Titlebook: Knowledge Science, Engineering and Management; 13th International C Gang Li,Heng Tao Shen,Xiang Zhao Conference proceedings 2020 Springer N

notion

Yag-Capsulotomy

A Contextualized Entity Representation for Knowledge Graph Completioney are usually not complete. This problem arises to the task of missing link prediction whose purpose is to perform link prediction between entities. Knowledge graph embedding has proved to be a highly effective technology in many tasks such as knowledge reasoning, filling in the missing links, and

單色

A Dual Fusion Model for Attributed Network Embeddingrk topology. Existing methods for ANE integrated node attributes and network topology by three fusion strategies: the early fusion (EF), the synchronous fusion (SF) and the late fusion (LF). In fact, different fusion strategies have their own advantages and disadvantages. In this paper, we develop a

TSH582

Attention-Based Knowledge Tracing with Heterogeneous Information Network Embedding. However, the sparsity of students’ practice data still limits the performance and application of knowledge tracing. An additional complication is that the contribution of the answer record to the current knowledge state is different at each time step. To solve these problems, we propose Attention-

MONY

Detecting Statistically Significant Events in Large Heterogeneous Attribute Graphs via Densest Subgrd on social platforms brings great challenges. Moreover, the content usually is informal, lacks of semantics and rapidly spreads in dynamic networks, which makes the situation even worse. Existing approaches, including content-based detection and network structure-based detection, only use limited a

manifestation

Edge Features Enhanced Graph Attention Network for Relation Extractionr to distill the useless information, the pruning strategy is introduced into the dependency tree for preprocessing. However, most hard-pruning strategies for selecting relevant partial dependency structures are too rough and have poor generalization performance. In this work, we propose an extensio

莊嚴(yán)

MMEA: Entity Alignment for Multi-modal Knowledge Graphrelational embeddings between different knowledge graphs, they may fail to effectively describe and integrate the multi-modal knowledge in the real application scenario. To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entit

Needlework

A Hybrid Model with Pre-trained Entity-Aware Transformer for Relation Extractiondopt Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) to encode sentences. However, CNN is difficult to learn long-range dependencies and the parallelization of training RNN is precluded by its sequential nature. In this paper, we propose a novel hybrid model that combines Piece-

極端的正確性

NovEA: A Novel Model of Entity Alignment Using Attribute Triples and Relation Triples However, traditional methods rely on external information and need to construct data features manually. Meanwhile, embedding models do not fully utilize the pertinent information of attributes in the knowledge graphs, which limit the role of attribute information in entity alignment. Considering th

嚴(yán)重傷害

A Robust Representation with Pre-trained Start and End Characters Vectors for Noisy Word Recognitionin the input text can affect negatively on the performance of these model, researchers are gradually paying more attention to the word recognition, which is placed before the downstream task to accomplish those tasks better. Text noise, in terms of words, usually includes random insertion, deletion,