LEADER 03745nam 22006615 450 001 9911020429703321 005 20250808130234.0 010 $a3-031-96863-8 024 7 $a10.1007/978-3-031-96863-1 035 $a(MiAaPQ)EBC32256987 035 $a(Au-PeEL)EBL32256987 035 $a(CKB)40150648500041 035 $a(DE-He213)978-3-031-96863-1 035 $a(EXLCZ)9940150648500041 100 $a20250808d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBisociative Literature-Based Discovery $eMethods with Tutorials in Python /$fby Nada Lavra?, Bojan Cestnik, Andrej Kastrin 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (182 pages) 311 08$a3-031-96862-X 327 $a1. 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. 330 $aThis 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. 606 $aData mining 606 $aQuantitative research 606 $aMachine learning 606 $aArtificial intelligence$xData processing 606 $aInformation storage and retrieval systems 606 $aData Mining and Knowledge Discovery 606 $aData Analysis and Big Data 606 $aMachine Learning 606 $aData Science 606 $aInformation Storage and Retrieval 615 0$aData mining. 615 0$aQuantitative research. 615 0$aMachine learning. 615 0$aArtificial intelligence$xData processing. 615 0$aInformation storage and retrieval systems. 615 14$aData Mining and Knowledge Discovery. 615 24$aData Analysis and Big Data. 615 24$aMachine Learning. 615 24$aData Science. 615 24$aInformation Storage and Retrieval. 676 $a006.312 700 $aLavrac?$b Nada$0853929 701 $aCestnik$b Bojan$01841772 701 $aKastrin$b Andrej$01841773 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020429703321 996 $aBisociative Literature-Based Discovery$94421616 997 $aUNINA