1) Traditional ontology research involved developing individual ontologies and annotating data with them, but now thousands of ontologies and huge amounts of data are available online. 2) This allows new opportunities for fundamental and applied research in automatically aligning data and ontologies to make sense of both, and mapping the landscape of semantics on the web. 3) Empirical studies are needed to understand ontology engineering practices by analyzing interconnected online ontologies, and how data and ontologies interact on the web.

1. Towards an
ecosystem of
data and
ontologies
Mathieu d’Aquin
and Enrico Motta
Knowledge Media Institute
The Open University

2. Large scale semantics on the web
• Traditional research and use of
ontologies has been piecemeal:
1. develop ontology
2. annotate data with ontology
• With the explosion of ontologies
and data on the web, the landscape
has changed
– Thousands of ontologies are now
available online, while huge
quantities of data are generated all
the time.
• This unprecedented scenario
introduces new opportunities for
both fundamental and applied
research

3. Experience from using online ontologies
NeOn Project
Methodological and technological support for
networked ontologies
– Ontology modularization, ontology design
patterns, ontology alignments, ontology
reuse, ontology search, ontology
visualisation, ontology evolution…
Key Infrastructure Component
Watson: ontology search engine and API for
exploiting available online ontologies. Used in:
– knowledge-based ontology matching
– query answering, word sense disambiguation
– information retrieval, semantic enrichment of
folksonomies, semantics-enhanced Web
browsing, ...
Refercences
d'Aquin, Motta et al. (2008) Towards a New Generation of Semantic Web Applications, IEEE Intelligent Systems
d'Aquin et al. (2009) NeOn Tool Support for Building Ontologies by Reuse, Demo at ICBO 2009
d'Aquin and Motta (2011) Watson, more than a Semantic Web search engine, Semantic Web Journal, 2

4. New challenges/research directions
– Automatically aligning data and ontologies to make sense
of both data and ontologies. For example:
• Enabling automatic evolution of ontologies
• Tidying up and automatically augment linked data sources
– Mapping the landscape of semantics on the web. For
example:
• Automatically identifying relations between ontologies
• Identifying and comparing different conceptual viewpoints on
the same domain
– Cf. our work on measuring agreement and disagreement
– Understanding usability of ontologies through appropriate
emprical studies
References
d'Aquin, M. (2009) Formally Measuring Agreement and Disagreement in
Ontologies, K-CAP 2009
d'Aquin and Motta (2011) Extracting Relevant Questions to an RDF
Dataset Using Formal Concept Analysis, K-CAP 2011
Motta et al. (2011) A Novel Approach to Visualizing and Navigating
Ontologies, ISWC 2011
d’Aquin et al. (2012) Combining Data Mining and Ontology Engineering
to enrich Ontologies and Linked Data, to appear Know@LOD ESWC
workshop

5. Steps forward
Need for Web-scale supporting infrastructures for
online ontologies
– Ontology repositories exist, but small
coverage, scope, etc.
– Need support for sustainable and accountable
publishing of ontologies
– Supporting usage monitoring and appropriate re-
use, including “find by example” / “find
alternatives”
Need for empirical investigations of online
ontologies
– Understanding the practices in knowledge
representation, ontology design and ontology
engineering through analyzing the large amounts
of interconnected ontologies online
– Understanding the practices in using ontologies
and how data and ontologies interact on the Web
References
Allocca, d'Aquin and Motta (2009) DOOR: Towards a Formalization of Ontology Relations, KEOD 2009
d'Aquin, Allocca, and Motta (2010) A Platform for Semantic Web Studies, Web Science 2010
d'Aquin and Noy (2011) Where to publish and find ontologies? A survey of ontology libraries, Journal of Web Semantics
d'Aquin and Gangemi (2011) Is there beauty in ontologies? Applied Ontology, 6, 3