For a single node/concept $u$ in the co-occurrence graph $G$, the Average Precision (AP) is calculated in two steps.

1. Constructed a list $labels = \{l_1, l_2, ..., l_n\}$, where $l_i = 1$ if $i$ is a child of $u$ and 0 otherwise, and calculated a list $distances = −\{d_1, d_2, ..., d_n\}$, where $d_i$ is the distance between $u$ and $i$.
2. The AP algorithm considers every possible distance threshold to classify the nodes as children of $u$ in the graph $G$ , and for each threshold $j$ returns the recall $R_j$ and the precision $P_j$ . Sorted $R_j$ and calculated $AP = \sum_{i=1}^n (R_i − R_{i−1})P_i$ The Mean Average Precision (mAP) is $mAP= \frac{1}{|G|}\sum_{u∈G} AP_u$

A higher mAP means a more efficient embedding when reconstructing the prerequisite relationships of the annotated graph.