Suicidal ideation (SI), as a psychiatric emergency, requires immediate assistance and intervention. Most people with SI do not actively seek help from mental health professionals, which may result in irreversible consequences. Research has shown that individuals experiencing SI increasingly express their thoughts and emotions on social media platforms, making the latter a viable venue for suicidal ideation detection (SID). This paper proposes, develops, and evaluates a knowledge-enhanced transformer-based approach (KETCH) to SID from social media content. KETCH comprises several key novel design artifacts, including a social media-oriented SI lexicon, a model-level method for integrating domain knowledge (i.e., lexicon) into a state-of-theart transformer, and aligned dynamic embedding and lexicon-based enhancement that integrate domain relevance and contextual importance of terms to effective SID. We evaluate KETCH's performance with social media data in two different languages collected from distinct platforms, and further examine its generalizability to user-level models for suicide risk prediction and depression detection. The results demonstrate the superior effectiveness, robustness, and generalizability of KETCH via a series of empirical evaluation and a field study. Our research makes multifold research contributions and opens up practical opportunities for timely detection and proactive intervention of SI, which can have far-reaching impacts on public health, the economy, and society.