graph-based knowledge representation
Multi-leg Searching by Adopting Graph-based Knowledge Representation
... Graph-based knowledge representation is used to explain the concept of maximal join, node similarity and edge ...using graph to show how the similarities are being measured and how the ...
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Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
... tion from relation paths between entity pairs is helpful for link prediction. Note that OPTransE outperforms baselines which do not take relation paths into consideration in most cases. These re- sults demonstrate the ...
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Document Clustering Using Graph Based Document Representation with Constraints
... use graph based document representation with the use of constraints ...document representation and semantic user-desired grouping of data. A graph data structure can easily capture the ...
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Similarity-based Knowledge Graph Queries for Recommendation Retrieval | www.semantic-web-journal.net
... in the Linked Open Data (LOD) cloud can help to im- prove the representation of user tastes in CB engines. Consider the following example for illustration: Sup- pose a user has stated that s/he likes a particular ...
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Graph based Representation and Analysis of Text Document: A Survey of Techniques
... The purpose of POS tagging [13] is to assign the correct lexical category (e.g., noun, verb, article...), to each word in a text. The main difficulty with POS tagging is that the assignment of a word class is often an ...
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Global error reduction in vision-based self-localization using a topological graph representation
... error based on an appropriate error ...metrics based on reprojection error, blur, error ellipse volume, and the perpendicular distance between the camera and a ...author’s knowledge, the topological ...
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Constraint Based Question Answering with Knowledge Graph
... In this work, we propose using Siamese convolutional neural networks (CNN) in Figure 4 to calculate the similarity of two sequences. The model consists of two neural networks taking two sequences as input and maps both ...
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Machine Reading Comprehension Using Structural Knowledge Graph aware Network
... External Knowledge Enhanced MRC Models There are several models that use knowledge for machine comprehension (Yang and Mitchel- l, 2017; Mihaylov and Frank, 2018; Weissenborn, 2017; Bauer et ...of ...
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING
... • Program generators: A Program Generator is a program that enables an individual to create program of their own easily with less effort and programming knowledge. With a program generator a user may only be ...
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Accurate Text Enhanced Knowledge Graph Representation Learning
... The main drawback of the above methods is that they represent the same entity/relation in dif- ferent triples with a unique representation. Un- fortunately, by detailed analyzing the triples in knowledge ...
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Knowledge Driven Paper Recommendation Using Heterogeneous Network Embedding Method
... the knowledge graph to the recommendation model by means of learning is presented in the following ways: first, through TRANSR [14] learning the vec- tors of entities and relationships in the ...
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING
... format, knowledge extraction from intelligence ,some interesting measures or thresholds are applied and exact pattern returned ,presentation in graph trees ...
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Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics
... In Diamond Project [15], the interactive search in large distributed data repositories was addressed. Particularly, the most relevant to medical domain are MassFind [16], FatFind [17] and PathFind [18]. MassFind is an ...
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Relative Representation Of Graph
... If we have to compare the earning of farmer of two different period of time , it would not be fair to directly plot the graph because value of money differ along with time. For e.g Rs1000 in 1947 is economically ...
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A Novel Graph based Compact Representation of Word Alignment
... In this paper, we propose a novel compact representation called weighted bipartite hypergraph to exploit the fertility model, which plays a critical role in word align- ment. However, estimating the probabili- ...
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On the representation number of a crown graph
... by the word 14213243. Thus, when discussing word-representability, one need only consider k-uniform words. The nice property of such words is that any cyclic shift of a k-uniform word represents the same graph ...
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Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning
... Das et al. improve DeepPath (Xiong et al., 2017) to MINERVA (Das et al., 2018), which views KG from QA’s perspective. It gets rid of pretraining, introduces LSTM to memorize paths traversed before, and trains an agent to ...
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Social Text Normalization using Contextual Graph Random Walks
... tion lexicon acquired form unlabeled data using distributional and string similarities. However, our approach is significantly different since we acquire the lexicon using random walks on a contextual similarity ...
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A knowledge-based approach for keywords modeling into a semantic graph
... Lexical, or string-based, similarity measures are the most basic similarity measures, they operate on string sequences and character composition, by calculating the distance metric between two text strings for ...
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Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention
... To automatically alleviate the wrong labelling problem, (Riedel et al., 2010; Hoffmann et al., 2011) apply multi-instance learning. In order to avoid the handcrafted features and errors propa- gated from NLP tools, (Zeng ...
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