A Semantic Layer Querying Tool

2021 ◽  
Author(s):  
Renato Stoffalette João
Keyword(s):  
Semantic Layer

Translating in a forked tongue: Interlinear glosses as a creative device in Japanese translations

2006 ◽  
Vol 1 (2) ◽  
pp. 3-41 ◽  
Author(s):  
Judy Wakabayashi
Keyword(s):  
Good Effect ◽  
Source Text ◽  
State Of Tension ◽  
Semantic Layer

An intriguing contrapuntal device available to Japanese translators and writers is small-font glosses known as rubi, marginalia juxtaposed alongside words or phrases to fulfil a multitude of functions. Moving far beyond their original role of a phonetic aid, rubi are often used bivocally to produce not only two unrelated pronunciations of a word but also an extra semantic layer, helping to transcend the limitations of conventional translational equivalents. Rubi glosses can enhance a word’s expressiveness, emphasize, exaggerate, elucidate or delimit its meaning, convey a different register or speech mode, or act as a paraphrase or inside joke. The double layering and shifting focus provided by different headword-rubi permutations enable translators to convey the meaning of source text concepts while retaining their foreignness, including a representation of the original sound (an aspect that is usually sacrificed when meaning is translated). Rubi can also have a subversive function, destabilizing the headword by qualifying or relativizing its meaning or acting as an intimate critique or commentary. Thus these in-text excurses often exist in a state of tension, an uneasy embrace, with the words to which they are attached. This article examines how rubi enable and exploit to good effect the elaborate interplay amongst different scripts, sound and meaning in Japanese translations, and suggests that some aspects of this double-voiced practice could be adapted by translators in other languages as an avenue for heteroglossic experimentation.

Spam Detection on Social Media Using Semantic Convolutional Neural Network

2018 ◽  
Vol 8 (1) ◽  
pp. 12-26 ◽  
Author(s):  
Gauri Jain ◽  
Manisha Sharma ◽  
Basant Agarwal
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Spam Detection ◽  
Learning Technology ◽  
The Social ◽  
Social Media Text ◽  
Current Article ◽  
Semantic Layer

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.

Abstractions in Intelligent Multimedia Databases

2013 ◽  
pp. 195-220
Author(s):  
Ranjan Parekh ◽  
Nalin Sharda
Keyword(s):  
Semantic Content ◽  
Multimedia Databases ◽  
Low Level ◽  
Layered Architecture ◽  
Automated Inference ◽  
Semantic Concepts ◽  
High Level ◽  
Intelligent Multimedia ◽  
Visual Keywords ◽  
Semantic Layer

Semantic characterization is necessary for developing intelligent multimedia databases, because humans tend to search for media content based on their inherent semantics. However, automated inference of semantic concepts derived from media components stored in a database is still a challenge. The aim of this chapter is to demonstrate how layered architectures and “visual keywords” can be used to develop intelligent search systems for multimedia databases. The layered architecture is used to extract meta-data from multimedia components at various layers of abstractions. While the lower layers handle physical file attributes and low-level features, the upper layers handle high-level features and attempts to remove ambiguities inherent in them. To access the various abstracted features, a query schema is presented, which provides a single point of access while establishing hierarchical pathways between feature-classes. Minimization of the semantic gap is addressed using the concept of “visual keyword” (VK). “Visual keywords” are segmented portions of images with associated low- and high-level features, implemented within a semantic layer on top of the standard low-level features layer, for characterizing semantic content in media components. Semantic information is however predominantly expressed in textual form, and hence is susceptible to the limitations of textual descriptors – viz. ambiguities related to synonyms, homonyms, hypernyms, and hyponyms. To handle such ambiguities, this chapter proposes a domain specific ontology-based layer on top of the semantic layer, to increase the effectiveness of the search process.

Semantic and Blockchain Technology

2021 ◽  
pp. 50-71
Author(s):  
Aswini R. ◽  
Padmapriya N.
Keyword(s):  
Digital Signature ◽  
Third Party ◽  
The Internet ◽  
Information Distribution ◽  
The Past ◽  
Blockchain Technology ◽  
Distributed Ledger ◽  
Semantic Layer

Blockchain is a distributed ledger with the ability of keeping up the uprightness of exchanges by decentralizing the record among participating clients. The key advancement is that it enables its users to exchange resources over the internet without the requirement for a centralised third party. Also, each 'block' is exceptionally associated with the past blocks by means of digital signature which implies that creation a change to a record without exasperating the previous records in the chain is beyond the realm of imagination, in this way rendering the data tamper-proof. A semantic layer based upon a blockchain framework would join the advantages of adaptable administration disclosure and approval by consensus. This chapter examines the engineering supporting the blockchain and portrays in detail how the information distribution is done, the structure of the block itself, the job of the block header, the block identifier, and the idea of the Genesis block.

Towards the Development of Smart Spaces-Based Socio-Cyber-Medicine Systems

2020 ◽  
pp. 395-416
Author(s):  
Yulia V. Zavyalova ◽  
Dmitry G. Korzun ◽  
Alexander Yu. Meigal ◽  
Alexander V. Borodin
Keyword(s):  
Medical Information ◽  
Semantic Network ◽  
Collaborative Work ◽  
Healthcare Services ◽  
Social World ◽  
Medical Professionals ◽  
End User ◽  
The Social ◽  
Source Data ◽  
Semantic Layer

The concept of Cyber-Medicine System (CMS) is applied to research and development of medical information systems where the Internet is used to integrate medical devices and healthcare services into the system and to connect patients and medical professionals. In this paper, the authors generalize the concept to Socio-CMS, where the social world is added to the fusion of physical and cyber worlds. The social world affects the end-user activity and provides opportunities for collaborative work. A semantic layer is introduced to integrate all system and domain objects from the three digitalized worlds into a smart space: multi-source data, ongoing processes, situation attributes, reasoning rules, and human activity. All objects are dynamically related, leading to such a knowledge-rich structure as a semantic network. Data mining and analytics apply semantic algorithms for this network, including the Big Data case. The derived knowledge feeds construction of advanced healthcare services for supporting medical professionals and for assisting patients.

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