{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":24,"n_cols":5,"rows_kept":24,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[18,2,18,3],[19,2,19,3],[21,2,21,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9911020429703321"]
[null,null,"Autore",null,"Lavrač Nada"]
[null,null,"Titolo",null,"Bisociative Literature-Based Discovery : Methods with Tutorials in Python / / by Nada Lavrač, Bojan Cestnik, Andrej Kastrin"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"3-031-96863-8"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (182 pages)"]
[null,null,"Disciplina",null,"006.312"]
[null,null,"Soggetti",null,"Data mining"]
[{"$nulls":4},"Quantitative research"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Artificial intelligence - Data processing"]
[{"$nulls":4},"Information storage and retrieval systems"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Data Analysis and Big Data"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Data Science"]
[{"$nulls":4},"Information Storage and Retrieval"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"1. Introduction -- 2. History, Resources and Tools -- 3. Background Technologies -- 4. Benchmark Data and Reusable Python Code -- 5. Text Mining for Closed Discovery -- 6. Outlier-based Closed Discovery -- 7. Semantic and Outlier-based Open Discovery -- 8. Network-based Closed Discovery -- 9. Embedding-based Closed Discovery -- 10. Research Trends and Lessons Learned."]
[null,null,null,"Sommario/riassunto","This monograph introduces the field of bisociative literature-based discovery (LBD) by first explaining the underlying LBD principles and techniques, followed by the presentation of bisociative LBD techniques and applications developed by the authors. LBD is a process of uncovering new knowledge by analyzing and connecting disparate pieces of information from different sources of literature. Selected techniques include conventional natural language processing (NLP) approaches, as well as outlier-based, concept-based, network-based, and embeddings-based LBD approaches. Reproducibility aspects of bisociative LBD research are also covered, addressing all steps of the "]
[{"$nulls":4},"bisociative LBD process: data acquisition, text preprocessing, hypothesis discovery, and evaluation. The monograph is targeted at researchers, students, and domain experts interested in knowledge exploration, information retrieval, text mining, data science or semantic technologies. By covering texts, relations, networks, and ontologies, this work empowers domain experts to transcend their knowledge silos when confronted with varied data formats in their research practice. The monograph’s open science approach with tutorials in Python allows for code reuse and experiment replicability."]