Learning Non-Taxonomic Relations on Demand for Ontology Extension

2014 ◽  
Vol 24 (08) ◽  
pp. 1159-1175 ◽  
Author(s):  
Yan Xu ◽  
Ge Li ◽  
Lili Mou ◽  
Yangyang Lu
Keyword(s):  
Semantic Relatedness ◽  
Semantic Relations ◽  
Learning Approaches ◽  
Important Research ◽  
Domain Specific ◽  
On Demand ◽  
Novel Approach ◽  
Important Research Topic ◽  
Good Potential ◽  
Taxonomic Relations

Learning non-taxonomic relations becomes an important research topic in ontology extension. Most of the existing learning approaches are mainly based on expert crafted corpora. These approaches are normally domain-specific and the corpora acquisition is laborious and costly. On the other hand, based on the static corpora, it is not able to meet personalized needs of semantic relations discovery for various taxonomies. In this paper, we propose a novel approach for learning non-taxonomic relations on demand. For any supplied taxonomy, it can focus on the segment of the taxonomy and collect information dynamically about the taxonomic concepts by using Wikipedia as a learning source. Based on the newly generated corpus, non-taxonomic relations are acquired through three steps: a) semantic relatedness detection; b) relations extraction between concepts; and c) relations generalization within a hierarchy. The proposed approach is evaluated on three different predefined taxonomies and the experimental results show that it is effective in capturing non-taxonomic relations as needed and has good potential for the ontology extension on demand.

scholarly journals An All-Batch Loss for Constructing Prediction Intervals

2021 ◽  
Vol 11 (4) ◽  
pp. 1728
Author(s):  
Hua Zhong ◽  
Li Xu
Keyword(s):  
Gradient Descent ◽  
State Of The Art ◽  
Prediction Interval ◽  
Feedforward Neural Networks ◽  
Important Research ◽  
Likelihood Principle ◽  
High Quality ◽  
Construction Methods ◽  
Important Research Topic ◽  
Benchmark Datasets

The prediction interval (PI) is an important research topic in reliability analyses and decision support systems. Data size and computation costs are two of the issues which may hamper the construction of PIs. This paper proposes an all-batch (AB) loss function for constructing high quality PIs. Taking the full advantage of the likelihood principle, the proposed loss makes it possible to train PI generation models using the gradient descent (GD) method for both small and large batches of samples. With the structure of dual feedforward neural networks (FNNs), a high-quality PI generation framework is introduced, which can be adapted to a variety of problems including regression analysis. Numerical experiments were conducted on the benchmark datasets; the results show that higher-quality PIs were achieved using the proposed scheme. Its reliability and stability were also verified in comparison with various state-of-the-art PI construction methods.

A Bibliometric Review on Outcome-Based Learning for Graduate Employability: Mapping the Research Front

2021 ◽  
pp. 002205742110164
Author(s):  
Mohammad Zahir Raihan ◽  
Md. Abul Kalam Azad
Keyword(s):  
Research Trend ◽  
Future Research ◽  
Research Front ◽  
Important Research ◽  
R Software ◽  
Future Research Agenda ◽  
Graduate Employability ◽  
Important Research Topic ◽  
Bibliometric Review ◽  
First Time

The outcome-based learning for graduate employability in higher education has been an important research topic among the policymakers, academicians, and researchers over the years. Yet, no bibliometric review on this topic has been published. This study, for the first time, examines bibliometric analysis on this topic examining current research trend and future research agenda. The bibliometrix package in R software and VOSviewer software are used for visualization and interpretation of results. A content analysis is performed to manually examine the bibliometric results.

Conformal Optimization Algorithm for Undevelopable Surfaces and its Application

2014 ◽  
Vol 602-605 ◽  
pp. 3570-3574
Author(s):  
Zhen Hua Luo ◽  
Fen Jiang
Keyword(s):  
Approximation Algorithms ◽  
Optimization Algorithm ◽  
Manufacturing Process ◽  
Research Topic ◽  
Important Research ◽  
Computer Aided Geometric Design ◽  
Industrial Manufacturing ◽  
Important Research Topic ◽  
Computer Aided ◽  
Planar Materials

In the industrial manufacturing process, most kinds of surfaces are processed by planar materials, but undevelopable surfaces are difficult develop to the plane. The approximation algorithms to develop a undevelopable surface is an important research topic in Computer Aided Geometric Design (CAGD). In this paper, we propose a new approximation algorithms based optimization algorithm. We guarantee the deformation vector make the minimum changes during the developing process. In the paper, some numerical example are given and the can illustrate the our method is effective.

To Predict or Not to Predict: Influences of Task and Strategy on the Processing of Semantic Relations

2007 ◽  
Vol 19 (8) ◽  
pp. 1259-1274 ◽  
Author(s):  
Dietmar Roehm ◽  
Ina Bornkessel-Schlesewsky ◽  
Frank Rösler ◽  
Matthias Schlesewsky
Keyword(s):  
Semantic Relatedness ◽  
Presentation Mode ◽  
Judgment Task ◽  
Sentence Context ◽  
Task Demands ◽  
Event Related Potential ◽  
Semantic Relations ◽  
Decision Task ◽  
Lexical Semantic ◽  
Experimental Environment

We report a series of event-related potential experiments designed to dissociate the functionally distinct processes involved in the comprehension of highly restricted lexical-semantic relations (antonyms). We sought to differentiate between influences of semantic relatedness (which are independent of the experimental setting) and processes related to predictability (which differ as a function of the experimental environment). To this end, we conducted three ERP studies contrasting the processing of antonym relations (black-white) with that of related (black-yellow) and unrelated (black-nice) word pairs. Whereas the lexical-semantic manipulation was kept constant across experiments, the experimental environment and the task demands varied: Experiment 1 presented the word pairs in a sentence context of the form The opposite of X is Y and used a sensicality judgment. Experiment 2 used a word pair presentation mode and a lexical decision task. Experiment 3 also examined word pairs, but with an antonymy judgment task. All three experiments revealed a graded N400 response (unrelated > related > antonyms), thus supporting the assumption that semantic associations are processed automatically. In addition, the experiments revealed that, in highly constrained task environments, the N400 gradation occurs simultaneously with a P300 effect for the antonym condition, thus leading to the superficial impression of an extremely “reduced” N400 for antonym pairs. Comparisons across experiments and participant groups revealed that the P300 effect is not only a function of stimulus constraints (i.e., sentence context) and experimental task, but that it is also crucially influenced by individual processing strategies used to achieve successful task performance.

Automatic Extraction of Semantic Relations from Wikipedia

2015 ◽  
Vol 24 (02) ◽  
pp. 1540010 ◽  
Author(s):  
Patrick Arnold ◽  
Erhard Rahm
Keyword(s):  
Finite State Machines ◽  
Background Knowledge ◽  
Semantic Relations ◽  
Automatic Extraction ◽  
State Machines ◽  
High Quality ◽  
Novel Approach ◽  
Finite State ◽  
Semantic Ontology

We introduce a novel approach to extract semantic relations (e.g., is-a and part-of relations) from Wikipedia articles. These relations are used to build up a large and up-to-date thesaurus providing background knowledge for tasks such as determining semantic ontology mappings. Our automatic approach uses a comprehensive set of semantic patterns, finite state machines and NLP techniques to extract millions of relations between concepts. An evaluation for different domains shows the high quality and effectiveness of the proposed approach. We also illustrate the value of the newly found relations for improving existing ontology mappings.

Global coronal and heliospheric magnetic field modelling for Solar Orbiter

2021 ◽  
Author(s):  
Thomas Wiegelmann ◽  
Thomas Neukirch ◽  
Iulia Chifu ◽  
Bernd Inhester
Keyword(s):  
Magnetic Field ◽  
Solar Rotation ◽  
Coronal Magnetic Field ◽  
Important Research ◽  
Parker Spiral ◽  
Important Research Topic ◽  
Mhd Equilibria ◽  
Solar Wind Flow ◽  
Nonlinear Force

<p>Computing the solar coronal magnetic field and plasma<br>environment is an important research topic on it's own right<br>and also important for space missions like Solar Orbiter to<br>guide the analysis of remote sensing and in-situ instruments.<br>In the inner solar corona plasma forces can be neglected and<br>the field is modelled under the assumption of a vanishing<br>Lorentz-force. Further outwards (above about two solar radii)<br>plasma forces and the solar wind flow has to be considered.<br>Finally in the heliosphere one has to consider that the Sun<br>is rotating and the well known Parker-spiral forms.<br>We have developed codes based on optimization principles<br>to solve nonlinear force-free, magneto-hydro-static and<br>stationary MHD-equilibria. In the present work we want to<br>extend these methods by taking the solar rotation into account.</p>

Semantic relations in the field of retailing

Terminology ◽  
2007 ◽  
Vol 13 (2) ◽  
pp. 201-223 ◽  
Author(s):  
Jeanne Eugenie Dancette
Keyword(s):  
Semantic Relations ◽  
Critical Importance ◽  
Linguistic Markers ◽  
Property Relations ◽  
Domain Specific ◽  
Generic Agent ◽  
Learning From Texts ◽  
Thematic Information ◽  
Specialized Texts ◽  
Specific Agent

Understanding the semantic relations between terms in specialized texts is of critical importance in translation and terminology, and generally speaking in learning from texts. Our research highlights the advantages of formalizing them in order to build hierarchies and sets of horizontal conceptual relations (i.e. process-oriented relations) for knowledge acquisition. This paper discusses a method for extracting domain-specific semantic relations in specialized texts. Obviously, some texts are more appropriate than others in this regard. ‘Knowledge-rich’ texts such as encyclopaedia and textbooks are considered good materials because of the density and richness of thematic information. Considering them as such, we used the encyclopaedic articles of the Dictionnaire analytique de la distribution/Analytical Dictionary of Retailing. We retrieved over 3000 terms semantically related to all 350 headwords of the Dictionary, and grouped them into 28 classes of relations (paradigmatic, i.e. generic, specific, agent, goal, instrument, recipient, location, etc., and also syntagmatic, such as related verbs and adjectives). This paper discusses in particular the generic, agent and property relations and examines the linguistic markers that permit their retrieval.

scholarly journals The Rise of Political Influencers— Perspectives on a Trend Towards Meaningful Content

2021 ◽  
Vol 6 ◽  
Author(s):  
Magdalena Riedl ◽  
Carsten Schwemmer ◽  
Sandra Ziewiecki ◽  
Lisa M. Ross
Keyword(s):  
Social Media ◽  
Political Communication ◽  
Political Ideology ◽  
Empirical Studies ◽  
Political Engagement ◽  
Opinion Leaders ◽  
Important Research ◽  
Conspiracy Theories ◽  
The Past ◽  
Important Research Topic

Despite an increasing information overflow in the era of digital communication, influencers manage to draw the attention of their followers with an authentic and casual appearance. Reaching large audiences on social media, they can be considered as digital opinion leaders. In the past, they predominantly appeared as experts for topics like fashion, sports, or gaming and used their status to cooperate with brands for marketing purposes. However, since recently influencers also turn towards more meaningful and political content. In this article, we share our perspective on the rise of political influencers using examples of sustainability and related topics covered on Instagram. By applying a qualitative observational approach, we illustrate how influencers make political communication look easy, while at the same time seamlessly integrating product promotions in their social media feeds. In this context, we discuss positive aspects of political influencers like contributions to education and political engagement, but also negative aspects such as the potential amplification of radical political ideology or conspiracy theories. We conclude by highlighting political influencers as an important research topic for conceptual and empirical studies in the future.

scholarly journals The happy victimizer phenomenon: Not found here

2014 ◽  
Vol 46 (1) ◽  
pp. 145-161
Author(s):  
Ana Jevtic ◽  
Jovan Miric
Keyword(s):  
Emotional Development ◽  
Positive Emotions ◽  
Negative Emotions ◽  
Age Groups ◽  
Research Topic ◽  
Important Research ◽  
Important Research Topic ◽  
Significant Difference ◽  
Moral Explanations

Children?s attribution of emotions to a moral transgressor is an important research topic in the psychology of moral and emotional development. This is especially because of the so-called Happy Victimizer Phenomenon (HVP) where younger children attribute positive emotions to a moral transgressor described in a story. In the two studies that we have conducted (children aged 5, 7 and 9, 20 of each age; 10 of each age in the second study) we have tested the possible influence of the fear of sanctions and the type of transgression (stealing and inflicting body injuries) on the attribution of emotions. Children were presented with stories that described transgressions and they were asked to answer how the transgressor felt. The fear of sanctions did not make a significant difference in attribution but the type of transgression did - more negative emotions were attributed for inflicting body injuries than for stealing. Positive emotions were explained with situational-instrumental explanations in 84% of cases while negative emotions were explained with moral explanations in 63,5%. Girls attributed more positive emotions (61%) than boys (39%). However, our main finding was that, for the aforementioned age groups, we did not find the HVP effect although it has regularly been registered in foreign studies. This finding denies the generalizability of the phenomenon and points to the significance of disciplining styles and, even more so, culture for children?s attribution of emotions to moral transgressors.

scholarly journals Machine learning approaches to calibrate individual-based infectious disease models

2021 ◽  
Author(s):  
Theresa Reiker ◽  
Monica Golumbeanu ◽  
Andrew Shattock ◽  
Lydia Burgert ◽  
Thomas A. Smith ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Transmission ◽  
Goodness Of Fit ◽  
Epidemiological Data ◽  
Bayesian Optimization ◽  
Model Complexity ◽  
Learning Approaches ◽  
Dimensional Parameter ◽  
Novel Approach ◽  
Transmission Models

AbstractIndividual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose a novel approach to calibrate disease transmission models via a Bayesian optimization framework employing machine learning emulator functions to guide a global search over a multi-objective landscape. We demonstrate our approach by application to an established individual-based model of malaria, optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Outperforming other calibration methodologies, the new approach quickly reaches an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability.One Sentence SummaryWe propose a novel, fast, machine learning-based approach to calibrate disease transmission models that outperforms other methodologies

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