Network maps of technology fields extracted from patent databases are useful to aid in technology forecasting and road mapping. Constructing such a network requires a measure of the relatedness between pairs of technology fields. Despite the existence of various relatedness measures in the literature, it is unclear how to consistently assess and compare them, and which ones to select for constructing technology network maps. This ambiguity has limited the use of technology network maps for technology forecasting and roadmap analyses. To address this challenge, here we propose a strategy to evaluate alternative relatedness measures and identify the superior ones by comparing the structure properties of resulting technology networks. Using United States patent data, we execute the strategy through a comparative analysis of twelve relatedness measures, which quantify inter-field knowledge input similarity, field-crossing diversification likelihood or frequency of innovation agents, and co-occurrences of technology classes in the same patents. Our comparative analyses suggest two superior relatedness measures, normalized co-reference and inventor diversification likelihood, for constructing technology network maps.