| Sentiment analysis: Adjectives and adverbs are better than adjectives alone. F Benamara, C Cesarano, A Picariello, DR Recupero, VS Subrahmanian ICWSM 7, 203-206, 2007 | 514 | 2007 |
| Semantic web machine reading with FRED A Gangemi, V Presutti, D Reforgiato Recupero, AG Nuzzolese, ... Semantic Web 8 (6), 873-893, 2017 | 238* | 2017 |
| Deep learning and time series-to-image encoding for financial forecasting S Barra, SM Carta, A Corriga, AS Podda, DR Recupero IEEE/CAA Journal of Automatica Sinica 7 (3), 683-692, 2020 | 207 | 2020 |
| AVA: Adjective-verb-adverb combinations for sentiment analysis VS Subrahmanian, D Reforgiato IEEE Intelligent Systems 23 (4), 43-50, 2008 | 201 | 2008 |
| Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting S Carta, A Ferreira, AS Podda, DR Recupero, A Sanna Expert systems with applications 164, 113820, 2021 | 190 | 2021 |
| Annotated rdf O Udrea, DR Recupero, VS Subrahmanian ACM Transactions on Computational Logic (TOCL) 11 (2), 1-41, 2010 | 166* | 2010 |
| Frame-based detection of opinion holders and topics: a model and a tool A Gangemi, V Presutti, DR Recupero IEEE Computational Intelligence Magazine 9 (1), 20-30, 2014 | 146 | 2014 |
| A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning S Carta, A Corriga, A Ferreira, AS Podda, DR Recupero Applied Intelligence 51, 889-905, 2021 | 120 | 2021 |
| Sentilo: frame-based sentiment analysis D Reforgiato Recupero, V Presutti, S Consoli, A Gangemi, AG Nuzzolese Cognitive Computation 7, 211-225, 2015 | 120 | 2015 |
| Framester: A wide coverage linguistic linked data hub A Gangemi, M Alam, L Asprino, V Presutti, DR Recupero Knowledge Engineering and Knowledge Management: 20th International …, 2016 | 114 | 2016 |
| Bridging learning analytics and cognitive computing for big data classification in micro-learning video collections D Dessì, G Fenu, M Marras, DR Recupero Computers in Human Behavior 92, 468-477, 2019 | 105 | 2019 |
| Human-centric artificial intelligence architecture for industry 5.0 applications JM Rožanec, I Novalija, P Zajec, K Kenda, H Tavakoli Ghinani, S Suh, ... International journal of production research 61 (20), 6847-6872, 2023 | 103 | 2023 |
| Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model S Carta, G Fenu, DR Recupero, R Saia Journal of Information Security and Applications 46, 13-22, 2019 | 98 | 2019 |
| A new unsupervised method for document clustering by using WordNet lexical and conceptual relations D Reforgiato Recupero Information Retrieval 10, 563-579, 2007 | 94 | 2007 |
| Generating knowledge graphs by employing natural language processing and machine learning techniques within the scholarly domain D Dessì, F Osborne, DR Recupero, D Buscaldi, E Motta Future Generation Computer Systems 116, 253-264, 2021 | 92 | 2021 |
| System and method for analysis of an opinion expressed in documents with regard to a particular topic VS Subrahmanian, DR Reforgiato, A Picariello, BJ Dorr, C Cesarano, ... US Patent 8,296,168, 2012 | 88 | 2012 |
| Antipole tree indexing to support range search and k-nearest neighbor search in metric spaces D Cantone, A Ferro, A Pulvirenti, DR Recupero, D Shasha IEEE transactions on knowledge and data engineering 17 (4), 535-550, 2005 | 85 | 2005 |
| Ensembling classical machine learning and deep learning approaches for morbidity identification from clinical notes V Kumar, DR Recupero, D Riboni, R Helaoui IEEE Access 9, 7107-7126, 2020 | 80 | 2020 |
| AI-KG: an automatically generated knowledge graph of artificial intelligence D Dessì, F Osborne, D Reforgiato Recupero, D Buscaldi, E Motta, H Sack The Semantic Web–ISWC 2020: 19th International Semantic Web Conference …, 2020 | 78 | 2020 |
| Explainable machine learning exploiting news and domain-specific lexicon for stock market forecasting SM Carta, S Consoli, L Piras, AS Podda, DR Recupero IEEE Access 9, 30193-30205, 2021 | 72 | 2021 |
Aldo GangemiUniversity of Bologna and ISTC, National Research Council, Italy在 unibo.it 的电子邮件经过验证
