[PDF] Top 20 Graph Pattern Entity Ranking Model for Knowledge Graph Completion
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Graph Pattern Entity Ranking Model for Knowledge Graph Completion
... edge graph embedding models and the following two models are observed feature ...Node+LinkFeat model (Toutanova and Chen, 2015), although this model is very simple (high MRRs imply that the ... See full document
10
Improved Knowledge Graph Embedding Using Background Taxonomic Information
... factorization model for knowledge graph completion when background taxonomic information (in terms of sub- classes and subproperties) is ...to entity embeddings of SimplE, a ... See full document
8
Entity Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval
... The interaction based models learn word-level interaction patterns from query-document pairs. ARC-II (Hu et al., 2014) and MatchPyramind (Pang et al., 2016) uti- lize Convolutional Neural Network (CNN) to capture ... See full document
11
A Capsule Network based Embedding Model for Knowledge Graph Completion and Search Personalization
... Evaluation protocol: Following Bordes et al. (2013), for each valid test triple (s, r, o), we re- place either s or o by each of all other entities to create a set of corrupted triples. We use the “Fil- tered” setting ... See full document
10
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
... For specific evaluation metrics, we employ the widely used mean rank (MR) and Hits@10 in the experiments. Mean rank indicates the aver- age rank of correct entities and Hits@10 means the proportion of correct entities ... See full document
10
Learning Sequence Encoders for Temporal Knowledge Graph Completion
... in knowledge graph completion ...prediction model for link prediction that assumes that changes to a KG are introduced by incoming ...event graph and used to predict the existence of ... See full document
6
Semi supervised Entity Alignment via Joint Knowledge Embedding Model and Cross graph Model
... semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model (KECG), which combines the above two types of ...cross-graph model to ... See full document
10
Abstract Graphs and Abstract Paths for Knowledge Graph Completion
... Entity types and the type of the domain and range of a relation have been proven to be useful for improving link prediction models. We inves- tigate here the hypothesis that by relying on the fact that such strong ... See full document
11
An Open-World Extension to Knowledge Graph Completion Models
... new entity, we aggregate its name and description into a text-based entity ...to graph-based embedding space, where we can now apply the graph-based model for predicting ...KGC ... See full document
8
TuckER: Tensor Factorization for Knowledge Graph Completion
... cannot model asymmetric re- lations; and transitive models such as TransE (Bor- des et ...on entity and relation em- bedding dimensionality ...TuckER model with entity embeddings of ... See full document
10
Knowledge Graph Completion via Complex Tensor Factorization
... of entity pairs ...the knowledge graph facts by exctracting them from textual data, as does Toutanova et ...prior knowledge in the form of Horn clauses in the objective loss of the Universal ... See full document
38
Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large Scale Datasets
... the model. For this, we keep the model as is, but with probability p , instead of corrupting the head or the tail of the triple with an entity chosen uniformly at random, we corrupt it with an ... See full document
6
Multi Channel Graph Neural Network for Entity Alignment
... Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by ro- bustly encoding two KGs via multiple chan- ...KG completion and ... See full document
10
Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity seeking Queries
... language model needs to build a bridge between the formal r and the textual b r, so that (un)likely r’s have (small) large ...a pattern-based ap- proach (Nakashole et ... See full document
11
Normalized Entity Graph for Computing Local Coherence
... The entity graph is a bipartite graph where one set of nodes represents entities and the other set of nodes represents the sentences of a ...their entity graph which take the number of ... See full document
5
Encoding World Knowledge in the Evaluation of Local Coherence
... The second task is summary coherence rating, in which, given a pair of summaries about the same set of source documents, we determine the rank- ing of these two summaries based on their de- grees of coherence. The ... See full document
10
Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications
... Models We implement all methods using the same loss and optimization for training, i.e., Ada- Grad and the binary cross-entropy loss. We use validation data to tune the hyperparameters and use a grid search to find the ... See full document
12
TransG : A Generative Model for Knowledge Graph Embedding
... There are many works to improve translation- based methods by considering other information. For instance, (Guo et al., 2015) aims at discov- ering the geometric structure of the embedding space to make it semantically ... See full document
10
A Graph based Method for Entity Linking
... Simplified Lesk algorithm (sLesk) (Lesk, 1986; Banerjee and Pedersen, 2002; Agirre and Ed- monds, 2006) is a well-known disambiguation algorithm which is similar to our graph-based method with in-degree measure. ... See full document
9
Regular number of line block graph of a graph
... is the minimum cardinality of total dominating set of . A set with minimum cardinality among all the maximal is called minimum independent . The cardinality of a minimum independent dominating set is called independent ... See full document
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