Publications

Selected Papers

• Dessí, D., Osborne, F., Recupero, D. R., Buscaldi, D., & Motta, E. (2022). SCICERO: A deep learning and NLP approach for generating scientific knowledge graphs in the computer science domain. Knowledge-Based Systems, 258, 109945.

• Borrego, A., Dessì, D., Hernández, I., Osborne, F., Recupero Reforgiato, D. Ruiz, D., Buscaldi, Davide., & Motta, E. (2022). Completing Scientific Facts in Knowledge Graphs of Research Concepts. IEEE Access.

• Dessì, D., Cirrone, J., Reforgiato Recupero, D., & Shasha, D. (2018). SuperNoder: a tool to discover over-represented modular structures in networks. BMC Bioinformatics, 19, 318.

• Dessí, D., Osborne, F., Reforgiato Recupero, D., Buscaldi, D., & Motta, E. (2022). CS-KG: A Large-Scale Knowledge Graph of Research Entities and Claims in Computer Science. In International Semantic Web Conference (pp. 678-696). Springer, Cham.

Journals

• Dessí, D., Osborne, F., Recupero, D. R., Buscaldi, D., & Motta, E. (2022). SCICERO: A deep learning and NLP approach for generating scientific knowledge graphs in the computer science domain. Knowledge-Based Systems, 258, 109945.

• Borrego, A., Dessì, D., Hernández, I., Osborne, F., Recupero Reforgiato, D. Ruiz, D., Buscaldi, Davide., & Motta, E. (2022). Completing Scientific Facts in Knowledge Graphs of Research Concepts. IEEE Access.

• Dessì, D., & Recupero Reforgiato, D. (2021). LexTex: a framework to generate lexicons using WordNet word senses in domain specific categories. Journal of Intelligent Information Systems, 1-24.

• Dessì, D., Recupero Reforgiato, D., & Sack, H. (2021). An Assessment of Deep Learning Models and Word Embeddings for Toxicity Detection within Online Textual Comments. Electronics 10.7 (2021): 779.

• Dessì, D., Osborne, F., Recupero Reforgiato, D., Buscaldi, D., & Motta E. (2020). Generating knowledge graphs by employing Natural Language Processing and Machine Learning techniques within the scholarly domain. Future Generation Computer Systems, 116, (pp. 253-264).

• Dessì, D., Fenu, G., Marras, M., & Reforgiato Recupero, D. (2019). Bridging learning analytics and Cognitive Computing for Big Data classification in micro-learning video collections. Computers in Human Behavior, 92, (pp. 468-477).

• Dessì, D., Cirrone, J., Reforgiato Recupero, D., & Shasha, D. (2018). SuperNoder: a tool to discover over-represented modular structures in networks. BMC Bioinformatics, 19, 318.

• Fenu, G., Pau, P. L., & Dessì, D. (2017). Functional models and extending strategies for ecological networks. Applied Network Science 2.1:10.

Conference/Workshop Proceedings

• Dessí, D., Osborne, F., Reforgiato Recupero, D., Buscaldi, D., & Motta, E. (2022). CS-KG: A Large-Scale Knowledge Graph of Research Entities and Claims in Computer Science. In International Semantic Web Conference (pp. 678-696). Springer, Cham.

• Hoppe, F., Dessì, D., & Sack, H. (2021). Understanding Class Representations: An Intrinsic Evaluation of Zero-Shot Text Classification. In Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG@ ISWC2021). In CEUR Workshop Proceedings (vol. 3034).

• Ihsan, A. Z., Dessì, D., Alam, M., Sack, H., & Sandfeld, S. (2021). Steps towards a Dislocation Ontology for Crystalline Materials. In Proceedings of the Second International Workshop on Semantic Digital Twins. In CEUR Workshop Proceedings (vol. 2887).

• Alam M., Birkholz H., Dessì D, Eberl C., Fliegl H., Gumbsch P., von Hartrott P., Mädler L., Niebel M., Sack H., Thomas A. Ontology Modelling for Materials Science Experiments.. Joint of the Poster and Demo Track and Workshop on Ontology-Driven Conceptual Modelling of Digital Twins & Semantics Posters and Demos 2021. In CEUR Workshop Proceedings (vol. 2941).

• Vafaie, M., Bruns, O., Pilz, N., Dessí, D., & Sack, H. (2021). Modelling Archival Hierarchies in Practice: Key Aspects and Lessons Learned. The 6th International Workshop on Computational History (HistoInformatics2021-JCDL2021). In CEUR Workshop Proceedings (vol. 2981).

• Tan, M. A., Tietz, T., Bruns, O., Oppenlaender, J., Dessí, D., & Sack, H. (2021). DDB-KG: The German Bibliographic Heritage in a Knowledge Graph. The 6th International Workshop on Computational History (HistoInformatics2021-JCDL2021). In CEUR Workshop Proceedings (vol. 2981).

• Bruns, O., Tietz, T., Vafaie, M., Dessí, D., & Sack, H. (2021). Towards a representation of temporal data in archival records: Use cases and requirements. In Proceedings of the International Workshop on Archives and Linked Data. In CEUR Workshop Proceedings (vol. 3019).

• Dessì, D., Käser, T., Marras, M., Popescu, E., & Sack, H. (2021). Designing Intelligent Systems for Online Education: Open Challenges and Future Directions. Proceedings of the First International Workshop on Enabling Data-Driven Decisions from Learning on the Web co-located with the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021). In CEUR Workshop Proceedings (vol. 2876).

• Hoppe, F., Dessì, D., & Sack, H. (2021). Deep Learning meets Knowledge Graphs for Scholarly Data Classification. Proceedings of the of 1st International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K 2021), Co-located with The Web Conference 2021.

• Alam, M., Biswas, R., Chen, Y., Dessì, D., Gesese, G. A., Hoppe, F., & Sack, H. (2021) HierClasSArt: Knowledge-Aware Hierarchical Classification of Scholarly Articles. Proceedings of the of 1st International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K 2021), Co-located with The Web Conference 2021.

• Tietz,T., Bruns, O., Göller, S., Razum, M., Dessì, D., & Sack, H. (2021). Knowledge Graph enabled Curation and Exploration of Nuremberg’s City Heritage. Accepted at Qurator 2021.

• Vsesviatska, O., Tietz, T., Hoppe, F., Sprau, M., Meyer, N., Dessi, D., & Sack, H. (2021). ArDO: An Ontology to Describe the Dynamics of Multimedia Archival Records. In Proceedings of the 36th ACM/SIGAPP Symposium on Applied Computing (pp: 1855-1863). ACM.

• Hoppe, F., Tietz, T., Dessì, D., Meyer, N., Sprau, M., Alam, M., Sack, H. (2020). The Challenges of German Archival Document Categorization on Insufficient Labeled Data. In CEUR Workshop Proceedings (Vol. 2695, pp. 15-20). WHiSe@ESWC 2020.

• Dessì, D., Osborne, F., Recupero Reforgiato, D., Buscaldi, D., Motta E., & Sack, H. (2020). AI-KG: an Automatically Generated Knowledge Graph of Artificial Intelligence. In International Semantic Web Conference (pp. 127-143). Springer, Cham.

• Dessì, D., Helaoui, R., Kumar, V., Reforgiato Recupero, D., Riboni, D. (2020). Towards Morbidity Identification Employing Deep Learning and Word Embeddings within Clinical Notes. In CEUR Workshop Proceedings (Vol. 2596, pp. 1-12). SmartPhil@ACM IUI.

• Consoli S., Dessì, D., Fenu, G., Marras, M. (2020). Deep Attention-based Model for Helpfulness Prediction of Healthcare Online Reviews. In CEUR Workshop Proceedings (Vol. 2596, pp. 33-49). SmartPhil@ACM IUI.

• Reforgiato Recupero, D., Dessì, D., Concas, E. (2019). A Flexible and Scalable Architecture for Human-Robot Interaction. In European Conference on Ambient Intelligence (pp.311-317). Springer.

• Buscaldi, D., Dessì, D., Motta, E., Osborne, F., & Reforgiato Recupero, D. (2019). Mining Scholarly Data for Fine-Grained Knowledge Graph Construction. In CEUR Workshop Proceedings (Vol. 2377, pp. 21-30).

• Dessì, D., Dragoni, M., Fenu, G., Marras, M., & Reforgiato Recupero, D. (2018). Analisi Emozionale di recensioni di corsi online basata su Deep Learning e Word Embeddings. In Conferenza GARR 2018. (in Italian)

• Dessì, D., Fenu, G., Marras, M., & Reforgiato Recupero, D. (2018). COCO: Semantic-Enriched Collection of Online Courses at Scale with Experimental Use Cases. In World Conference on Information Systems and Technologies (pp. 1386-1396).

• Dessì, D., Reforgiato Recupero, D., Fenu, G., & Consoli, S. (2017). Exploiting Cognitive Computing and Frame Semantic Features for Biomedical Document Clustering. SeWeBMeDA@ESWC 14^th: (pp. 20-34).

• Fenu, G., Pau, P. L., & Dessì, D. (2016). Modeling and Extending Ecological Networks Using Land Similarity. In International Workshop on Complex Networks and their Applications (pp. 709-718). Springer.

Posters&Demos

• Tan, M. A., Tietz, T., Bruns, O., Oppenlaender, J., Dessì, D., & Sack, H. (2021). DDB-EDM to FaBiO: The Case of the German Digital Library. In Proceedings of the International Semantic Web Conference Posters, Demos and Industry Tracks. In CEUR Workshop Proceedings (vol. 2980).

• Bardaro G., Dessì, D., Motta, E., Osborne, F., & Reforgiato Recupero, D. (2019). Parsing Natural Language Sentences in Robot Actions. In CEUR Workshop Proceedings ISWC 18^th Satellite Poster&Demos (pp. 93-96).

• Buscaldi, D., Dessì, D., Motta, E., Osborne, F., & Reforgiato Recupero, D. (2019). Mining Scholarly Publications for Scientific Knowledge Graph Construction. ESWC 16th Satellite Events. (pp. 8-12).

• Dessì, D., Dragoni, M., Fenu, G., Marras, M., & Reforgiato Recupero, D. (2019). Evaluating neural word embeddings created from online course reviews for sentiment analysis. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (pp. 2124-2127). ACM.

• Dessì, D., Fenu, G., Marras, M. & Reforgiato Recupero, D. (2017). Leveraging Cognitive Computing for Multi-Class Classification of E-Learning Videos. ESWC 14th (pp. 21-25). Springer.

Book Chapters

• Dessì, D., Dragoni, M., Fenu, G., Marras, M., Reforgiato, D. (2020). Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course Reviews. In Deep Learning-Based Approaches for Sentiment Analysis (pp. 57-83)

• Dessì, D., Reforgiato Recupero, D., Fenu, G., & Consoli, S. (2019). A Recommender System of Medical Reports Leveraging Cognitive Computing and Frame Semantics. In Machine Learning Paradigms (pp. 7-30).