scholarly journals Large-scale semantic networks

2009 ◽  
Author(s):  
Václav Novák ◽  
Sven Hartrumpf ◽  
Keith Hall
Keyword(s):  
Large Scale ◽  
Semantic Networks

Distant connectivity and multiple-step priming in large-scale semantic networks.

2020 ◽  
Vol 46 (12) ◽  
pp. 2261-2276 ◽  
Author(s):  
Abhilasha A. Kumar ◽  
David A. Balota ◽  
Mark Steyvers
Keyword(s):  
Large Scale ◽  
Semantic Networks ◽  
Multiple Step

Implicit Knowledge Discovery in Design Semantic Network by Applying Pythagorean Means on Shortest Path Searching

2017 ◽  
Author(s):  
Feng Shi ◽  
Liuqing Chen ◽  
Ji Han ◽  
Peter Childs
Keyword(s):  
Text Mining ◽  
Knowledge Discovery ◽  
Language Processing ◽  
Shortest Path ◽  
Large Scale ◽  
Semantic Network ◽  
Semantic Networks ◽  
Implicit Knowledge ◽  
Implicit Associations ◽  
Correlation Degree

With the advent of the big-data era, massive textual information stored in electronic and digital documents have become valuable resources for knowledge discovery in the fields of design and engineering. Ontology technologies and semantic networks have been widely applied with text mining techniques including Natural Language Processing (NLP) to extract structured knowledge associations from the large-scale unstructured textual data. However, most existing works mainly focus on how to construct the semantic networks by developing various text mining methods such as statistical approaches and semantic approaches, while few studies are found to focus on how to subsequently analyze and fully utilize the already well-established semantic networks. In this paper, a specific network analysis method is proposed to discover the implicit knowledge associations from the existing semantic network for improving knowledge discovery and design innovation. Pythagorean means are applied with Dijkstra’s shortest path algorithm to discover the implicit knowledge associations either around a single knowledge concept or between two concepts. Six criteria are established to evaluate and rank the correlation degree of the implicit associations. Two engineering case studies were conducted to illustrate the proposed knowledge discovery process, and the results showed the effectiveness of the retrieved implicit knowledge associations on helping providing relevant knowledge from various aspects, and provoking creative ideas for engineering innovation.

Leveraging Large-Scale Semantic Networks for Adaptive Robot Task Learning and Execution

Big Data ◽  
2016 ◽  
Vol 4 (4) ◽  
pp. 217-235 ◽  
Author(s):  
Adrian Boteanu ◽  
Aaron St. Clair ◽  
Anahita Mohseni-Kabir ◽  
Carl Saldanha ◽  
Sonia Chernova
Keyword(s):  
Large Scale ◽  
Semantic Networks ◽  
Task Learning ◽  
Robot Task

scholarly journals Understanding the aging lexicon by linking individuals' experience, semantic networks, and cognitive performance

2021 ◽  
Author(s):  
Dirk U. Wulff ◽  
Simon De Deyne ◽  
Samuel Aeschbach ◽  
Rui Mata
Keyword(s):  
Individual Differences ◽  
Cognitive Performance ◽  
Cognitive Aging ◽  
Large Scale ◽  
Semantic Networks ◽  
Semantic Representations ◽  
Knowledge Representations ◽  
Concurrent Assessment ◽  
Empirical Approaches ◽  
Size And Structure

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance. We conclude that the extent to which experience-dependent changes in semantic representations contribute to individual differences in cognitive aging remains unclear. To help fill this gap, we outline an empirical agenda involving the concurrent assessment of large-scale semantic networks and cognitive performance in younger and older adults, and present preliminary data to establish the feasibility and limitations of such empirical approaches.

ASKNET: CREATING AND EVALUATING LARGE SCALE INTEGRATED SEMANTIC NETWORKS

2008 ◽  
Vol 02 (03) ◽  
pp. 343-364 ◽  
Author(s):  
BRIAN HARRINGTON ◽  
STEPHEN CLARK
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Question Answering ◽  
Semantic Information ◽  
Semantic Network ◽  
Word Sense Disambiguation ◽  
Semantic Networks ◽  
Difficult Problem ◽  
Word Sense

Extracting semantic information from multiple natural language sources and combining that information into a single unified resource is an important and fundamental goal for natural language processing. Large scale resources of this kind can be useful for a wide variety of tasks including question answering, word sense disambiguation and knowledge discovery. A single resource representing the information in multiple documents can provide significantly more semantic information than is available from the documents considered independently. The ASKNet system utilises existing NLP tools and resources, together with spreading activation based techniques, to automatically extract semantic information from a large number of English texts, and combines that information into a large scale semantic network. The initial emphasis of the ASKNet system is on wide-coverage, robustness and speed of construction. In this paper we show how a network consisting of over 1.5 million nodes and 3.5 million edges, more than twice as large as any network currently available, can be created in less than 3 days. Evaluation of large-scale semantic networks is a difficult problem. In order to evaluate ASKNet we have developed a novel evaluation metric based on the notion of a network "core" and employed human evaluators to determine the precision of various components of that core. We have applied this evaluation to networks created from randomly chosen articles used by DUC (Document Understanding Conference). The results are highly promising: almost 80% precision in the semantic core of the networks.

scholarly journals SEMANTIC NETWORKS FOR ENGINEERING DESIGN: A SURVEY

2021 ◽  
Vol 1 ◽  
pp. 2621-2630
Author(s):  
Ji Han ◽  
Serhad Sarica ◽  
Feng Shi ◽  
Jianxi Luo
Keyword(s):  
Engineering Design ◽  
Language Processing ◽  
Large Scale ◽  
Design Research ◽  
Semantic Network ◽  
Semantic Networks ◽  
Future Research ◽  
Research Directions ◽  
Engineering Data ◽  
Future Research Directions

AbstractThere have been growing uses of semantic networks in the past decade, such as leveraging large-scale pre-trained graph knowledge databases for various natural language processing (NLP) tasks in engineering design research. Therefore, the paper provides a survey of the research that has employed semantic networks in the engineering design research community. The survey reveals that engineering design researchers have primarily relied on WordNet, ConceptNet, and other common-sense semantic network databases trained on non-engineering data sources to develop methods or tools for engineering design. Meanwhile, there are emerging efforts to mine large scale technical publication and patent databases to construct engineering-contextualized semantic network databases, e.g., B-Link and TechNet, to support NLP in engineering design. On this basis, we recommend future research directions for the construction and applications of engineering-related semantic networks in engineering design research and practice.

A network perspective on mediated Europeanized public spheres: Assessing the degree of Europeanized media coverage in light of the 2014 European Parliament election

2017 ◽  
Vol 32 (6) ◽  
pp. 568-582 ◽  
Author(s):  
Christiane Grill ◽  
Hajo Boomgaarden
Keyword(s):  
European Union ◽  
Media Coverage ◽  
Large Scale ◽  
Semantic Networks ◽  
European Parliament ◽  
Online News ◽  
Political Life ◽  
The European Union ◽  
Public Spheres ◽  
Union Governance

The European Union has become an active political player in the political realm, raising the question about the European Union’s linkages with all aspects of political life reflected in national Europeanized public spheres. This study offers empirical evidence on the extent to which mass media support, challenge or even ignore political representatives in European Union affairs, and thus legitimize, respectively delegitimize European Union governance. The analysis is based on large-scale content analyses of print, TV and online news gathered before and after the 2014 European Parliament election in Austria ( N = 6432). Semantic networks show that national media focus on the European Union’s legislative body, the implications of the European Union’s exclusive competences on the nation state and on well-established European Union member countries. In doing so, national Europeanized public spheres constituted by the media legitimize the European Union’s governance in these areas while other aspects of European integration are ignored.

scholarly journals A data set linking large-scale, individual semantic networks and cognitive performance

2021 ◽  
Author(s):  
Dirk U. Wulff ◽  
Samuel Aeschbach ◽  
Simon De Deyne ◽  
Rui Mata
Keyword(s):  
Cognitive Performance ◽  
Large Scale ◽  
Semantic Networks ◽  
Data Set ◽  
Web Based ◽  
Age Related ◽  
Report Data ◽  
Concurrent Assessment ◽  
Associative Recall ◽  
Shed Light

We report data from a proof-of-concept study involving the concurrent assessment of large-scale individual semantic networks and cognitive performance. The data include 10,800 free associations--collected using a dedicated web-based platform over the course of 2-4 weeks--and responses to several cognitive tasks, including verbal fluency, episodic memory, associative recall tasks, from four younger and four older native German speakers. The data are unique in scope and composition and shed light on individual and age-related differences in mental representations and their role in cognitive performance across the lifespan.

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