| 39701 |
Creator |
c9aa7f2e582d191ed728ad414c5ea711 |
| 39701 |
Creator |
21e3abf33e3daaa89c07ea7d5da24bb0 |
| 39701 |
Date |
2012 |
| 39701 |
Date |
2012 |
| 39701 |
Is Part Of |
repository |
| 39701 |
abstract |
For a number of years now we have seen the emergence of repositories of research data
specified using OWL/RDF as representation languages, and conceptualized according
to a variety of ontologies. This class of solutions promises both to facilitate the
integration of research data with other relevant sources of information and also to
support more intelligent forms of querying and exploration. However, an issue which
has only been partially addressed is that of generating and characterizing semantically
the relations that exist between research areas. This problem has been traditionally
addressed by manually creating taxonomies, such as the ACM classification of research
topics. However, this manual approach is inadequate for a number of reasons: these
taxonomies are very coarse-grained and they do not cater for the fine- grained research
topics, which define the level at which typically researchers (and even more so, PhD
students) operate. Moreover, they evolve slowly, and therefore they tend not to cover
the most recent research trends. In addition, as we move towards a semantic characterization
of these relations, there is arguably a need for a more sophisticated characterization
than a homogeneous taxonomy, to reflect the different ways in which research areas
can be related. In this paper we propose Klink, a new approach to i) automatically
generating relations between research areas and ii) populating a bibliographic ontology,
which combines both machine learning methods and external knowledge, which is drawn
from a number of resources, including Google Scholar and Wikipedia. We have tested
a number of alternative algorithms and our evaluation shows that a method relying
on both external knowledge and the ability to detect temporal relations between research
areas performs best with respect to a manually constructed standard. |
| 39701 |
abstract |
For a number of years now we have seen the emergence of repositories of research data
specified using OWL/RDF as representation languages, and conceptualized according
to a variety of ontologies. This class of solutions promises both to facilitate the
integration of research data with other relevant sources of information and also to
support more intelligent forms of querying and exploration. However, an issue which
has only been partially addressed is that of generating and characterizing semantically
the relations that exist between research areas. This problem has been traditionally
addressed by manually creating taxonomies, such as the ACM classification of research
topics. However, this manual approach is inadequate for a number of reasons: these
taxonomies are very coarse-grained and they do not cater for the fine- grained research
topics, which define the level at which typically researchers (and even more so, PhD
students) operate. Moreover, they evolve slowly, and therefore they tend not to cover
the most recent research trends. In addition, as we move towards a semantic characterization
of these relations, there is arguably a need for a more sophisticated characterization
than a homogeneous taxonomy, to reflect the different ways in which research areas
can be related. In this paper we propose Klink, a new approach to i) automatically
generating relations between research areas and ii) populating a bibliographic ontology,
which combines both machine learning methods and external knowledge, which is drawn
from a number of resources, including Google Scholar and Wikipedia. We have tested
a number of alternative algorithms and our evaluation shows that a method relying
on both external knowledge and the ability to detect temporal relations between research
areas performs best with respect to a manually constructed standard. |
| 39701 |
authorList |
authors |
| 39701 |
presentedAt |
ext-c214cb9cb65c5271fdfa90fe677db190 |
| 39701 |
status |
peerReviewed |
| 39701 |
uri |
http://data.open.ac.uk/oro/document/223802 |
| 39701 |
uri |
http://data.open.ac.uk/oro/document/223803 |
| 39701 |
uri |
http://data.open.ac.uk/oro/document/223804 |
| 39701 |
uri |
http://data.open.ac.uk/oro/document/223805 |
| 39701 |
uri |
http://data.open.ac.uk/oro/document/223806 |
| 39701 |
uri |
http://data.open.ac.uk/oro/document/223807 |
| 39701 |
uri |
http://data.open.ac.uk/oro/document/224153 |
| 39701 |
volume |
7649 |
| 39701 |
volume |
7649 |
| 39701 |
type |
AcademicArticle |
| 39701 |
type |
Article |
| 39701 |
label |
Mining semantic relations between research areas |
| 39701 |
label |
Mining semantic relations between research areas |
| 39701 |
Title |
Mining semantic relations between research areas |
| 39701 |
Title |
Mining semantic relations between research areas |
| 39701 |
in dataset |
oro |