Sensor Assignment to Missions: A Natural Language Knowledge-Based Approach

Author(s):  
Alun Preece
2014 ◽  
Author(s):  
David Braines ◽  
Geeth de Mel ◽  
Chris Gwilliams ◽  
Christos Parizas ◽  
Diego Pizzocaro ◽  
...  

2021 ◽  
Vol 54 (2) ◽  
pp. 1-37
Author(s):  
Dhivya Chandrasekaran ◽  
Vijay Mago

Estimating the semantic similarity between text data is one of the challenging and open research problems in the field of Natural Language Processing (NLP). The versatility of natural language makes it difficult to define rule-based methods for determining semantic similarity measures. To address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods beginning from traditional NLP techniques such as kernel-based methods to the most recent research work on transformer-based models, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network–based methods, and hybrid methods. Discussing the strengths and weaknesses of each method, this survey provides a comprehensive view of existing systems in place for new researchers to experiment and develop innovative ideas to address the issue of semantic similarity.


Author(s):  
D. Kiritsis ◽  
Michel Porchet ◽  
L. Boutzev ◽  
I. Zic ◽  
P. Sourdin

Abstract In this paper we present our experience from the use of two different expert system development environments to Wire-EDM CAD/CAM knowledge based application. The two systems used follow two different AI approaches: the one is based on the constraint propagation theory and provides a natural language oriented programming environment, while the other is a production rule system with backward-forward chaining mechanisms and a conventional-like programming style. Our experience showed that the natural language programming style offers an easier and more productive environment for knowledge based CAD/CAM systems development.


Author(s):  
Saravanakumar Kandasamy ◽  
Aswani Kumar Cherukuri

Semantic similarity quantification between concepts is one of the inevitable parts in domains like Natural Language Processing, Information Retrieval, Question Answering, etc. to understand the text and their relationships better. Last few decades, many measures have been proposed by incorporating various corpus-based and knowledge-based resources. WordNet and Wikipedia are two of the Knowledge-based resources. The contribution of WordNet in the above said domain is enormous due to its richness in defining a word and all of its relationship with others. In this paper, we proposed an approach to quantify the similarity between concepts that exploits the synsets and the gloss definitions of different concepts using WordNet. Our method considers the gloss definitions, contextual words that are helping in defining a word, synsets of contextual word and the confidence of occurrence of a word in other word’s definition for calculating the similarity. The evaluation based on different gold standard benchmark datasets shows the efficiency of our system in comparison with other existing taxonomical and definitional measures.


Author(s):  
Azleena Mohd Kassim ◽  
Yu-N Cheah

Information Technology (IT) is often employed to put knowledge management policies into operation. However, many of these tools require human intervention when it comes to deciding how the knowledge is to be managed. The Sematic Web may be an answer to this issue, but many Sematic Web tools are not readily available for the regular IT user. Another problem that arises is that typical efforts to apply or reuse knowledge via a search mechanism do not necessarily link to other pages that are relevant. Blogging systems appear to address some of these challenges but the browsing experience can be further enhanced by providing links to other relevant posts. In this chapter, the authors present a semantic blogging tool called SEMblog to identify, organize, and reuse knowledge based on the Sematic Web and ontologies. The SEMblog methodology brings together technologies such as Natural Language Processing (NLP), Sematic Web representations, and the ubiquity of the blogging environment to produce a more intuitive way to manage knowledge, especially in the areas of knowledge identification, organization, and reuse. Based on detailed comparisons with other similar systems, the uniqueness of SEMblog lies in its ability to automatically generate keywords and semantic links.


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