商品描述
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 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.
商品描述(中文翻譯)
本專著介紹了雙聯想文獻基礎發現（LBD）領域，首先解釋了LBD的基本原則和技術，接著展示了作者所開發的雙聯想LBD技術和應用。LBD是一個通過分析和連接來自不同文獻來源的不同信息片段來揭示新知識的過程。
所選技術包括傳統的自然語言處理（NLP）方法，以及基於異常值、概念、網絡和嵌入的LBD方法。專著還涵蓋了雙聯想LBD研究的可重複性方面，涉及雙聯想LBD過程的所有步驟：數據獲取、文本預處理、假設發現和評估。
本專著的目標讀者是對知識探索、信息檢索、文本挖掘、數據科學或語義技術感興趣的研究人員、學生和領域專家。通過涵蓋文本、關係、網絡和本體，這項工作使領域專家在面對研究實踐中多樣化的數據格式時，能夠超越他們的知識孤島。專著的開放科學方法結合Python教程，允許代碼重用和實驗可重複性。
作者簡介
Nada Lavrač is a Research Councillor and a former Head of the Department of Knowledge Technologies at the Jozef Stefan Institute, Ljubljana, Slovenia. She is a Full Professor at the University of Nova Gorica and was Head of the ICT Programme and Vice-Dean of the International Postgraduate School Jozef Stefan. Her research interests include machine learning, semantic data mining, text mining, computational creativity, and applications of machine learning in medicine and bioinformatics. She has been a keynote speaker at KI, ADBIS, ISWC, LPNMR, JSMI, and AIME conferences, and has chaired several conferences, including ILP, ICCC, IDA, DS, and AIME. She served on the editorial boards of Artificial Intelligence in Medicine, AI Communications, New Generation Computing, Applied AI, Machine Learning, and Data Mining and Knowledge Discovery. She is an ECCAI/EurAI Fellow (and was ECCAI Vice-President from 1996 to 1998), an ELLIS Fellow (and was an ELLIS Board Member from 2022 to 2025), and was a member of the International Machine Learning Society and the Artificial Intelligence in Medicine boards. She also received several national awards for her outstanding contributions to machine learning. Bojan Cestnik is the founder and CEO of the high-tech software company Temida, a Senior Researcher at the Jozef Stefan Institute, and a Professor of Computer Science at the University of Nova Gorica and the Jozef Stefan International Postgraduate School, all in Slovenia. His work combines scientific research with real-world applications in the field of artificial intelligence. He specializes in machine learning, predictive analytics, and decision making. He has developed innovative methods that improve the interpretability and reliability of machine learning models and knowledge-based systems, significantly contributing to decision-support applications that demand robustness and transparency. Andrej Kastrin is an Associate Professor of Biostatistics and Biomedical Informatics at the University of Ljubljana, Slovenia. His research focuses on the foundations of artificial intelligence, large-scale statistical learning, text mining, and complex network analysis, with particular emphasis on computational scientific discovery. He has authored over 100 peer-reviewed publications, including journal articles and conference papers. He is actively involved in both national and European research initiatives and frequently serves on program committees for leading data science conferences. He is also an editor of the international journal Advances in Methodology and Statistics and the chief organizer of the international conference Applied Statistics. He teaches courses in statistics and data science and supervises PhD students.
作者簡介(中文翻譯)
Nada Lavrač 是斯洛維尼亞盧布爾雅那約瑟夫·斯特凡研究所的研究顧問及前知識技術部門主任。她是新戈里察大學的全職教授，曾擔任資訊與通信技術（ICT）計畫主任及約瑟夫·斯特凡國際研究生學校的副院長。她的研究興趣包括機器學習、語意資料探勘、文本探勘、計算創造力，以及機器學習在醫學和生物資訊學中的應用。她曾在 KI、ADBis、ISWC、LPNMR、JSMI 和 AIME 會議上擔任主題演講者，並主持過多個會議，包括 ILP、ICCC、IDA、DS 和 AIME。她曾擔任《人工智慧在醫學中的應用》（Artificial Intelligence in Medicine）、《AI 通訊》（AI Communications）、《新一代計算》（New Generation Computing）、《應用人工智慧》（Applied AI）、《機器學習》（Machine Learning）和《資料探勘與知識發現》（Data Mining and Knowledge Discovery）的編輯委員會成員。她是 ECCAI/EurAI 的研究員（並於 1996 至 1998 年擔任 ECCAI 副總裁）、ELLIS 研究員（並於 2022 至 2025 年擔任 ELLIS 董事會成員），同時也是國際機器學習學會及人工智慧在醫學中的應用委員會的成員。她因在機器學習領域的傑出貢獻而獲得多項國家獎項。
Bojan Cestnik 是高科技軟體公司 Temida 的創辦人及執行長，並擔任約瑟夫·斯特凡研究所的高級研究員，以及新戈里察大學和約瑟夫·斯特凡國際研究生學校的計算機科學教授，均位於斯洛維尼亞。他的工作結合了科學研究與人工智慧領域的實際應用。他專注於機器學習、預測分析和決策制定。他開發了創新的方法，改善了機器學習模型和基於知識系統的可解釋性和可靠性，對於需要穩健性和透明度的決策支持應用做出了顯著貢獻。
Andrej Kastrin 是斯洛維尼亞盧布爾雅那大學的生物統計學和生物醫學資訊學副教授。他的研究專注於人工智慧的基礎、大規模統計學習、文本探勘和複雜網絡分析，特別強調計算科學發現。他已發表超過 100 篇經過同行評審的出版物，包括期刊文章和會議論文。他積極參與國內和歐洲的研究計畫，並經常擔任領先資料科學會議的程序委員會成員。他也是國際期刊《方法學與統計學進展》（Advances in Methodology and Statistics）的編輯，以及國際會議《應用統計學》（Applied Statistics）的首席組織者。他教授統計學和資料科學課程，並指導博士生。
