scholarly journals Knowledge based Semantic Annotation Generation of Music

2012 ◽  
Vol 47 (8) ◽  
pp. 8-12 ◽  
Author(s):  
Sunitha Abburu
Keyword(s):  
Semantic Annotation ◽  
Knowledge Based

scholarly journals Educing knowledge from text: semantic information extraction of spatial concepts and places

2021 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Evangelos Papadias ◽  
Margarita Kokla ◽  
Eleni Tomai
Keyword(s):  
Natural Language ◽  
Domain Knowledge ◽  
Semantic Information ◽  
Semantic Annotation ◽  
Semantic Search ◽  
Web Based ◽  
Spatial Concepts ◽  
Knowledge Based ◽  
Relevant Domain ◽  
Semantic Visualization

Abstract. A growing body of geospatial research has shifted the focus from fully structured to semistructured and unstructured content written in natural language. Natural language texts provide a wealth of knowledge about geospatial concepts, places, events, and activities that needs to be extracted and formalized to support semantic annotation, knowledge-based exploration, and semantic search. The paper presents a web-based prototype for the extraction of geospatial entities and concepts, and the subsequent semantic visualization and interactive exploration of the extraction results. A lightweight ontology anchored in natural language guides the interpretation of natural language texts and the extraction of relevant domain knowledge. The approach is applied on three heterogeneous sources which provide a wealth of spatial concepts and place names.

A Knowledge-Based Deep Learning Architecture for Aspect-Based Sentiment Analysis

2021 ◽  
pp. 2150046
Author(s):  
Georgios Alexandridis ◽  
John Aliprantis ◽  
Konstantinos Michalakis ◽  
Konstantinos Korovesis ◽  
Panagiotis Tsantilas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Semantic Annotation ◽  
Affective State ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Knowledge Based ◽  
Analysis Task ◽  
Online Reputation

The task of sentiment analysis tries to predict the affective state of a document by examining its content and metadata through the application of machine learning techniques. Recent advances in the field consider sentiment to be a multi-dimensional quantity that pertains to different interpretations (or aspects), rather than a single one. Based on earlier research, the current work examines the said task in the framework of a larger architecture that crawls documents from various online sources. Subsequently, the collected data are pre-processed, in order to extract useful features that assist the machine learning algorithms in the sentiment analysis task. More specifically, the words that comprise each text are mapped to a neural embedding space and are provided to a hybrid, bi-directional long short-term memory network, coupled with convolutional layers and an attention mechanism that outputs the final textual features. Additionally, a number of document metadata are extracted, including the number of a document’s repetitions in the collected corpus (i.e. number of reposts/retweets), the frequency and type of emoji ideograms and the presence of keywords, either extracted automatically or assigned manually, in the form of hashtags. The novelty of the proposed approach lies in the semantic annotation of the retrieved keywords, since an ontology-based knowledge management system is queried, with the purpose of retrieving the classes the aforementioned keywords belong to. Finally, all features are provided to a fully connected, multi-layered, feed-forward artificial neural network that performs the analysis task. The overall architecture is compared, on a manually collected corpus of documents, with two other state-of-the-art approaches, achieving optimal results in identifying negative sentiment, which is of particular interest to certain parties (like for example, companies) that are interested in measuring their online reputation.

scholarly journals Text Semantic Annotation: A Distributed Methodology Based on Community Coherence

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 160
Author(s):  
Christos Makris ◽  
Georgios Pispirigos ◽  
Michael Angelos Simos
Keyword(s):  
Social Sciences ◽  
Community Detection ◽  
Chemical Engineering ◽  
Semantic Annotation ◽  
Digital Marketing ◽  
Bag Of Words ◽  
Detection Algorithms ◽  
Text Annotation ◽  
Knowledge Based ◽  
Scientific Fields

Text annotation is the process of identifying the sense of a textual segment within a given context to a corresponding entity on a concept ontology. As the bag of words paradigm’s limitations become increasingly discernible in modern applications, several information retrieval and artificial intelligence tasks are shifting to semantic representations for addressing the inherent natural language polysemy and homonymy challenges. With extensive application in a broad range of scientific fields, such as digital marketing, bioinformatics, chemical engineering, neuroscience, and social sciences, community detection has attracted great scientific interest. Focusing on linguistics, by aiming to identify groups of densely interconnected subgroups of semantic ontologies, community detection application has proven beneficial in terms of disambiguation improvement and ontology enhancement. In this paper we introduce a novel distributed supervised knowledge-based methodology employing community detection algorithms for text annotation with Wikipedia Entities, establishing the unprecedented concept of community Coherence as a metric for local contextual coherence compatibility. Our experimental evaluation revealed that deeper inference of relatedness and local entity community coherence in the Wikipedia graph bears substantial improvements overall via a focus on accuracy amelioration of less common annotations. The proposed methodology is propitious for wider adoption, attaining robust disambiguation performance.

scholarly journals Creating and Exploring Semantic Annotation for Behaviour Analysis

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2778 ◽  
Author(s):  
Kristina Yordanova ◽  
Frank Krüger
Keyword(s):  
Interrater Reliability ◽  
Recognition Performance ◽  
Semantic Annotation ◽  
Ground Truth ◽  
Training Data ◽  
Grand Challenge ◽  
Behaviour Analysis ◽  
Knowledge Based ◽  
Novel Approach

Providing ground truth is essential for activity recognition and behaviour analysis as it is needed for providing training data in methods of supervised learning, for providing context information for knowledge-based methods, and for quantifying the recognition performance. Semantic annotation extends simple symbolic labelling by assigning semantic meaning to the label, enabling further reasoning. In this paper, we present a novel approach to semantic annotation by means of plan operators. We provide a step by step description of the workflow to manually creating the ground truth annotation. To validate our approach, we create semantic annotation of the Carnegie Mellon University (CMU) grand challenge dataset, which is often cited, but, due to missing and incomplete annotation, almost never used. We show that it is possible to derive hidden properties, behavioural routines, and changes in initial and goal conditions in the annotated dataset. We evaluate the quality of the annotation by calculating the interrater reliability between two annotators who labelled the dataset. The results show very good overlapping (Cohen’s κ of 0.8) between the annotators. The produced annotation and the semantic models are publicly available, in order to enable further usage of the CMU grand challenge dataset.

Is the Personal Always Political?

2017 ◽  
Vol 38 (3) ◽  
pp. 133-143 ◽  
Author(s):  
Danny Osborne ◽  
Yannick Dufresne ◽  
Gregory Eady ◽  
Jennifer Lees-Marshment ◽  
Cliff van der Linden
Keyword(s):  
Survey Data ◽  
National Survey ◽  
Negative Relationship ◽  
Openness To Experience ◽  
Political Sophistication ◽  
Knowledge Based

Abstract. Research demonstrates that the negative relationship between Openness to Experience and conservatism is heightened among the informed. We extend this literature using national survey data (Study 1; N = 13,203) and data from students (Study 2; N = 311). As predicted, education – a correlate of political sophistication – strengthened the negative relationship between Openness and conservatism (Study 1). Study 2 employed a knowledge-based measure of political sophistication to show that the Openness × Political Sophistication interaction was restricted to the Openness aspect of Openness. These studies demonstrate that knowledge helps people align their ideology with their personality, but that the Openness × Political Sophistication interaction is specific to one aspect of Openness – nuances that are overlooked in the literature.

Connectives Increase Knowledge-Based Inferences

1994 ◽  
Author(s):  
Gregory Barker ◽  
Keith Millis ◽  
Jonathan M. Golding
Keyword(s):  
Knowledge Based

The contribution of low-level sensory features versus knowledge-based factors for encoding of objects in working memory

2013 ◽  
Author(s):  
Valerio Santangelo ◽  
Simona Arianna Di Francesco ◽  
Serena Mastroberardino ◽  
Emiliano Macaluso
Keyword(s):  
Working Memory ◽  
Low Level ◽  
Knowledge Based ◽  
Sensory Features
Sign in / Sign up

Export Citation Format

Share Document