Traditional knowledge reasoning methods based on representation learning can only be used for closed-world knowledge reasoning. Conducting open-world knowledge reasoning effectively is a hot issue currently. Therefore， a knowledge reasoning model based on path and enhanced triplet text， named PEOR （Path and Enhanced triplet text for Open world knowledge Reasoning）， was proposed. First， multiple paths generated by structures between entity pairs and enhanced triplets generated by individual entity neighborhood structures were utilized. Among then， the path text was obtained by concatenating the text of triplets in the path， and the enhanced triplet text was obtained by concatenating the text of head entity neighborhood， relation， and tail entity neighborhood. Then， BERT （Bidirectional Encoder Representations from Transformers） was employed to encode the path text and enhanced triplet text separately. Finally， semantic matching attention calculation was performed using path vectors and triplet vectors， followed by aggregation of semantic information from multiple paths using semantic matching attention. Comparison experimental results on three open-world knowledge graph datasets： WN18RR， FB15k-237， and NELL-995 show that compared with suboptimal model BERTRL （BERT-based Relational Learning）， the proposed model has Hits@10 （Hit ratio） metric improved by 2.6， 2.3 and 8.5 percentage points， respectively， validating the effectiveness of the proposed model.

To address the problem that relevant models typically only model characters and relevant vocabulary without fully utilizing the unique glyph structure information and entity type information of Chinese characters， a model that integrates prior knowledge and glyph features for Named Entity Recognition （NER） task was proposed. Firstly， the input sequence was encoded using a Transformer combined with Gaussian attention mechanism， and the Chinese definitions of entity types were obtained from Chinese Wikipedia. Bidirectional Gated Recurrent Unit （BiGRU） was used to encode the entity type information as prior knowledge， which was combined with the character representation using an attention mechanism. Secondly， Bidirectional Long Short-Term Memory （BiLSTM） network was used to encode the long-distance dependency relationship of the input sequence， and a glyph encoding table was used to obtain traditional Chinese characters’ Cangjie codes and simplified Chinese characters’ modern Wubi codes. Then， Convolutional Neural Network （CNN） was used to extract glyph feature representations， and the traditional and simplified glyph feature representations were combined with different weights， which were then combined with the character representation encoded by BiLSTM using a gating mechanism. Finally， decoding was performed using Conditional Random Field （CRF） to obtain a sequence of named entity annotations. Experiment results on the colloquial dataset Weibo， the small dataset Boson， and the large dataset PeopleDaily show that， compared with the baseline model MECT （Multi-metadata Embedding based Cross-Transformer）， the proposed model has the F1 value increased by 2.47， 1.20， and 0.98 percentage points， respectively， proving the effectiveness of the proposed model.

Abductive reasoning is an important task in Natural Language Inference （NLI）， which aims to infer reasonable process events （hypotheses） between the given initial observation event and final observation event. Earlier studies independently trained the inference model from each training sample； recently， mainstream studies have considered the semantic correlation between similar training samples and fitted the reasonableness of the hypotheses with the frequency of these hypotheses in the training set， so as to describe the reasonableness of the hypotheses in different environments more accurately. On this basis， while describing the reasonableness of the hypotheses， the difference and relativity constraints between reasonable hypotheses and unreasonable hypotheses were added， thereby achieving the purpose of two-way characterization of the reasonableness and unreasonableness of the hypotheses， and the overall relativity was modeled through many-to-many training. In addition， considering the difference of the word importance in the process of event expression， an attention module was constructed for different words in the samples. Finally， an abductive reasoning model based on attention balance list was formed. Experimental results show that compared with the L2R² （Learning to Rank for Reasoning） model， the proposed model has the accuracy and AUC improved by about 0.46 and 1.36 percentage points respectively on the mainstream abductive inference dataset Abductive Reasoning in narrative Text （ART） ， which prove the effectiveness of the proposed model.

Accurate shadow boundary detecting and texture-preserving are two critical difficulties in shadow removal. To solve these problems, a new shadow removal method based on gradient field was proposed. Firstly, shadow boundary was detected approximately. Then, the gradients in internal shadow region and shadow boundary were modified respectively to obtain the non-shadowed gradient field. Based on the gradient field, the information in shadow regions was recovered with Poisson equation. The experimental results with several images indicate that the method can remove shadow from images easily while preserving the textures in the shadow regions, and it is not sensitive to the accuracy of shadow boundary.