Abstract
The increasingly huge volume of financial information found in a number of heterogeneous business sources is characterized by unstructured content, disparate data models and implicit knowledge. As Semantic Technologies mature, they provide a consistent and reliable basis to summon financial knowledge properly to the end user. In this paper, we present SONAR, a semantically enhanced financial search engine empowered by semi-structured crawling, inference-driven and ontology population strategies bypassing the present state-of-the-art technology caveats and shortcomings. © 2009 Springer Berlin Heidelberg.
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CITATION STYLE
Gómez, J. M., García-Sánchez, F., Valencia-García, R., Toma, I., & Moreno, C. G. (2009). SONAR: A semantically empowered financial search engine. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5601 LNCS, pp. 405–414). https://doi.org/10.1007/978-3-642-02264-7_42
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