A Semantic Network Encoder for Associated Fact Prediction


A network of concepts that are connected to one another by semantic relations is called a semantic network. Binary semantic network as well as multiplex semantic network are both included in this structure. The associated fact prediction is a link prediction task that aims to infer the implicitly connected facts by mining the high-level representation of the network. This is accomplished through a process known as "association mining." Previous techniques for predicting associated facts placed a large amount of importance on the topological characteristics of the network but did not make use of the information contained in semantic expressions. In this paper, we propose a Semantic Network Encoder (SemNE) that can learn a feature mapping function from binary semantic networks and then apply that function in a pre-training manner to multiplex semantic networks. This function is learned from the binary semantic networks. An embedding encoder and a prediction decoder are both part of the SemNE framework, which is a two-stage architecture. In order to enrich the network representation, it models both the semantic information and the network topology simultaneously. In order to unify the topological feature representations and the semantic feature representations, a method of word self-organization that is based on the factual boundary has been proposed. Experimental results on binary semantic networks show that SemNE achieves the state-of-the-art results in associated fact prediction. Experimental results on multiplex semantic networks show that SemNE is scalable and can effectively improve the performance of existing models. Both sets of results demonstrate that SemNE achieves the state-of-the-art results in associated fact prediction.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE :Chip Design for Turbo Encoder Module for In-Vehicle System - 2018ABSTRACT:This paper studies style and implementation of the Turbo encoder to be an embedded module in the in-vehicle system (IVS) chip. Field programmable
PROJECT TITLE :Design and simulation of CRC encoder and decoder using VHDL - 2018ABSTRACT:Cyclic Redundancy Check (CRC) technique is an efficient error detection technique that used to detect single and burst errors. CRC technique
PROJECT TITLE :Novel Solutions of Delta-Sigma Based Rectifying Encoder - 2017ABSTRACT:This brief presents novel low-complexity styles of rectifying encoders for direct processing of the first-order delta-sigma modulated pulse
PROJECT TITLE : Design and simulation of Turbo encoder in quantum-dot cellular automata - 2016 ABSTRACT: Quantum-dot cellular automata (QCA) could be a potential nanoelectronic technology for info processing. To be considered
PROJECT TITLE : A New XOR-Free Approach for Implementation of Convolutional Encoder - 2016 ABSTRACT: This letter presents a new algorithm to construct an XOR-Free architecture of an influence efficient Convolutional Encoder.

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry