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.
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Gómez, J.M., García-Sánchez, F., Valencia-García, R., Toma, I., Moreno, C.G. (2009). SONAR: A Semantically Empowered Financial Search Engine. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds) Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy. IWINAC 2009. Lecture Notes in Computer Science, vol 5601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02264-7_42
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DOI: https://doi.org/10.1007/978-3-642-02264-7_42
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