The successful adoption by clinicians of evidence-based clinical practice guidelines (CPGs) contained in clinical information systems requires efficient translation of free-text guidelines into computable formats. Natural language processing (NLP) has the potential to improve the efficiency of such translation. However, it is laborious to develop NLP to structure free-text CPGs using existing formal knowledge representations (KR). In response to this challenge, this vision paper discusses the value and feasibility of supporting symbiosis in text-based knowledge acquisition (KA) and KR. We compare two ontologies: (1) an ontology manually created by domain experts for CPG eligibility criteria and (2) an upper-level ontology derived from a semantic pattern-based approach for automatic KA from CPG eligibility criteria text. Then we discuss the strengths and limitations of interweaving KA and NLP for KR purposes and important considerations for achieving the symbiosis of KR and NLP for structuring CPGs to achieve evidence-based clinical practice.