東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

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 /
LDR:02712nmm a2200349 a 4500 001 2414121
003 DE-He213
005 20250808130234.0
006 m d
007 cr nn 008maaau
008 260205s2025 sz s 0 eng d
020 $a 9783031968631 $q (electronic bk.)
020 $a 9783031968624 $q (paper)
024 7 $a 10.1007/978-3-031-96863-1 $2 doi
035 $a 978-3-031-96863-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a UYQE $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b L414 2025
100 1 $a Lavrač, Nada. $3 3790625
245 1 0 $a Bisociative literature-based discovery $h [electronic resource] : $b methods with tutorials in Python / $c by Nada Lavrač, Bojan Cestnik, Andrej Kastrin.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xiv, 173 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Information retrieval $x Data processing. $3 3790628
650 0 $a Data mining. $3 562972
650 0 $a Natural language processing (Computer science) $3 565309
650 0 $a Python (Computer program language) $3 729789
650 1 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Data Analysis and Big Data. $3 3538537
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Science. $3 3538937
650 2 4 $a Information Storage and Retrieval. $3 761906
700 1 $a Cestnik, Bojan. $3 3790626
700 1 $a Kastrin, Andrej. $3 3790627
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-96863-1
950 $a Computer Science (SpringerNature-11645)