Open Information Extraction (OpenIE) aims to extract structured information in the form of triples from unstructured text, serving as a foundation for various downstream NLP tasks. Despite the success of neural OpenIE models, their dependence on large-scale annotated datasets poses a challenge, particularly in low-resource settings. In this paper, we introduce a novel approach to address the low-resource OpenIE task through two key innovations: (1) we improve the quality of training data by curating small-scale, high-quality datasets annotated by a large language model (GPT-3.5), leveraging both OpenIE principles and few-shot examples to form LSOIE-g principles and LSOIE-g examples; (2) we propose CycleOIE, a training framework that maximizes data efficiency through a cycle-consistency mechanism, enabling the model to learn effectively from minimal data. Experimental results show that CycleOIE, when trained on only 2k+ instances, achieves comparable results to models trained on over 90k instances. Our contributions are further validated through extensive experiments, demonstrating the superior performance of CycleOIE and our curated LSOIE-g datasets in low-resource OpenIE as well as revealing the internal mechanisms of CycleOIE.

In 5G networks with SDN architecture, the traffic explosion and the rising of diverse service requirements lead to many challenges for 5G core networks on flexibility and scalability. In order to achieve high-speed forwarding of traffic and diversified transmission requirements in the 5G era, combined with SDN and Segment Routing, we propose a scalable flow forwarding scheme called Segment Routing based on Path Aggregation and Rule sharing (PARS-SR) to solve SDN switch flow table resource shortage problem. Traditional OpenFlow-based or MPLS-based flow forwarding scheme may lead to performance degradation due to flow-table overflowed or heavy MPLS label load incurred. PARS-SR exploits SDN, Segment Routing and intelligent path encoding algorithm to achieve a trade-off between flow table resource and MPLS label load. The proposed PARS-SR can learn the flow path information online to implement path aggregation and rule sharing by aggregating a large number of flows into a small number of flow entries based on the coincidence degree of the flow path. To find the optimal flow path aggregation scheme, we present an intelligent encoding algorithm to maximize the overall cost saving. The simulation results show that PARS-SR can effectively reduce both the number of flow entries and the MPLS label load of the packet.