NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

Natural Language Queries on Enterprise Linked Dataspaces:
A Vocabulary Independent Approach
André Freitas, João Gabriel Oliveira, Edward Curry, Seán O’Riain
Digital Enterprise Research Institute (DERI)
National University of Ireland, Galway
{firstname.lastname@deri.org}
Abstract
This work describes Treo, a natural language query
mechanism for Linked Data which focuses on the
provision of a precise and scalable semantic matching
approach between natural language queries and
distributed heterogeneous Linked Datasets. Treo's
semantic matching approach combines three key
elements: entity search, a Wikipedia-based semantic
relatedness measure and spreading activation search.
While entity search allows Treo to cope with queries
over high volume and distributed data, the combination
of entity search and spreading activation search using a
Wikipedia-based semantic relatedness measure
provides a flexible approach for handling the semantic
match between natural language queries and Linked
Data.
1. Introduction
Linked Data brings the promise of incorporating a new
dimension to the Web where the availability of Webscale
data can determine a paradigmatic transformation
of the Web and its applications. However, together with
its opportunities, Linked Data brings inherent
challenges in the way users and applications consume
the available data. End-users consuming Linked Data on
the Web or on corporate intranets should be able to
query data spread over potentially a large number of
heterogeneous, complex and distributed datasets. The
freedom and universality provided by search engines in
the Web of Documents were fundamental in the process
of maximizing the value of the information available on
the Web. Linked Data consumers, however, need
previous understanding of the available vocabularies in
order to execute expressive queries over Linked
Datasets. This constraint strongly limits the visibility
and value of Linked Data. Ideally a query mechanism
for Linked Data should abstract users from the
representation of data. This work focuses on the
investigation of a query mechanism that could address
this challenge providing a vocabulary independent
natural language query approach for Linked Data.
2. Description of the Approach
In order to address the problem, an approach based on
the combination of entity search, a Wikipedia-based
semantic relatedness measure and spreading activation
is proposed. The combination of these three elements in
a query mechanism for Linked Data is a new
contribution in the space. The center of the approach
93
relies on the use of a Wikipedia-based semantic
relatedness measure as a key element for matching
query terms to vocabulary terms, addressing an existing
gap in the literature. Wikipedia-based relatedness
measures address limitations of existing works which
are based on similarity measures/term expansion based
on WordNet.
The final query processing approach provides an
opportunity to revisit cognitive inspired spreading
activation models over semantic networks under
contemporary lenses. The recent availability of Linked
Data, large Web corpora, hardware resources and a
better understanding of the principles behind
information retrieval can provide the necessary
resources to enable practical applications over cognitive
inspired architectures.
3. Evaluation
A prototype, Treo, was developed and evaluated in
terms quality of results using the QALD Workshop
DBpedia training query set [1] containing 50 natural
language queries over DBPedia. Examples of queries
present in the dataset include: “who was the wife of
president Lincoln?” and “which capitals in Europe
were host cities of the summer Olympic games?”. Mean
reciprocal rank, precision and recall measures were
collected.
The proposed approach was able to answer 70% of the
queries and the final values for the collected measures
are mrr=0.492, precision=0.395 and recall=0.451. The
relatedness measure was able to cope with nontaxonomic
variations between query and vocabulary
terms, showing high average discrimination in the node
selection process (average difference between the
relatedness value of answer nodes and the relatedness
mean is 2.81 σ).
The results for each query were analyzed and queries
with errors were categorized into 5 different error
classes. The removal of the queries with errors that are
considered addressable in the short term (Partial
Ordered Dependency Structure Error, Pivot Error,
Relatedness Error) defines estimated projected
measurements of precision=0.64, recall=0.75 and
mrr=0.82.
References
1st Workshop on Question Answering over Linked Data (QALD-1),
://www.sc.cit-ec.uni-bielefeld.de/qald-1, (2011).