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Proceedings ArticleDOI

A Knowledge Graph Based Approach for Automatic Speech and Essay Summarization

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TLDR
The method uses speech recognition as well as Named Entity Recognition to identify entities from spoken content to create optimized Knowledge Graphs in the English Language.
Abstract
Every day, big amounts of unstructured data is generated. This data is of the form of essays, research papers, speeches, patents, scholastic articles, book chapters etc. In today’s world, it is very important to extract key patterns from huge text passages or verbal speeches. This paper proposes a novel method for summarizing multilingual vocal as well as written paragraphs and speeches, using semantic Knowledge Graphs. Using the proposed model, big text extracts or speeches can be summarized for better understanding and analysis. The method uses speech recognition as well as Named Entity Recognition to identify entities from spoken content to create optimized Knowledge Graphs in the English Language.

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References
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Patent

Methods and systems for automated semantic knowledge leveraging graph theoretic analysis and the inherent structure of communication

Aditya Damle
TL;DR: In this paper, a system that processes a collection of one or more documents and thereby constructs a knowledge base is described, which leverages innovative graph theoretical analysis of documents leveraging the inherent structure in communication.
Journal ArticleDOI

Review of empirical research on knowledge management practices and firm performance

TL;DR: It is demonstrated that innovation is a likely outcome of utilization of KM practices, but there are numerous other factors that influence the financial performance figures, and organizations should pay attention to specific KM leadership attributes and organizational arrangements in order to achieve firm performance through KM.
Journal ArticleDOI

A literature review on knowledge management in SMEs

TL;DR: In this article, a systematic review of the literature on knowledge management in SMEs and SMEs networks is presented, highlighting the state-of-the-art knowledge management techniques.
Proceedings ArticleDOI

Leveraging Knowledge Graphs for Web-Scale Unsupervised Semantic Parsing

TL;DR: This paper uses an iterative graph crawl algorithm to bootstrap a web-scale semantic parser with no requirement for semantic schema design, no data collection, and no manual annotations, and uses a maximum-a-posteriori unsupervised adaptation technique on sample data from a specific domain to refine the parsers.
Journal ArticleDOI

Sematch: Semantic similarity framework for Knowledge Graphs

TL;DR: The framework provides a number of similarity tools and datasets, and allows users to compute semantic similarity scores of concepts, words, and entities, as well as to interact with Knowledge Graphs through SPARQL queries.
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