling the domain and facilitates the integration of heterogeneous produced data with background knowledge. Furthermore, expressive ontology reasoning allows to infer implicit facts and enables intelligent decision making. The data produced in these domains is often volatile. Time-critical systems, such as IoT Nurse Call systems, require timely processing of the produced IoT data. However, there is still a mismatch between volatile data and expressive ontology reasoning, since the incoming data frequency is often higher than the reasoning time. For this reason, we present an approximation technique that allows to extract a subset of data to speed-up the reasoning process. We demonstrate this technique in a Nurse Call proof of concept where the locations of the nurses are tracked and the most suited nurse is selected when the patient launches a call and in an extension of an existing benchmark. We managed to speed up the reasoning process up to 10 times for small datasets and up to more than 1000 times for large datasets

Drawing OWL 2 ontologies with Eddy the editor

In this paper we introduce Eddy, a new open-source tool for the graphical editing of OWL~2 ontologies. Eddy is specifically designed for creating ontologies in Graphol, a completely visual ontology language that is equivalent to OWL~2. Thus, in Eddy ontologies are easily drawn as diagrams, rather than written as sets of formulas, as commonly happens in popular ontology design and engineering environments. This makes Eddy particularly suited for usage by people who are more familiar with diagramatic languages for conceptual modeling rather than with typical ontology formalisms, as is often required in non-academic and industrial contexts. Eddy provides intuitive functionalities for specifying Graphol diagrams, guarantees their syntactic correctness, and allows for exporting them in standard OWL 2 syntax. A user evaluation study we conducted shows that Eddy is perceived as an easy and intuitive tool for ontology specification

Survey over Existing Query and Transformation Languages

A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability of many current Semantic Web approaches to cope with data available in such diverging representation formalisms as XML, RDF, or Topic Maps. A common query language is the first step to allow transparent access to data in any of these formats. To further the understanding of the requirements and approaches proposed for query languages in the conventional as well as the Semantic Web, this report surveys a large number of query languages for accessing XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from all these areas. From the detailed survey of these query languages, a common classification scheme is derived that is useful for understanding and differentiating languages within and among all three areas

vSPARQL: A View Definition Language for the Semantic Web

Translational medicine applications would like to leverage the biological and biomedical ontologies, vocabularies, and data sets available on the semantic web. We present a general solution for RDF information set reuse inspired by database views. Our view definition language, vSPARQL, allows applications to specify the exact content that they are interested in and how that content should be restructured or modified. Applications can access relevant content by querying against these view definitions. We evaluate the expressivity of our approach by defining views for practical use cases and comparing our view definition language to existing query languages