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Bisociative literature-based discove...

Lavrač, Nada.

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Bisociative literature-based discovery = methods with tutorials in Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bisociative literature-based discovery/ by Nada Lavrač, Bojan Cestnik, Andrej Kastrin.
Reminder of title:	methods with tutorials in Python /
Author:	Lavrač, Nada.
other author:	Cestnik, Bojan.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiv, 173 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Information retrieval - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-96863-1
ISBN:	9783031968631

Bisociative literature-based discovery = methods with tutorials in Python /
Lavrač, Nada.

Bisociative literature-based discoverymethods with tutorials in Python /[electronic resource] :by Nada Lavrač, Bojan Cestnik, Andrej Kastrin. - Cham :Springer Nature Switzerland :2025. - xiv, 173 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783031968631

Standard No.: 10.1007/978-3-031-96863-1doiSubjects--Topical Terms:

3790628
Information retrieval
--Data processing.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Bisociative literature-based discovery = methods with tutorials in Python /
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505 0 $a 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.
520 $a 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.
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