[PDF] Top 20 Knowledge Graph and Text Jointly Embedding
Has 10000 "Knowledge Graph and Text Jointly Embedding" found on our website. Below are the top 20 most common "Knowledge Graph and Text Jointly Embedding".
Knowledge Graph and Text Jointly Embedding
... name graph may contaminate the knowledge ...the text but we do not have the complete set, which will break the semantic balance of word ...name graph intervenes, the unsupervised word ... See full document
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TransG : A Generative Model for Knowledge Graph Embedding
... the embedding space to make it semantically ...between knowledge and texts, with a loss function for jointly modeling knowledge graph and text re- ... See full document
10
Text Generation from Knowledge Graphs with Graph Transformers
... Implementation Details Our models are trained end-to-end to minimize the negative joint log like- lihood of the target text vocabulary and the copied entity indices. We use SGD optimization with mo- mentum (Qian, ... See full document
10
GAKE: Graph Aware Knowledge Embedding
... learning knowledge representations, most of them mainly consider knowledge base as a set of triples and models each triple separately and ...whole knowledge base could be regarded as a directed ... See full document
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Embedding Open domain Common sense Knowledge from Text
... embedded knowledge graph obtained by Equation 3 relies on distributional information in order to create an op- timal embedding for each concept and each lexical relation the ...the knowledge ... See full document
8
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation
... cognition knowledge of Chinese and the contextual information, users are able to consume the spam information, even when some characters in the content are intentionally mutated into their similar variations ... See full document
10
Knowledge Graph Embedding with Numeric Attributes of Entities
... Knowledge Graph (KG) embedding projects entities and relations into low dimensional vector space, which has been successfully applied in KG completion ...previous embedding approaches only ... See full document
5
DBee: A Database for Creating and Managing Knowledge Graphs and Embeddings
... Current machine learning systems such as Keras (Chollet et al., 2015), and PyTorch (Paszke et al., 2017) focus mostly on exposing their user to the definition of neural architectures, abstracting away the computation ... See full document
9
Knowledge graph embedding by dynamic translation
... In this paper, we only combine our DT principle with classical translation-based models. One of our future works is to incorporate more information such as the relation paths [31], [32] and the textual descriptions on ... See full document
10
Representing Text for Joint Embedding of Text and Knowledge Bases
... of knowledge base and textual in- formation was first shown to outperform either source alone in the framework of path-ranking al- gorithms in a combined knowledge base and text graph (Lao et ... See full document
11
Improving Knowledge Graph Embedding Using Simple Constraints
... Recent years have seen growing interest in learn- ing distributed representations for entities and re- lations in KGs, a.k.a. KG embedding. Early works on this topic devised very simple models to learn such ... See full document
12
Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process
... Topic models, such as Probabilistic Latent Seman- tic Analysis (PLSA) (Hofmann, 2017) and La- tent Dirichlet Allocation (LDA) (Blei et al., 2003), play significant roles in helping machines inter- pret text ... See full document
11
TransGate: Knowledge Graph Embedding with Shared Gate Structure
... A number of works attempt to improve knowledge graph embedding in different ways. Some models explore different loss function to improve embeddings. Zhou et al. (Zhou et al. 2017) propose a ... See full document
8
Accurate Text Enhanced Knowledge Graph Representation Learning
... enhance knowledge representation is to utilize entity descriptions of entities and ...and text using entity ...a knowledge graph by modelling both knowl- edge triples and entity ...hance ... See full document
11
Jointly Embedding Knowledge Graphs and Logical Rules
... of observed triples per relation. During grounding, we select those ground rules with at least one triple observed. Grounding is required only once before learning, and is not included during the iterations. Extensions. ... See full document
11
Entity Disambiguation by Knowledge and Text Jointly Embedding
... Entity Disambiguation Entity disambiguation methods roughly fall into two categories: local approaches and collective approaches. Local ap- proaches disambiguate each mention in a docu- ment separately. For example, ... See full document
10
Jointly Embedding Entities and Text with Distant Supervision
... for knowledge base entities and concepts is becoming increasingly important for NLP applica- ...for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of ... See full document
12
Jointly Embedding Relations and Mentions for Knowledge Population
... joint embedding model for predicting relations between a pair of entities in the scenario of rela- tion ...either knowledge bases or free ...in knowledge reposito- ries and the mentions of relations ... See full document
6
Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
... This paper concentrates on KG-specific neighborhood ag- gregators, which is of practical importance but only received limited focus (Hamaguchi et al. 2017). To the best of our knowledge, neither conventional ... See full document
8
Improved Knowledge Graph Embedding Using Background Taxonomic Information
... domains, embedding models, such as tensor factorization models, can be used to make predictions of new ...ing knowledge graph completion method enables injection of taxonomic ...public ... See full document
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