Artificial Intelligence

The integration of heterogeneous biomedical information is one important step towards providing the level of personalization required in the next generation of healthcare provision. In order to provide the computer-based decision support systems needed to access this integrated healthcare information it will be necessary to handle the semantics of (amongst other things) medical protocols. The EC FP6 Health-e-Child project aims to develop an integrated healthcare platform for European paediatrics and decision support tools to access personalized health information. This paper introduces both the integrated data model in the Health-e-Child project and through a case study using the brain tumour protocols it demonstrates the semantic annotation of patient data acquired in the project using UMLS as the primary source of semantic data.

Ontology matching consists of finding correspondences between ontology entities. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. Test cases can use ontologies of different nature (from expressive OWL ontologies to simple directories) and use different modalities, eg, blind evaluation, open evaluation, consensus. OAEI-2009 builds over previous campaigns by having 5 tracks with 11 test cases followed by 16 participants. This paper is an overall presentation of the OAEI 2009 campaign.

Abstract. In this paper we introduce a new view definition language, named OntoPathView, which combines the simplicity of XPath with the high expressiveness of RDF/S based view languages. This language will allow us to work in a collaborative environment for the development of complex ontologies. Keywords. Semantic Web. Ontologies, View Languages, Knowledge Engineering. Collaborative Environment. Semantic Groups

In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings suffer from logical flaws, their usefulness may be diminished. In this paper we present an approximate method to detect and correct violations to the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. We show that this is indeed the case in our application domain based on the EU Optique project. Additionally, our extensive evaluation conducted with both the Optique use case and the data sets from the Ontology Alignment Evaluation Initiative (OAEI) suggests that our method is both useful and feasible in practice.

In this paper we present the EU Optique project that aims at developing an end-to-end OBDA system for managing Big Data in industries. We discuss limitations of state of the art OBDA systems and present the general architecture of the Optique's OBDA system that aims at overcoming these limitations.

The Optique project aims at providing an end-to-end solution for scalable Ontology-Based Data Access to Big Data integration, where end-users will formulate queries based on a familiar conceptualization of the underlying domain, that is, over an ontology. From user queries the Optique platform will automatically generate appropriate queries over the underlying integrated data, optimize and execute them. The key components in the Optique platform are the ontology and mappings that provide the relationships between the ontology and the underlying data. In this paper we discuss the problem of bootstrapping and maintenance of ontologies and mappings. The important challenge in both tasks is debugging errors in ontologies and mappings. We will present examples of different kinds of error, and give our preliminary view on their debugging.

Accessing the relevant data in Big Data scenarios is increasingly difficult both for end-user and IT-experts, due to the volume, variety, and velocity dimensions of Big Data.This brings a hight cost overhead in data access for large enterprises. For instance, in the oil and gas industry, IT-experts spend 30-70% of their time gathering and assessing the quality of data . The Optique project (http://www.optique-project.eu/) advocates a next generation of the well known Ontology-Based Data Access (OBDA) approach to address the Big Data dimensions and in particular the data access problem. The project aims at solutions that reduce the cost of data access dramatically.

Despite the dramatic growth of data accumulated by enterprises, obtaining value out of it is extremely challenging. In particular, the data access bottleneck prevents domain experts from getting the right piece of data within a constrained time frame. The Optique Platform unlocks the access to Big Data by providing end users support for directly formulating their information needs through an intuitive visual query interface. The submitted query is then transformed into highly optimized queries over the data sources, which may include streaming data, and exploiting massive parallelism in the backend whenever possible. The Optique Platform thus responds to one major challenge posed by Big Data in data-intensive industrial settings.

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.

The Optique European project 3 [6] aims at providing an end-to-end solution for scalable access to Big Data integration, where end users will formulate queries based on a familiar conceptualization of the underlying domain. From the users' queries the Optique platform will automatically generate appropriate queries over the underlying integrated data, optimize and execute them on the Cloud. In this paper we present the distributed query processing engine of the Optique platform. The efficient execution of complex queries posed by end users is an important and challenging task. The engine aims at providing a scalable solution for query execution in the Cloud, and should cope with heterogeneity of data sources as well as with temporal and streaming 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 ontologybased 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.

We demonstrate an ontology-based visual query system, namely OptiqueVQS, for end users without any technical background to formulate rather complex information needs into formal queries over databases. It is built on multiple and coordinated representation and interaction paradigms and a flexible widget-based architecture.

Grounded on real industrial use cases, we recently proposed an ontology-based visual query system for SPARQL, named OptiqueVQS. Ontology-based visual query systems employ ontologies and visual representations to depict the domain of interest and queries, and are promising to enable end users without any technical background to access data on their own. However, even with considerably small ontologies, the number of ontology elements to choose from increases drastically, and hence hinders usability. Therefore, in this paper, we propose a method using the log of past queries for ranking and suggesting query extensions as a user types a query, and identify emerging issues to be addressed.

The Optique project aims at developing an end-to-end system for semantic data access to Big Data in industries such as Statoil ASA and Siemens AG. In our demonstration we present the first version of the Optique system customised for the Norwegian Petroleum Directorate's FactPages, a publicly available dataset relevant for engineers at Statoil ASA. The system provides different options, including visual, to formulate queries over ontologies and to display query answers. Optique 1.0 offers installation wizards that allow to extract ontologies from relational schemata, extract and define mappings connecting ontologies and schemata, and align and approximate ontologies. Moreover, the system offers highly optimised techniques for query answering.

Data access in an enterprise setting is a determining factor for the potential of value creation processes such as sense-making, decision making, and intelligence analysis. As such, providing friendly data access tools that directly engage domain experts (i.e., end-users) with data, as opposed to the situations where database/IT experts are required to extract data from databases, could substantially increase competitiveness and profitability. However, the ever increasing volume, complexity, velocity, and variety of data, known as the Big Data phenomenon, renders the end-user data access problem even more challenging. Optique, an ongoing European project with a strong industrial perspective, aims to countervail the Big Data effect, and to enable scalable end-user data access to traditional relational databases by using an ontology-based approach. In this paper, we specifically present the preliminary design and development of our ontology-based visual query system and discuss directions for addressing the Big Data effect.

Data access in an enterprise setting is a determining factor for value creation processes, such as sense making, decision making, and intelligence analysis. Particularly, in an enterprise setting, intuitive data access tools that directly engage domain experts with data could substantially increase competitiveness and profitability. In this respect, the use of ontologies as a natural communication medium between end users and computers has emerged as a prominent approach. To this end, this article introduces a novel ontology-based visual query system, named OptiqueVQS, for end users. OptiqueVQS is built on a powerful and scalable data access platform and has a user-centric design supported by a widget-based flexible and extensible architecture allowing multiple coordinated representation and interaction paradigms to be employed. The results of a usability experiment performed with non-expert users suggest that OptiqueVQS provides a decent level of expressivity and high usability, and hence is quite promising. Keywords Visual query formulation · visual query systems · ontology-based data access · data retrieval

Value creation in an organisation is a time-sensitive and data-intensive process, yet it is often delayed and bounded by the reliance on IT experts extracting data for domain experts. Hence, there is a need for providing people who are not professional developers with the flexibility to pose relatively complex and ad hoc queries in an easy and intuitive way. In this respect, visual methods for query formulation undertake the challenge of making querying independent of users' technical skills and the knowledge of the underlying textual query language and the structure of data. An ontology is more promising than the logical schema of the underlying data for guiding users in formulating queries, since it provides a richer vocabulary closer to the users' understanding. However, on the one hand, today the most of world's enterprise data reside in relational databases rather than triple stores, and on the other, visual query formulation has become more compelling due to ever-increasing data size and complexity – known as Big Data. This article presents and argues for ontology-based visual query formulation for end users; discusses its feasibility in terms of ontology-based data access, which virtualises legacy relational databases as RDF, and the dimensions of Big Data; presents key conceptual aspects and dimensions, challenges, and requirements; and reviews, categorises, and discusses notable approaches and systems.