| 25712 |
Creator |
31c7eb2d63e8876c11e2755b271c8287 |
| 25712 |
Creator |
b8d521a6173aa941c32cd5686f640bfa |
| 25712 |
Creator |
c9aa7f2e582d191ed728ad414c5ea711 |
| 25712 |
Creator |
ext-4f4c83686823fd0aa269add897cedaa4 |
| 25712 |
Creator |
ext-35d47fdf00e59184586222b14761a507 |
| 25712 |
Creator |
ext-94c7dfa4ddeaf7d5a3f6d6bc203cd238 |
| 25712 |
Date |
2011-12 |
| 25712 |
Is Part Of |
repository |
| 25712 |
Is Part Of |
p15708268 |
| 25712 |
abstract |
Currently, techniques for content description and query processing in Information
Retrieval (IR) are based on keywords, and therefore provide limited capabilities to
capture the conceptualizations associated with user needs and contents. Aiming to
solve the limitations of keyword-based models, the idea of conceptual search, understood
as searching by meanings rather than literal strings, has been the focus of a wide
body of research in the IR field. More recently, it has been used as a prototypical
scenario (or even envisioned as a potential “killer app”) in the Semantic Web (SW)
vision, since its emergence in the late nineties. However, current approaches to semantic
search developed in the SW area have not yet taken full advantage of the acquired
knowledge, accumulated experience, and technological sophistication achieved through
several decades of work in the IR field. Starting from this position, this work investigates
the definition of an ontology-based IR model, oriented to the exploitation of domain
Knowledge Bases to support semantic search capabilities in large document repositories,
stressing on the one hand the use of fully-fledged ontologies in the semantic-based
perspective, and on the other hand the consideration of unstructured content as the
target search space. The major contribution of this work is an innovative, comprehensive
semantic search model, which extends the classic IR model, addresses the challenges
of the massive and heterogeneous Web environment, and integrates the benefits of both
keyword and semantic-based search. Additional contributions include: an innovative
rank fusion technique that minimizes the undesired effects of knowledge sparseness
on the yet juvenile SW, and the creation of a large-scale evaluation benchmark, based
on IR evaluation standards, which allows a rigorous comparison between IR and SW approaches.
Conducted experiments show that our semantic search model obtained comparable and
better performance results (in terms of MAP and P@10 values) than the best TREC automatic
system. |
| 25712 |
authorList |
authors |
| 25712 |
issue |
4 |
| 25712 |
status |
nonPeerReviewed |
| 25712 |
volume |
9 |
| 25712 |
type |
AcademicArticle |
| 25712 |
type |
Article |
| 25712 |
label |
Fernández, Miriam ; Cantador, Iván; López, Vanessa ; Vallet, David; Castells, Pablo
and Motta, Enrico (2011). Semantically enhanced information retrieval: an ontology-based
approach. Journal of Web Semantics: Science, Services and Agents on the World Wide
Web, 9(4) pp. 434–452. |
| 25712 |
label |
Fernández, Miriam ; Cantador, Iván; López, Vanessa ; Vallet, David; Castells, Pablo
and Motta, Enrico (2011). Semantically enhanced information retrieval: an ontology-based
approach. Journal of Web Semantics: Science, Services and Agents on the World Wide
Web, 9(4) pp. 434–452. |
| 25712 |
Title |
Semantically enhanced information retrieval: an ontology-based approach |
| 25712 |
in dataset |
oro |