Semantic network edges: a human-machine approach to represent typed relations in social networks

Pattuelli, M. Cristina; Miller, Matthew

doi:10.1108/jkm-11-2014-0453

Cited by 15 publications

(14 citation statements)

References 7 publications

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“…Other scholars use ontology and corpus to calculate the similarity according to the common node range and information of its synonym set [17]. Other scholars have proposed to calculate the similarity between words by the vector space method after extracting synonyms [18]. However, the above methods will be affected by the English dictionary and only focus on the estimation of similarity from the semantic aspect of English words, which is prone to large errors in accuracy.…”

Section: Related Workmentioning

confidence: 99%

Accurate Estimation of English Word Similarity Based on Semantic Network

Gong

2021

Security and Communication Networks

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The application of artificial intelligence in the field of English needs to process a large amount of English text data, but the deviation of English word similarity reduces its overall English translation accuracy and data processing efficiency. Therefore, this paper proposes an accurate estimation of English word similarity based on semantic network, which combines a variety of computing methods to form a compound computing structure based on semantic network. The experimental results show that the error between the Semantic Web-based English word similarity calculation method and manual evaluation is small, and the accuracy of English word similarity calculation is improved to a certain extent. In addition, compared with other English word similarity calculation methods, the English word similarity calculation method based on semantic network is more in line with people’s cognition and understanding of knowledge, has higher reliability, and has certain practical value in the field of English.

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Section: Related Workmentioning

confidence: 99%

Accurate Estimation of English Word Similarity Based on Semantic Network

Gong

2021

Security and Communication Networks

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“…In general, the synthesis depends on the development of semantics and knowledge "tacicity". This tacicity can be enriched through social constellations, Pattuelli and Miller (2015) used crowdsourcing and social network strategies to semantically improve data and make them readable and linkable through the integration of contextual information and relations classification. The authors also employed LOD methods to furnish knowledge representation techniques for networked data sets.…”

Section: Phase Iii: Analysismentioning

confidence: 99%

Extraction of knowledge from open government data

Mohamed

Pillutla

Tomasi

2020

VJIKMS

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Purpose The purpose of this paper is to establish a new conceptual iterative framework for extracting knowledge from open government data (OGD). OGD is becoming a major source for knowledge and innovation to generate economic value, if properly used. However, currently there are no standards or frameworks for applying knowledge continuum tactics, techniques and procedures (TTPs) to improve elicit knowledge extraction from OGD in a consistent manner. Design/methodology/approach This paper is based on a comprehensive review of literature on both OGD and knowledge management (KM) frameworks. It provides insights into the extraction of knowledge from OGD by using a vast array of phased KM TTPs into the OGD lifecycle phases. Findings The paper proposes a knowledge iterative value network (KIVN) as a new conceptual model that applies the principles of KM on OGD. KIVN operates through applying KM TTPs to transfer and transform discrete data into valuable knowledge. Research limitations/implications This model covers the most important knowledge elicitation steps; however, users who are interested in using KIVN phases may need to slightly customize it based on their environment and OGD policy and procedure. Practical implications After its validation, the model allows facilitating systemic manipulation of OGD for both data-consuming industries and data-producing governments to establish new business models and governance schemes to better make use of OGD. Originality/value This paper offers new perspectives on eliciting knowledge from OGD and discussing crucial, but overlooked area of the OGD arena, namely, knowledge extraction through KM principles.

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“…These drawbacks possibly cause controlled and limited content, as well as missed useful and relevant content, as indexing might reflect prejudices of professional indexers (King, 1987;Law and Whittaker, 1992). In response to Debortoli et al's (2016) Pattuelli and Miller (2015) present a novel approach to the development and semantic enhancement of a social network to support the analysis and interpretation of digital oral history data from jazz archives and special collections. Their approach is similar to the current work in providing an automated IE method to create a social network.…”

Section: Literature Reviewmentioning

confidence: 99%

Creation of knowledge-added concept maps: time augmention via pairwise temporal analysis

Sasson

Ravid

Pliskin

2017

JKM

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Purpose Although acknowledged as a principal dimension in the context of text mining, time has yet to be formally incorporated into the process of visually representing the relationships between keywords in a knowledge domain. This paper aims to develop and validate the feasibility of adding temporal knowledge to a concept map via pair-wise temporal analysis (PTA). Design/methodology/approach The paper presents a temporal trend detection algorithm – vector space model – designed to use objective quantitative pair-wise temporal operators to automatically detect co-occurring hot concepts. This PTA approach is demonstrated and validated without loss of generality for a spectrum of information technologies. Findings The rigorous validation study shows that the resulting temporal assessments are highly correlated with subjective assessments of experts (n = 136), exhibiting substantial reliability-of-agreement measures and average predictive validity above 85 per cent. Practical implications Using massive amounts of textual documents available on the Web to first generate a concept map and then add temporal knowledge, the contribution of this work is emphasized and magnified against the current growing attention to big data analytics. Originality/value This paper proposes a novel knowledge discovery method to improve a text-based concept map (i.e. semantic graph) via detection and representation of temporal relationships. The originality and value of the proposed method is highlighted in comparison to other knowledge discovery methods.

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Semantic network edges: a human-machine approach to represent typed relations in social networks

Cited by 15 publications

References 7 publications

Accurate Estimation of English Word Similarity Based on Semantic Network

Accurate Estimation of English Word Similarity Based on Semantic Network

Extraction of knowledge from open government data

Creation of knowledge-added concept maps: time augmention via pairwise temporal analysis

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