Score!: Scalable and Complete Reasoning with Incomplete Ontology Reasoners

Decisions in industry, government and health care increasingly depend on improved access to and processing of digital information. This has led to a pressing demand for more powerful and flexible information systems. New generation information systems will need to efficiently process large data sets, exploit machine-readable domain knowledge, and answer queries by taking into account both knowledge and data.

Ontology-based information systems (OISs) constitute a rapidly maturing technology with the potential to meet these requirements. An ontology provides a vocabulary of terms that are familiar to the user, together with axioms describing the meaning of those terms. OISs can exploit the rich domain knowledge in an ontology to provide a unified view of the data and enrich query answers with implicit information using an automated reasoner.

Several standards for ontology and query languages have been developed, including RDF, OWL, OWL 2, and SPARQL. OWL and its revision OWL 2 provide a powerful and flexible ontology modelling language that can capture features such as class hierarchies, incomplete information, negative information, and so on. OWL ontologies are being used in an increasing range of applications, and are becoming a core technology for accessing, gathering, and sharing knowledge and data.

Applications involving large amounts of data, however, still pose serious challenges to the applicability of OISs. Problems in the applicability of OISs typically originate from conflicting application requirements.

- Modelling complex application domains requires rich ontology languages.
- Fine-grained access to information requires powerful query languages.
- Answering queries over large data sets requires scalable reasoners.
- Critical decisions that depend on access to information require query answers that are either complete, or where the incompleteness is well-understood.

Due to high worst-case complexity of the relevant reasoning problems, scalability is usually in conflict with the use of powerful ontology and query languages, and many applications give up completeness to achieve the desired scalability. As a result, existing OISs fail to meet one or more of these requirements: they support only weak ontology or query languages, they do not scale to the required volumes of data, or they do not provide guarantees as to the completeness of query answers.

Our goal in this project is to lay the foundations for a new generation of OISs that meet all the aforerementioned requirements, thus providing the ideal combination of expressive power, scalability and completeness.

To accomplish such an ambitious goal, we observe that the limitations imposed by the trade-offs between expressivity, scalability and completeness apply at the language level: that is, they involve worst-case complexity bounds for every ontology, query, and data set expressed in given ontology, query and data modeling languages. The class of ontologies, queries and data sets that are relevant to a particular application is, however, much more restricted. For example, although application data is often unknown or frequently changing, the ontology itself is fixed at design time, or changes infrequently. As a result, a reasoner known to be incomplete in general for given query and ontology languages might yield the same results as a complete reasoner for the application at hand. Identifying such cases is challenging, but it would have tremendous added value: applications could exploit scalable incomplete reasoners while still enjoying completeness guarantees, thus achieving 'the best of both worlds'.

We believe that our main goal can be accomplished by designing OISs that are optimised for the ontologies, queries and data sets relevant to the application at hand. Such OISs would maximise scalability while ensuring completeness of query answers, even for rich ontologies, large-scale data sets, and complex user queries.

Our work is likely to have a major impact on the development of Ontology-based Information Systems (OISs): systems that use domain knowledge to facilitate the exploitation of large and complex data sources.

The use of highly scalable OISs has already been recognised as a great commercial opportunity, and technology vendors have started to augment their existing systems to exploit ontologies. For example, Oracle Inc. has recently enhanced its well-known database management system with modules that support semantic data management and which rely on ontological reasoning with large amounts of data. We are also aware of, and in many cases are working directly with, a large number of companies and other organisations who are investigating, developing or using OISs; these include Ordnance Survey, Siemens, IBM, Software AG, Kaiser Permanente, EDF Energy, Alcatel-Lucent, Samsung and Siemens. Other examples of (smaller) IT providers that are incorporating ontological reasoning components in their software include OpenLink, OntoText, Clark&Parsia, and ExperienceOn. These technology vendors will probably be the most direct beneficiaries of our research, with users of OISs benefiting from the resulting improvements in reliability and scalability.

In the long term, beneficiaries could include anyone who uses or depends on an ontology-based information system, from the medical doctor who uses the NHS patient record service to the average citizen who checks the BBC's website for the latest results in the World Cup. Note that both of these activities already exploit OISs, with the SNOMED ontology playing a central role in NHS information systems, and the BBC's 2010 World Cup web site creating pages dynamically from content stored in an OIS.

Concerning dissemination and engagement, we will disseminate the results of this research through the following channels:

- publications in leading conferences and journals in the fields of artificial intelligence, Web technologies, and databases;

- contacts with industry: we have well-established collaborations with numerous developers and users of OISs including Oracle, Samsung, Alcatel-Lucent, EDF, BAE Systems, Siemens, Kaiser Permanente, Clark&Parsia, and ExperienceOn;

- participation in relevant coordination and standardisation efforts within groups and organisations such as the (W3C) and the OWL Experiences and Directions Group (OWLED);

- distribution of software via the Web; and

- industry liaisons activities via the Host Institution, which includes talks, industry showcases, periodic newsletters, and an industry liaison website.

Concerning exploitation, the commercialisation of IP resulting from the project will be managed by Isis Innovation, a subsidiary of the University of Oxford, founded to exploit the outcomes of Oxford's research activities. The Host Institution is very proactive in communicating funding opportunities to its academics for exploiting research output via Isis and therefore extensive departmental support and assistance during a potential exploitation process is to be expected.

Possible ways of commercially exploiting our expected results through Isis include licensing innovative software and creating a new start-up company.

Finally, we have already participated in several research projects, many of which involved close interaction and collaboration with users and industrial partners. We also have extensive experience in standardisation efforts as well as in the organisation of workshops and other events which have gathered a significant number of users and practitioners from both academia and industry.