Evgeny Kharlamov

Evolution of DL-lite knowledge bases

We study the problem of evolution for Knowledge Bases (KBs) expressed in Description Logics (DLs) of the DL-Lite family. DL-Lite is at the basis of OWL 2 QL, one of the tractable fragments of OWL 2, the recently proposed revision of the Web Ontology Language. We propose some fundamental principles that KB evolution should respect. We review known model and formula-based approaches for evolution of propositional theories. We exhibit limitations of a number of model-based approaches: besides the fact that they are either not expressible in DL-Lite or hard to compute, they intrinsically ignore the structural properties of KBs, which leads to undesired properties of KBs resulting from such an evolution. We also examine proposals on update and revision of DL KBs that adopt the model-based approaches and discuss their drawbacks. We show that known formula-based approaches are also not appropriate for DL-Lite evolution, either due to high complexity of computation, or because the result of such an action of evolution is not expressible in DL-Lite. Building upon the insights gained, we propose two novel formula-based approaches that respect our principles and for which evolution is expressible in DL-Lite. For our approaches we also developed polynomial time algorithms to compute evolution of DL-Lite KBs.

How Semantic Technologies Can Enhance Data Access at Siemens Energy

We present a description and analysis of the data access challenge in the Siemens Energy. We advocate for Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. Finally, we describe our preliminary installation and evaluation of the platform in Siemens.

Optique: Towards OBDA Systems for Industry

The recently started EU FP7-funded project Optique will develop an end-to-end OBDA system providing scalable end-user access to industrial Big Data stores. This paper presents an initial architectural specification for the Optique system along with the individual system components.

OptiqueVQS: towards an ontology-based visual query system for big data

A recent EU project, named Optique, with a strong industrial perspective, strives to enable scalable end-user access to Big Data. To this end, Optique employs an ontology-based approach, along with other techniques such as query optimisation and parallelisation, for scalable query formulation and evaluation. In this paper, we specifically focus on end-user visual query formulation, demonstrate our preliminary ontology-based visual query system (i.e., interface), and discuss initial insights for alleviating the affects of Big Data.

BootOX: Practical Mapping of RDBs to OWL 2

Ontologies have recently became a popular mechanism to expose relational database RDBs due to their ability to describe the domain of data in terms of classes and properties that are clear to domain experts. Ontological terms are related to the schema of the underlying databases with the help of mappings, i.e., declarative specifications associating SQL queries to ontological terms. Developing appropriate ontologies and mappings for given RDBs is a challenging and time consuming task. In this work we present BootOX, a system that aims at facilitating ontology and mapping development by their automatic extraction i.e., bootstrapping from RDBs, and our experience with the use of BootOX in industrial and research contexts. BootOX has a number of advantages: it allows to control the OWL 2 profile of the output ontologies, bootstrap complex and provenance mappings, which are beyond the W3C direct mapping specification. Moreover, BootOX allows to import pre-existing ontologies via alignment.

SemFacet: semantic faceted search over yago

In this paper we demonstrate a system SemFacet, that is a proof of concept prototype for our semantic faceted search approach. SemFacet is implemented on top of the Yago knowledge base, powered by the OWL 2 RL triple store RDFox, and the full text search engine Lucene. SemFacet has provided very encouraging results. Via logical reasoning SemFacet can automatically (i) extract facets, (ii) update the faceted query interface with facets relevant for the current stage of the users query constructions session. SemFacet supports faceted queries that are much more expressive than the ones of traditional faceted search applications; in particular SemFacet allows to (i) relate several collections of documents, and (ii) change the focus of queries (and, thus, SemFacet provides control over the documents in the query output to be displayed on the screen). Our approach is fully declarative: the same backend implementation can be used to power faceted search over any application, provided that metadata and knowledge are represented in RDF and OWL 2.

Aggregate queries over ontologies

Answering queries over ontologies is an important issue for the Semantic Web. Aggregate queries were widely studied for relational databases but almost no results are known for aggregate queries over ontologies. In this work we investigate the latter problem. We propose syntax and semantics for epistemic aggregate queries over ontologies and study query answering for MAX, MIN, COUNT, CNTD, SUM, AVG queries for the ontology language DL-LiteA.

Faceted Search over Ontology-Enhanced RDF Data

An increasing number of applications rely on RDF, OWL 2, and SPARQL for storing and querying data. SPARQL, however, is not targeted towards end-users, and suitable query interfaces are needed. Faceted search is a prominent approach for end-user data access, and several RDF-based faceted search systems have been developed. There is, however, a lack of rigorous theoretical underpinning for faceted search in the context of RDF and OWL 2. In this paper, we provide such solid foundations. We formalise faceted interfaces for this context, identify a fragment of first-order logic capturing the underlying queries, and study the complexity of answering such queries for RDF and OWL 2 profiles. We then study interface generation and update, and devise efficiently implementable algorithms. Finally, we have implemented and tested our faceted search algorithms for scalability, with encouraging results.

Capturing model-based ontology evolution at the instance level: The case of DL-Lite

Evolution of Knowledge Bases (KBs) expressed in Description Logics (DLs) has gained a lot of attention lately. Recent studies on the topic have mostly focused on so-called model-based approaches (MBAs), where the evolution of a KB results in a set of models. For KBs expressed in tractable DLs, such as those of the DL-Lite family, which we consider here, it has been shown that one faces inexpressibility of evolution, i.e., the result of evolution of a DL-Lite KB in general cannot be expressed in DL-Lite, in other words, DL-Lite is not closed under evolution. What is still missing in these studies is a thorough understanding of various important aspects of the evolution problem for DL-Lite KBs: Which fragments of DL-Lite are closed under evolution? What causes the inexpressibility? Can one approximate evolution in DL-Lite, and if yes, how? This work provides some understanding of these issues for an important class of MBAs, which cover the cases of both update and revision. We describe what causes inexpressibility, and we propose techniques (based on what we call prototypes) that help to approximate evolution under the well-known approach by Winslett, which is inexpressible in DL-Lite. We also identify a fragment of DL-Lite closed under evolution, and for this fragment we provide polynomial-time algorithms to compute or approximate evolution results for various MBAs.

Ontology-Based Integration of Streaming and Static Relational Data with Optique

Real-time processing of data coming from multiple heterogeneous data streams and static databases is a typical task in many industrial scenarios such as diagnostics of large machines. A complex diagnostic task may require a collection of up to hundreds of queries over such data. Although many of these queries retrieve data of the same kind, such as temperature measurements, they access structurally different data sources. In this work we show how Semantic Technologies implemented in our system optique can simplify such complex diagnostics by providing an abstraction layer---ontology---that integrates heterogeneous data. In a nutshell, optique allows complex diagnostic tasks to be expressed with just a few high-level semantic queries. The system can then automatically enrich these queries, translate them into a collection with a large number of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment. We will demo the benefits of optique on a real world scenario from Siemens.