Knowledge of the business relationships between Autonomous Systems (ASes) is the basis for studying many aspects of the Internet. Despite the significant progress achieved by the latest inference algorithms, their inference results still suffer from errors on many special or critical links, thus hindering many relationship-related applications. We take an in-depth analysis on the challenges inherent in inferring AS relationships, including complex routing policies, limited and biased vantage point (VP) coverage, as well as a lack of accurate validation data. To address these challenges, we introduce HELA, a framework for inferring AS relationships with a hybrid of empirical and machine learning algorithms. HELA incorporates an array of grouping, voting, and machine learning algorithms and allows flexible substitution of each. We systematically evaluate various combinations of them to determine the most effective one for HELA. Furthermore, we describe the collection of varied validation datasets, including BGP community and RPSL records from Internet Routing Registries (IRRs), as well as OneStep community. Our up-to-date dataset corrects errors in previous published validation sets, contains 95% more labelled links, and exhibits a closer alignment to actual link distribution. Using routing data and validation datasets composed for each month during$2021\sim 2023$, we access HELA’s superiority in both inference accuracy and stability compared to the state-of-the-art inference algorithms, i.e., AS-Rank, ProbLink, and HELA’s predecessor TopoScope. In particular, HELA achieves up to$2.9\times $reduction on error rates across overall datasets, up to$2.6\times $reduction with a$4.5\times $decrease on standard deviation on incomplete and biased datasets, and up to$1.7\times $reduction on various sources of validation datasets.