Mapping the (R-)Evolution of Technological Fields

A Semantic Network Approach

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

5 Citationer (Scopus)
559 Downloads (Pure)

Resumé

The aim of this paper was to provide a framework and novel methodology geared towards mapping technological change in complex interdependent systems by using large amounts of unstructured data from various recent on- and offline sources. Combining techniques from the fields of natural language processing and network analysis, we are able to identify technological fields as overlapping communities of knowledge fragments. Over time persistence of these fragments allows to observe how these fields evolve into trajectories, which may change, split, merge and finally disappear. As empirical example we use the broad area of Technological Singularity, an umbrella term for different technologies ranging from neuroscience to machine learning and bioengineering, which are seen as main contributors to the development of artificial intelligence and human enhancement technologies. Using a socially enhanced search routine, we extract 1,398 documents for the years 2011-2013. Our analysis highlights the importance of generic interface that allow ease the recombination of technology to increase the pace of technological progress. While we can identify consistent technology fields in static document collections, more advanced ontology reconciliation is needed to be able to track a larger number of communities over time.
OriginalsprogEngelsk
BogserieLecture Notes in Computer Science
Vol/bind8851
Sider (fra-til)359-383
Antal sider24
ISSN0302-9743
DOI
StatusUdgivet - 2014
BegivenhedSocInfo 2014: 6th International Conference on Social Informatics - MediaPro / Imagina, Avinguda Diagonal 177, 08018 Barcelona, Barcelona, Spanien
Varighed: 10 nov. 201413 nov. 2014
Konferencens nummer: 6

Konference

KonferenceSocInfo 2014
Nummer6
LokationMediaPro / Imagina, Avinguda Diagonal 177, 08018 Barcelona
LandSpanien
ByBarcelona
Periode10/11/201413/11/2014

Fingerprint

Semantic Network
Semantics
Fragment
Technological Change
Reconciliation
Neuroscience
Network Analysis
Electric network analysis
Recombination
Persistence
Natural Language
Artificial intelligence
Ontology
Overlapping
Learning systems
Large scale systems
Artificial Intelligence
Complex Systems
Machine Learning
Enhancement

Citer dette

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title = "Mapping the (R-)Evolution of Technological Fields: A Semantic Network Approach",
abstract = "The aim of this paper was to provide a framework and novel methodology geared towards mapping technological change in complex interdependent systems by using large amounts of unstructured data from various recent on- and offline sources. Combining techniques from the fields of natural language processing and network analysis, we are able to identify technological fields as overlapping communities of knowledge fragments. Over time persistence of these fragments allows to observe how these fields evolve into trajectories, which may change, split, merge and finally disappear. As empirical example we use the broad area of Technological Singularity, an umbrella term for different technologies ranging from neuroscience to machine learning and bioengineering, which are seen as main contributors to the development of artificial intelligence and human enhancement technologies. Using a socially enhanced search routine, we extract 1,398 documents for the years 2011-2013. Our analysis highlights the importance of generic interface that allow ease the recombination of technology to increase the pace of technological progress. While we can identify consistent technology fields in static document collections, more advanced ontology reconciliation is needed to be able to track a larger number of communities over time.",
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Mapping the (R-)Evolution of Technological Fields : A Semantic Network Approach. / Jurowetzki, Roman; Hain, Daniel S.

I: Lecture Notes in Computer Science, Bind 8851, 2014, s. 359-383.

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

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AB - The aim of this paper was to provide a framework and novel methodology geared towards mapping technological change in complex interdependent systems by using large amounts of unstructured data from various recent on- and offline sources. Combining techniques from the fields of natural language processing and network analysis, we are able to identify technological fields as overlapping communities of knowledge fragments. Over time persistence of these fragments allows to observe how these fields evolve into trajectories, which may change, split, merge and finally disappear. As empirical example we use the broad area of Technological Singularity, an umbrella term for different technologies ranging from neuroscience to machine learning and bioengineering, which are seen as main contributors to the development of artificial intelligence and human enhancement technologies. Using a socially enhanced search routine, we extract 1,398 documents for the years 2011-2013. Our analysis highlights the importance of generic interface that allow ease the recombination of technology to increase the pace of technological progress. While we can identify consistent technology fields in static document collections, more advanced ontology reconciliation is needed to be able to track a larger number of communities over time.

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