Ontologies are widely considered to be the backbone of the Semantic Web and are being recognized in a multiplicity of research fields and appli- cation areas. Resolving semantic mismatches, logical inconsistencies and conceptual confliicts between independently developed ontologies is likely to become an important major in ontology engineering. We learned that the above-mentioned problems should be solved by human collaboration, and cannot be achieved by automated methods except in simple cases. The main goal of this research is to investigate an effective methodol- ogy for collaborative ontology. It used consensus techniques that support a solution to conflicts among different viewpoints of participants in the collaborative ontology (CoO) building process. A machine plays an in- termediary role as a leader of the collaborative group that identifies the conflicts among different versions of the ontology. A reconciled version that best reflects the conflicting versions in a compromise is generated automatically. The criteria for a collaborative ontology have been pro- posed, and are aimed at guiding and evaluating the CoO process. In every cycle of the iterative process, the ontology is revised and evolved by reaching consensus among the participants' viewpoints.