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1. INTRODUCTION
The vast realms of the Web encompass a substantial portion
of the human knowledge nowadays. However, the particular
pieces of the knowledge are interleaved with a lot of noise
and scattered among many information resources of various
relevance. Thus it is often difficult or even infeasible to get
what one needs to know from the ballast of largely irrelevant
content. Our goal is to contribute to giving more meaning to
the content on the Web, identifying and interlinking relevant
knowledge out there so that machines can help humans to
make use of it more efficiently. We are particularly interested
in giving a clear empirical grounding to the symbolic meaning
being extracted from or asserted on the Web. To do so, we
have investigated a generalised extension of the distributional
semantics principles (a sub-field of computational linguistics).
This is to allow for identification of general, reliable patterns
and links among the loosely structured, heterogeneous and
noisy (Semantic) Web data, and enable meaningful symbolic
reasoning with such relevant content.
2. SOLUTION
Our approach is partly motivated by the recently devised
method of universal distributional representation and analysis
of natural language corpora [1]. We generalise and extend the
tensor-based representation of weighed co-occurrence relations
between natural language expressions proposed in [1] to reflect
the domain of Linked Data and knowledge on the Semantic
Web. Moreover, we provide novel methods of smooth combination
of the distributional and symbolic semantic levels
in order to allow for automated formal reasoning about the
empirically grounded knowledge emerging from the Web.
As of now, we have delivered EUREEKA [2] and
CORAAL [3] – two prototypes presenting particular implementations
of the general theoretical principles of our
approach. EUREEKA is a software library for extraction,
representation, integration and processing of emergent knowledge.
Currently it focuses mainly on knowledge acquired from
natural language texts and existing ontologies. It implements
a specific multi-context perspective of the basic corpus data,
where uncertain binary relations may further be specified
by an arbitrary number of additional context arguments (explicitly
attached provenance, time, space, etc.). It deals with
a single sub-perspective of entities and their corresponding
relationships, employing selective distance-based similarities
for the execution of queries, evaluation of inference rules
and simple analogical reasoning. The entities are ranked by
Empirically Grounded Linked Knowledge
Vít Nováček
Digital Enterprise Research Institute (DERI),
National University of Ireland Galway (NUIG)
IDA Business Park, Lower Dangan, Galway, Ireland
e-mail: vit.novacek@deri.org
134
means of a generalised IR approach and the ranking values
serve for a basic implementation of anytime versions of the
reasoning and querying algorithms. See [2] for more details.
The EUREEKA library can be downloaded at http://pypi.
python.org/pypi/eureeka/0.1.
On the top of EUREEKA, we delivered CORAAL [3],
an intelligent publication search engine deployed primarily
in the life sciences domain. In a nutshell, CORAAL is able
to extract knowledge in the form of argument-link-argument
statements associated with a positive or negative certainty
value and publication provenance information (i.e., which
publications were used for extraction and/or inference of the
statements). The extracted knowledge can be automatically
integrated with existing domain resources (such as machinereadable
life science thesauri) and augmented or refined by
means of the EUREEKA reasoning services. The content of
the CORAAL knowledge base is served to users via a search
interface that allows for complex statement queries in addition
to classical full-text capabilities. The search results can be
browsed and filtered along multiple facets, which enables the
users to quickly pinpoint the knowledge (i.e., statements) that
interests them, as well as the publication sources pertinent to
it. The CORAAL tool can be accessed at http://coraal.deri.ie.
3. CONCLUSION
We evaluated our approach via the CORAAL search engine.
A sample of actual users (experts in the domain of
cancer research and clinical care) helped us to assess various
quantitative and qualitative aspects of the emergent content
processed within our framework. The results of our approach
outperformed available base-lines by rather large margins
(improvements in the range of 189 − 374%), demonstrating
a promising potential of the proposed approach. Note that this
short paper is but a very superficial and rather incomplete
overview. Readers interested in learning more about our research,
its evaluation and about the large volume of related
work are invited to have a look at [2].
Acknowledgements: This work has been supported by
Science Foundation Ireland under Grant No. SFI/08/CE/I1380.
REFERENCES
[1] M. Baroni and A. Lenci. Distributional memory: A general framework
for corpus-based semantics. Computational Linguistics, 2010.
[2] V. Nováček. EUREEKA! Towards a Practical Emergent Knowledge
Processing. PhD thesis, DERI, NUIG, 2010. Available at (Dec 2010):
http://goo.gl/M1WkO.
[3] V. Nováček, T. Groza, S. Handschuh, and S. Decker. Coraal–dive into
publications, bathe in the knowledge. Journal of Web Semantics, 8(2-
3):176 – 181, 2010.
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