The Experts below are selected from a list of 66009 Experts worldwide ranked by ideXlab platform
Kristin L Wood - One of the best experts on this subject based on the ideXlab platform.
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technet technology Semantic Network based on patent data
Expert Systems With Applications, 2020Co-Authors: Serhad Sarica, Jianxi Luo, Kristin L WoodAbstract:Abstract The growing developments in general Semantic Networks, knowledge graphs and ontology databases have motivated us to build a large-scale comprehensive Semantic Network of technology-related data for engineering knowledge discovery, technology search and retrieval, and artificial intelligence for engineering design and innovation. Specially, we constructed a technology Semantic Network (TechNet) that covers the elemental concepts in all domains of technology and their Semantic associations by mining the complete U.S. patent database from 1976. To derive the TechNet, natural language processing techniques were utilized to extract terms from massive patent texts and recent word embedding algorithms were employed to vectorize such terms and establish their Semantic relationships. We report and evaluate the TechNet for retrieving terms and their pairwise relevance that is meaningful from a technology and engineering design perspective. The TechNet may serve as an infrastructure to support a wide range of applications, e.g., technical text summaries, search query predictions, relational knowledge discovery, and design ideation support, in the context of engineering and technology, and complement or enrich existing Semantic databases. To enable such applications, the TechNet is made public via an online interface and APIs for public users to retrieve technology-related terms and their relevancies.
Dimitris Kotzinos - One of the best experts on this subject based on the ideXlab platform.
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evolution of the political opinion landscape during electoral periods
EPJ Data Science, 2021Co-Authors: Tomas Mussi Reyero, Mariano G Beiro, Ignacio J Alvarezhamelin, Laura Hernandez, Dimitris KotzinosAbstract:We present a study of the evolution of the political landscape during the 2015 and 2019 presidential elections in Argentina, based on data obtained from the micro-blogging platform Twitter. We build a Semantic Network based on the hashtags used by all the users following at least one of the main candidates. With this Network we can detect the topics that are discussed in the society. At a difference with most studies of opinion on social media, we do not choose the topics a priori, they emerge from the community structure of the Semantic Network instead. We assign to each user a dynamical topic vector which measures the evolution of her/his opinion in this space and allows us to monitor the similarities and differences among groups of supporters of different candidates. Our results show that the method is able to detect the dynamics of formation of opinion on different topics and, in particular, it can capture the reshaping of the political opinion landscape which has led to the inversion of result between the two rounds of 2015 election.
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evolution of the political opinion landscape during electoral periods
arXiv: Social and Information Networks, 2020Co-Authors: Tomas Mussi Reyero, Mariano G Beiro, Ignacio J Alvarezhamelin, Laura Hernandez, Dimitris KotzinosAbstract:We present a study of the evolution of the political landscape during the 2015 and 2019 presidential elections in Argentina, based on the data obtained from the micro-blogging platform Twitter. We build a Semantic Network based on the hashtags used by all the users following at least one of the main candidates. With this Network we can detect the topics that are discussed in the society. At a difference with most studies of opinion on social media, we do not choose the topics a priori, they naturally emerge from the community structure of the Semantic Network instead. We assign to each user a dynamical topic vector which measures the evolution of her/his opinion in this space and allows us to monitor the similarities and differences among groups of supporters of different candidates. Our results show that the method is able to detect the dynamics of formation of opinion on different topics and, in particular, it can capture the reshaping of the political opinion landscape which has led to the inversion of result between the two rounds of the 2015 election.
Serhad Sarica - One of the best experts on this subject based on the ideXlab platform.
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technet technology Semantic Network based on patent data
Expert Systems With Applications, 2020Co-Authors: Serhad Sarica, Jianxi Luo, Kristin L WoodAbstract:Abstract The growing developments in general Semantic Networks, knowledge graphs and ontology databases have motivated us to build a large-scale comprehensive Semantic Network of technology-related data for engineering knowledge discovery, technology search and retrieval, and artificial intelligence for engineering design and innovation. Specially, we constructed a technology Semantic Network (TechNet) that covers the elemental concepts in all domains of technology and their Semantic associations by mining the complete U.S. patent database from 1976. To derive the TechNet, natural language processing techniques were utilized to extract terms from massive patent texts and recent word embedding algorithms were employed to vectorize such terms and establish their Semantic relationships. We report and evaluate the TechNet for retrieving terms and their pairwise relevance that is meaningful from a technology and engineering design perspective. The TechNet may serve as an infrastructure to support a wide range of applications, e.g., technical text summaries, search query predictions, relational knowledge discovery, and design ideation support, in the context of engineering and technology, and complement or enrich existing Semantic databases. To enable such applications, the TechNet is made public via an online interface and APIs for public users to retrieve technology-related terms and their relevancies.
Thomas L Griffiths - One of the best experts on this subject based on the ideXlab platform.
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random walks on Semantic Networks can resemble optimal foraging
Neural Information Processing Systems, 2015Co-Authors: Joshua T Abbott, Joseph L Austerweil, Thomas L GriffithsAbstract:When people are asked to retrieve members of a category from memory, clusters of Semantically related items tend to be retrieved together. A recent article by Hills, Jones, and Todd (2012) argued that this pattern reflects a process similar to optimal strategies for foraging for food in patchy spatial environments, with an individual making a strategic decision to switch away from a cluster of related information as it becomes depleted. We demonstrate that similar behavioral phenomena also emerge from a random walk on a Semantic Network derived from human word-association data. Random walks provide an alternative account of how people search their memories, postulating an undirected rather than a strategic search process. We show that results resembling optimal foraging are produced by random walks when related items are close together in the Semantic Network. These findings are reminiscent of arguments from the debate on mental imagery, showing how different processes can produce similar results when operating on different representations.
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human memory search as a random walk in a Semantic Network
Neural Information Processing Systems, 2012Co-Authors: Joseph L Austerweil, Joshua T Abbott, Thomas L GriffithsAbstract:The human mind has a remarkable ability to store a vast amount of information in memory, and an even more remarkable ability to retrieve these experiences when needed. Understanding the representations and algorithms that underlie human memory search could potentially be useful in other information retrieval settings, including internet search. Psychological studies have revealed clear regularities in how people search their memory, with clusters of Semantically related items tending to be retrieved together. These findings have recently been taken as evidence that human memory search is similar to animals foraging for food in patchy environments, with people making a rational decision to switch away from a cluster of related information as it becomes depleted. We demonstrate that the results that were taken as evidence for this account also emerge from a random walk on a Semantic Network, much like the random web surfer model used in internet search engines. This offers a simpler and more unified account of how people search their memory, postulating a single process rather than one process for exploring a cluster and one process for switching between clusters.
Rhonda B Friedman - One of the best experts on this subject based on the ideXlab platform.
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the underlying mechanisms of Semantic memory loss in alzheimer s disease and Semantic dementia
Neuropsychologia, 2008Co-Authors: Sean L Rogers, Rhonda B FriedmanAbstract:Patients with Alzheimer’s disease (AD) and patients with Semantic dementia (SD) both exhibit deficits on explicit tasks of Semantic memory such as picture naming and category fluency. These deficits have been attributed to a degradation of the stored Semantic Network. An alternative explanation attributes the Semantic deficit in AD to an impaired ability to consciously retrieve items from the Semantic Network. The present study used an implicit lexical-decision priming task to examine the integrity of the underlying Semantic Network in AD and SD patients matched for degree of impairment on explicit Semantic memory tasks. The AD (n = 11) and SD (n = 11) patient groups were matched for age, education, level of dementia and impairment on four explicit Semantic memory tasks. Healthy elderly participants (n = 22) were matched for age and education. Semantic priming effects were evaluated for three types of Semantic relationships (attributes, category coordinates, and category superordinates) and compared to lexical associative priming. Healthy controls showed significant priming across all conditions. In contrast, AD patients showed normal superordinate priming, and significant (although somewhat reduced) coordinate priming, but no attribute priming. SD patients showed no priming effect for any Semantic relationship. All groups showed significant associative priming. The results indicate that SD patients do indeed have substantial degradation of Semantic memory, while AD patients have a partially intact Network, accounting for priming in superordinate and coordinate conditions. These findings suggest that AD patients’ impairment on explicit Semantic tasks is the product of deficient explicit retrieval in combination with a partially degraded Semantic Network. © 2007 Elsevier Ltd. All rights reserved.
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the underlying mechanisms of Semantic memory loss in alzheimer s disease and Semantic dementia
Neuropsychologia, 2008Co-Authors: Sean L Rogers, Rhonda B FriedmanAbstract:Patients with Alzheimer's disease (AD) and patients with Semantic dementia (SD) both exhibit deficits on explicit tasks of Semantic memory such as picture naming and category fluency. These deficits have been attributed to a degradation of the stored Semantic Network. An alternative explanation attributes the Semantic deficit in AD to an impaired ability to consciously retrieve items from the Semantic Network. The present study used an implicit lexical-decision priming task to examine the integrity of the underlying Semantic Network in AD and SD patients matched for degree of impairment on explicit Semantic memory tasks. The AD (n=11) and SD (n=11) patient groups were matched for age, education, level of dementia and impairment on four explicit Semantic memory tasks. Healthy elderly participants (n=22) were matched for age and education. Semantic priming effects were evaluated for three types of Semantic relationships (attributes, category coordinates, and category superordinates) and compared to lexical associative priming. Healthy controls showed significant priming across all conditions. In contrast, AD patients showed normal superordinate priming, and significant (although somewhat reduced) coordinate priming, but no attribute priming. SD patients showed no priming effect for any Semantic relationship. All groups showed significant associative priming. The results indicate that SD patients do indeed have substantial degradation of Semantic memory, while AD patients have a partially intact Network, accounting for priming in superordinate and coordinate conditions. These findings suggest that AD patients' impairment on explicit Semantic tasks is the product of deficient explicit retrieval in combination with a partially degraded Semantic Network.