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Information retrieval from scientific abstract and citation databases: A query-by-documents approach based on Monte-Carlo sampling
dc.contributor.author | Lechtenberg, Fabian |
dc.contributor.author | Farreres de la Morena, Xavier |
dc.contributor.author | Galvan Cara, Aldwin Lois |
dc.contributor.author | Somoza Tornos, Ana |
dc.contributor.author | Espuña Camarasa, Antonio |
dc.contributor.author | Graells Sobré, Moisès |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Química |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2022-07-14T13:13:08Z |
dc.date.available | 2022-07-14T13:13:08Z |
dc.date.issued | 2022-08-01 |
dc.identifier.citation | Lechtenberg, F. [et al.]. Information retrieval from scientific abstract and citation databases: A query-by-documents approach based on Monte-Carlo sampling. "Expert systems with applications", 1 Agost 2022, vol. 199, núm. 116967. |
dc.identifier.issn | 0957-4174 |
dc.identifier.uri | http://hdl.handle.net/2117/370214 |
dc.description.abstract | The rapidly increasing amount of information and entries in abstract and citation databases steadily complicates the information retrieval task. In this study, a novel query-by-document approach using Monte-Carlo sampling of relevant keywords is presented. From a set of input documents (seed) keywords are extracted using TF-IDF and subsequently sampled to repeatedly construct queries to the database. The occurrence of returned documents is counted and serves as a proxy relevance metric. Two case studies based on the Scopus® database are used to demonstrate the method and its key advantages. No expert knowledge and human intervention is needed to construct the final search strings which reduces the human bias. The methods practicality is supported by the high re-retrieval of seed documents of 7/8 and 26/31 in high ranks in the two presented case studies. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject.lcsh | Monte Carlo method |
dc.subject.other | Systematic literature review |
dc.subject.other | Decision-making support |
dc.subject.other | Recommender system |
dc.subject.other | Monte-Carlo sampling |
dc.subject.other | Knowledge management |
dc.title | Information retrieval from scientific abstract and citation databases: A query-by-documents approach based on Monte-Carlo sampling |
dc.type | Article |
dc.subject.lemac | Montecarlo, Mètode de |
dc.contributor.group | Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural |
dc.contributor.group | Universitat Politècnica de Catalunya. CEPIMA - Center for Process and Environment Engineering |
dc.identifier.doi | 10.1016/j.eswa.2022.116967 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0957417422003931 |
dc.rights.access | Open Access |
local.identifier.drac | 33089053 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Lechtenberg, F.; Farreres, J.; Galvan, A.; Somoza, A.; Espuña, A.; Graells, M. |
local.citation.publicationName | Expert systems with applications |
local.citation.volume | 199 |
local.citation.number | 116967 |
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