The Knowledge graph (KG) uses the triples to describe the facts in the real world. It has been widely used in intelligent analysis and applications. However, possible noises and conflicts are inevitably introduced in the process of constructing. And the KG based tasks or applications assume that the knowledge in the KG is completely correct and inevitably bring about potential deviations. In this paper, we establish a knowledge graph triple trustworthiness measurement model that quantify their semantic correctness and the true degree of the facts expressed. The model is a crisscrossing neural network structure. It synthesizes the internal semantic information in the triples and the global inference information of the KG to achieve the trustworthiness measurement and fusion in the three levels of entity level, relationship level, and KG global level. We analyzed the validity of the model output confidence values, and conducted experiments in the real-world dataset FB15K (from Freebase) for the knowledge graph error detection task. The experimental results showed that compared with other models, our model achieved significant and consistent improvements.

Named entity relation extraction is an important subject in the field of information extraction. Although many English extractors have achieved reasonable performance, an effective system for Chinese relation extraction remains undeveloped due to the lack of Chinese annotation corpora and the specificity of Chinese linguistics. Here, we summarize three kinds of unique but common phenomena in Chinese linguistics. In this article, we investigate unsupervised linguistics-based Chinese open relation extraction (ORE), which can automatically discover arbitrary relations without any manually labeled datasets, and research the establishment of a large-scale corpus. By mapping the entity relations into dependency-trees and considering the unique Chinese linguistic characteristics, we propose a novel unsupervised Chinese ORE model based on Dependency Semantic Normal Forms (DSNFs). This model imposes no restrictions on the relative positions among entities and relationships and achieves a high yield by extracting relations mediated by verbs or nouns and processing the parallel clauses. Empirical results from our model demonstrate the effectiveness of this method, which obtains stable performance on four heterogeneous datasets and achieves better precision and recall in comparison with several Chinese ORE systems. Furthermore, a large-scale knowledge base of entity and relation, called COER, is established and published by applying our method to web text, which conquers the trouble of lack of Chinese corpora.

Named Entity Recognition (NER) is an important basis for the tasks in natural language processing such as relation extraction, entity linking and so on. The common method of existing Chinese NER systems is to use the character sequence as the input, and the intention is to avoid the word segmentation. However, the character sequence cannot express enough semantic information, so that the recognition accuracy of Chinese NER is not as good as western language such as English. To solve this issue, we propose a Chinese NER method based on Character-Word Mixed Embedding (CWME), and the method is in accord with the pipeline of Chinese natural language processing. Our experiments show that incorporating CWME can effectively improve the performance for the Chinese corpus with state-of-the-art neural architectures widely used in NER, and the proposed method yields nearly 9% absolute improvement over previously results.

Exponential growth of information generated by social networks requires efficient and scalable recommendation techniques to produce useful results. Traditional methods have become unqualified because they consider only ratings instead of rankings in an item list, and they ignore social contextual information, which is valuable for predicting users’ preference. It is significant and challenging to fuse social contextual information into learning to recommendation methods. In this study, the authors first extend user latent features by exploiting users’ social relationship such as friendship or trust relations, and extend item latent features with concurrent items. Then they integrate both users’ and items’ social contextual information into a pairwise learning to recommendation model (named as UIContextRank) to enhance ranking accuracy and recommendation quality. Furthermore, they extend UIContextRank in a distributed environment to improve efficiency and scalability. The authors conduct experiments on both bidirectional and unidirectional social network datasets. The results show that their method significantly outperforms other approaches.