Ontology-Based Data Access (OBDA) is an approach to query relational data via a unified semantic access point powered by an ontology that is 'connected' to the underlying databases via mappings. OPTIQUE is an end-to-end OBDA platform. It offers support for semi-automatic bootstrapping of ontologies and mappings from relational databases thus facilitating system deployment, an intuitive interface to pose queries over a deployed system, and a query processing and optimisation module that allows to efficiently answer user queries. In this demonstration attendees will be able to experience OPTIQUE with data from the oil and gas industry and data from the music domain.
In enterprises the ability of domain experts to quickly understand and analyze data is at the core of making accurate business decisions. In many cases this requires an interactive data exploration: domain experts need to access and analyze available data sources directly without involving IT-experts [7]. Challenges in providing such direct data access include the complexity of database schemata that can contain hundreds and thousands of tables, and the conceptual mismatch between the language and structures that the domain experts use to describe the data, and the way the data is described and structured by database schema languages [2, 7, 8].
Ontology-Based Data Access (OBDA) [12] is a prominent approach to end-user oriented direct data access. OBDA provides semantic access to databases via an ontology while leaving the data in its original stores. A virtue of an ontology is that it allows domain experts to express information needs in their own terms without considering the way data is organized in the source, which makes the query formulation task independent from IT-expert involvement. OBDA mappings describe the relationships between the ontological vocabulary and the schema of the underlying data. In OBDA user queries formulated over ontologies are processed in two stages: first, the query is enriched using logical reasoning by compiling relevant parts of the ontology into the query, second, the resulting query is unfolded, i.e., translated into a SQL query using mappings. The resulting SQL query is executed over the underlying data and the obtained answers are returned to the user.
OBDA has recently attracted a lot of attention, e.g., [1, 13], however, to the best of our knowledge no system supports the full OBDA life cycle from system deployment to end user query formulation. In this demo we present OPTIQUE [3, 6], an end-to-end integrated ? This research has been partially supported by the EU project Optique (FP7-IP-318338), the
Royal Society, the EPSRC grants Score!, DBonto, and MaSI3. 2
The three-layer architecture of OPTIQUE is depicted in Figure 1, where double arrows represent a query or data flow, and solid arrows represent a dependency between components: if A points to B, then A can call B. The OPTIQUE implementation is based on the Information Workbench [4], a generic and extensible platform for semantic data management that provides many base components for OPTIQUE and APIs for managing metadata assets.
Answer Visualiser
Query Interface
Query
Constructor Geospacial Visualiser
Streaming
Data Table
Convertor
Ranking, Query logs,
Cashed vals Domain Experts IT-Specialist
Query Transformer NPD FDaacttaPbaagsees
Reasoner Ontologies
Mappings
DB schemata Query Optimiser
Deployment
Interface Bootstrapping Alignment
Layering
Mapping Editor External Ontologies OBDA platform for enterprises that comes with a suit of novel components covering needs of both IT-experts and end users to deploy an OBDA system in an enterprise from scratch, effectively maintain and use it for data access tasks. The demonstration will focus on three features from OPTIQUE platform: (i) System deployment using semi-automatic bootstrapping of ontologies and mappings from relational databases and aligning them with existing ontologies, (ii) Query processing and optimization for efficient query answering, (iii) Visual query formulation for enabling end-user formulation of queries without prior knowledge of the SPARQL language.
During the demo we will allow attendees to experience the above mentioned OPTIQUE components and the platform as a whole on two datasets: the Northwind database, and public data from our work with Statoil [7]. over OWL 2 QL ontologies only, thus, if the bootstrapped or imported OWL ontology is not in QL, then OPTIQUE approximates it to QL.
Query Answering. OPTIQUE’s query processing module is based on our ONTOP [9]
system. The naive implementation of the two stage approach for answer computation
in OBDA performs poorly in practice and optimizations are required [14]. Thus, we
developed a number of techniques to optimize both stages and implemented them in
the OPTIQUE query processing module. Enrichment is optimized by addressing both
the redundancy in the enriched queries and the inefficiency of enrichment computation.
In the former case we minimize the mappings and the enriched queries with respect to
query containment. For the latter, we use a variant of a graph reachability algorithm, we
improve computation of class hierarchies entailed by the ontology, which the enrichment
heavily relies on. Additionally, we move part of online reasoning offline: for all atomic
queries we perform expensive enrichment offline and compile the results of this
computation into the existing mappings, thus, enriching mappings. Unfolding is optimized
by turning large and highly redundant SQL queries returned after the second stage of
query processing into compact and efficiently executable SQL queries. Optimizations
are achieved both structurally, by pushing joins inside the unions and special functions
(such as URI construction) as high as possible in the query tree, and semantically, by
detecting and removing inefficient joins between sub-queries. Experiments show that
these optimization techniques allow us to dramatically outperform existing OBDA query
processing engines [
Query Formulation. The query formulation module is based on our OPTIQUEVQS [16] system. Visually formulated queries are automatically translated into SPARQL which can be sent to the query transformation module. Users can also write queries in SPARQL directly. The query formulation module has a widget-based architecture and exploits multiple representation and interaction paradigms for query composition. In particular, it uses a graph metaphor for navigation between classes via object properties, and faceted search for query refinement via data properties. At each step of the query formulation process ranked suggestions are automatically generated to guide users in constructing the query. The suggestions are generated by reasoning over the ontology and query logs. An important feature of the system is a special treatment of data properties: it automatically generates different end-user oriented representations of data values, including sliders restricting possible ranges of numerical values, such as age, depths, etc., and drop boxes with precomputed lists for categorical data, such as names of companies, geographical locations, etc. The current version of the system allows the construction of tree-shaped conjunctive queries enhanced with simple aggregate functions. 3
Figure 2 contains different screenshots from the OPTIQUE platform applied to our demonstration scenarios. The central screenshot has the main menu of the platform for administering data sources, mappings, ontologies and queries, and performing actions on these: bootstrapping, query transformation setup, general system configuration including query optimization, and visual queries construction. The bottom-left screenshot shows a visual query in the OptiqueVQS, and images on the right show the answers to this query in a table and a map view. The top-left screenshot visualizes the integration of a bootstrapped and an imported ontology. During the demonstration we will present OPTIQUE end-to-end, with the tools and techniques behind these screenshots and more, over two datasets: Northwind DB (northwinddatabase.codeplex.com) is a demo database with easy-tounderstand business data comprising customers, products, orders, employees, etc. It contains a total of 14 tables and 12 referential constraints.
NPD FactPages [15] is a public fragment from our Statoil deployment. This data is heavily used in the oil and gas industry, it consists of 70 tables, 276 different attributes, 96 foreign keys, and about 50 MB of mostly aggregated data and metadata. Exploration of this scenario requires from demo attendees some basic knowledge of the oil and gas domain. 4