In other words, the history of the process "blinds" the process in favor of interpreting its place in the graph from the scholarly angle.17

![0_image_0.png](0_image_0.png)

The trivial intersection method of identifying the degree of similarity between two graph structures can be extended. Other algorithms, such as those based on a spreading activation within a semantic graph [15, 14, 16, 34] can be used as a more fuzzy and probabilistic means of determining the relative
"semantic distance" between two graphs [17]. Spreading activation methods are more analogous to the connectionist paradigm of cognitive science than the symbolic methods of artificial intelligence research [36]. The purpose of a spreading activation algorithm is to determine which resources in a semantic graph are most related to some other set of resources. In general, a spreading activation algorithm diffuses an energy distribution over a graph starting from a set of resources and proceeding until a predetermined number of steps have been taken or the energy decays to some  ≈ 0.18 Those resources that received the most energy flow during the spreading activation process are considered the most similar to the set of source resources. With respect to the particular example at hand, the energy diffusion would start at the resources in H and the results would be compared with resources of Tx and Ty.

If the set of resource in Tx received more energy than those in Ty, then the dilated triple Tx is considered more representative of the context of H.

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