Christoph Pinkel

An Experimental Comparison of RDF Data Management Approaches in a SPARQL Benchmark Scenario

Efficient RDF data management is one of the cornerstones in realizing the Semantic Web vision. In the past, different RDF storage strategies have been proposed, ranging from simple triple stores to more advanced techniques like clustering or vertical partitioning on the predicates. We present an experimental comparison of existing storage strategies on top of the SP2Bench SPARQL performance benchmark suite and put the results into context by comparing them to a purely relational model of the benchmark scenario. We observe that (1) in terms of performance and scalability, a simple triple store built on top of a column-store DBMS is competitive to the vertically partitioned approach when choosing a physical (predicate, subject, object) sort order, (2) in our scenario with real-world queries, none of the approaches scales to documents containing tens of millions of RDF triples, and (3) none of the approaches can compete with a purely relational model. We conclude that future research is necessary to further bring forward RDF data management.

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.

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.

RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration

A major challenge in information management today is the integration of huge amounts of data distributed across multiple data sources. A suggested approach to this problem is ontology-based data integration where legacy data systems are integrated via a common ontology that represents a unified global view over all data sources. However, data is often not natively born using these ontologies. Instead, much data resides in legacy relational databases. Therefore, mappings that relate the legacy relational data sources to the ontology need to be constructed. Recent techniques and systems that automatically construct such mappings have been developed. The quality metrics of these systems are, however, often only based on self-designed benchmarks. This paper introduces a new publicly available benchmarking suite called RODI, which is designed to cover a wide range of mapping challenges in Relational-to-Ontology Data Integration scenarios. RODI provides a set of different relational data sources and ontologies representing a wide range of mapping challenges as well as a scoring function with which the performance of relational-to-ontology mapping construction systems may be evaluated.

How to Best Find a Partner? An Evaluation of Editing Approaches to Construct R2RML Mappings

R2RML defines a language to express mappings from relational data to RDF. That way, applications built on top of the W3C Semantic Technology stack can seamlessly integrate relational data. A major obstacle to using R2RML, though, is the effort for manually curating the mappings. In particular in scenarios that aim to map data from huge and complex relational schemata (e.g., [5]) to more abstract ontologies efficient ways to support the mapping creation are needed.

Enabling semantic access to static and streaming distributed data with optique: demo

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, which can be easily formulated with our visual query formulation system. Optique can then automatically enrich these queries, translate them into a large collection of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment.

DataOps: Seamless End-to-End Anything-to-RDF Data Integration

While individual components for semantic data integration are commonly available, end-to-end solutions are rare. We demonstrate DataOps, a seamless Anything-to-RDF semantic data integration toolkit. DataOps supports the integration of both semantic and non-semantic data from an extensible host of different formats. Setting up data sources end-to-end works in three steps: 1 accessing the data from arbitrary locations in different formats, 2 specifying mappings depending on the data format e.g., R2RML for relational data, and 3 consolidating new data with existing data instances e.g., by establishing owl:sameAs links. All steps are supported through a fully integrated Web interface with configuration forms and different mapping editors. Visitors of the demo will be able to perform all three steps of the integration process.

RODI: Benchmarking relational-to-ontology mapping generation quality

Accessing and utilizing enterprise or Web data that is scattered across multiple data sources is an important task for both applications and users. Ontology-based data integration, where an ontology mediates between the raw data and its consumers, is a promising approach to facilitate such scenarios. This approach crucially relies on useful mappings to relate the ontology and the data, the latter being typically stored in relational databases. A number of systems to support the construction of such mappings have recently been developed. A generic and effective benchmark for reliable and comparable evaluation of the practical utility of such systems would make an important contribution to the development of ontology-based data integration systems and their application in practice. We have proposed such a benchmark, called RODI. In this paper, we present a new version of RODI, which significantly extends our previous benchmark, and we evaluate various systems with it. RODI includes test scenarios from the domains of scientific conferences, geographical data, and oil and gas exploration. Scenarios are constituted of databases, ontologies, and queries to test expected results. Systems that compute relational-to-ontology mappings can be evaluated using RODI by checking how well they can handle various features of relational schemas and ontologies, and how well the computed mappings work for query answering. Using RODI, we conducted a comprehensive evaluation of seven systems.

Interactive Pay as You Go Relational-to-Ontology Mapping

Ontology Based Data Access (OBDA) enables access to relational data with a complex structure through ontologies as conceptual domain models. To this end, mappings are required. A key aim of OBDA is to facilitate access to data with a complex structure. Ironically, though, in todays existing OBDA systems mappings typically need to be compiled by hand, which is a complex and labor intensive task. Additionally, existing semi-automatic mapping approaches suffer from high human effort for cleaning up results. Fully automatic approaches, on the other side, suffer from a lack of precision and/or recall. In setups where the correctness of query results is crucial but the initial human effort must still be kept be small as possible, neither approach is acceptable. This situation calls for a guided, pay as you go feedback process for human mapping validation. We envision a comprehensive suite of methods and techniques that work well with one another in a seamless mapping process and support mapping construction in the context of OBDA. This suite will in part consist of a recombination and adaptation of various existing methods, but will also comprise newly devised algorithms and techniques.

Durchblick - A Conference Assistance System for Augmented Reality Devices

We present Durchblick, a conference assistance system for Augmented Reality devices. We demonstrate a prototype which can deliver context-sensitive event information and recommendations via Google Glass. This prototype incorporates semantic data from user-specific and public sources to build user profiles, maintains rich context information and employs event processing as well as recommender systems to proactively select and present relevant information.