Information Extraction (IE) in Natural Language Processing (NLP) aims to extract structured information from unstructured text to assist a computer in understanding natural language. Machine learning-based IE methods bring more intelligence and possibilities but require an extensive and accurate labeled corpus. In the materials science domain, giving reliable labels is a laborious task that requires the efforts of many professionals. To reduce manual intervention and automatically generate materials corpus during IE, in this work, we propose a semi-supervised IE framework for materials via automatically generated corpus. Taking the superalloy data extraction in our previous work as an example, the proposed framework using Snorkel automatically labels the corpus containing property values. Then Ordered Neurons-Long Short-Term Memory (ON-LSTM) network is adopted to train an information extraction model on the generated corpus. The experimental results show that the F1-score of γ’ solvus temperature, density and solidus temperature of superalloys are 83.90%, 94.02%, 89.27%, respectively. Furthermore, we conduct similar experiments on other materials, the experimental results show that the proposed framework is universal in the field of materials.

A standardized representation and sharing of crop disease and pest data is crucial for enhancing crop yields, especially in China, which features vast cultivation areas and complex agricultural ecosystems. A knowledge graph for crop diseases and pests, acting as a repository of entities and relationships, is crucial conceptually for achieving unified data management. However, there is currently a lack of knowledge graphs specifically designed for this field. In this paper, we propose CropDP-KG, a knowledge graph for crop diseases and pests in China, which leverages natural language processing techniques to analyze data from the Chinese crop diseases and pests image-text database. CropDP-KG covers relevant information on crop diseases and pests in China, featuring 8 primary entities such as diseases, symptoms, and crops, and is organized into 7 relationships such as primary occurrence locations, affected parts and suitable temperature. In total, it includes 13,840 entities and 21,961 relationships. In the case studies presented in this research, we also show a versatile application of CropDP, namely a knowledge service system, and have released its codebase under an open-source license. The content of this paper provides a guide for users to build their own knowledge graphs, aiming to help them effectively reuse and extend the knowledge graphs they create.

As a promising indoor localization technology, WiFi fingerprint-based localization encounters many issues that need to be addressed urgently, such as high-overhead fingerprint map construction, device heterogeneity among either mobile devices or access points (APs), etc. In this article, we present CBWF: a lightweight circular boundary -based WiFi fingerprinting localization system that is able to provide low-overhead, device calibration-free accurate indoor localization. CBWF achieves this by dividing a localization area into multiple subregions, and then leveraging the relation between the received signal strength (RSS) vectors from two different APs as fingerprints for localization. The key idea behind CBWF is that a superior division mechanism is attained to divide the localization area. Specifically, we propose the circle boundary mechanism to better approximate the real boundary of subregions, compared with the widely used linear boundary mechanism, and then sufficiently exploit the theoretical characteristics behind this novel mechanism. Extensive simulation and real-world experiments show that our lightweight system outperforms state-of-the-art approaches. Specifically, in a 40 m $\times 17$ m real scenario with only 20 reference points (RPs) and 11 APs, CBWF achieves an average localization accuracy of 2.95 and 4.15 m for two different mobile devices, respectively. Our codes are available at: https://github.com/dadadaray/circular-boundary .