## 4 Experiments
### Path Coherence Evaluation  
We adopted the approach proposed in paper [7], evaluating the path coherence of the structured life narratives by computing the distance between the word vectors of the event node pairs within one topic. The distance between two events can be computed by \(dis\big{(}E_{i},E_{j}\big{)}=1-\bigg{(}\frac{E_{i}:E_{j}}{|E_{i}|^{2}+|E_{j}| ^{2}-E_{i}:E_{j}}\bigg{)}\). This metric also indirectly measures the validity of an edge between two event nodes in a topic path. The coherence of a topic path is represented by the average of the distances of all event node pairs' summation:  
\[Tdis(E_{1},E_{2},\cdots,E_{k})=\frac{1}{k-1}\sum_{i=1}^{k-1}dis(E_{i},E_{i+1}) \tag{14}\]  
The goal is to find out which structure presents the life stories most coherently and logically. We present the results of the evaluation metrics in Table 2. Our LSH-GEFR achieves the best performance as it adopts a bilayer graph to obtain the potential relationships among events. This makes the associated events more similar in terms of feature representation to the extent that the relevant event nodes are also closer together.  
Path coherence here refers to the rationality of the ordering of events under the same topic. In the LSH-GEFR algorithm, we consider three aspects to rank events temporally or logically: the time of the event, the position of the event trigger word in the life story, and the correlation between the events. Suppose the event node \(E_{i}\) is known, and the event with the highest integrated correlation is found from the event pool as the previous or next node of the node. Combining the time, trigger word position, and the correlation between events, the integrated correlation between events is calculated as follows: \(Rel\left(E_{i},E_{j}\right)=tm\mathit{Dif}\left(tm_{i},tm_{j}\right)\times pos \mathit{Dif}\left(pos_{i},pos_{j}\right)\times\varphi_{e}(e_{i},e_{j})\). \(tm\mathit{Dif}\left(tm_{i},tm_{j}\right)\) denotes the distance of the timestamp between two events and \(pos\mathit{Dif}\left(pos_{i},pos_{j}\right)\) denotes the distance between the location of the event trigger words of the two events. The algorithm integrates three aspects of two events: temporal distance, trigger word distance, and event relevance, which results in a more reasonable ordering of events under the same topic and logical path coherence.  
The LDA + TO algorithm employs simple chronological sorting based solely on temporal cues. While the Story Forest and EventNET algorithms integrate both temporal cues and event relevance into event sorting, their event features rely solely on event text vectors, disregarding the influence of event elements. In contrast, LSH-GEFR's event features are built on BERT encoding, encompassing relevant features from event elements and event networks. This incorporation strengthens the correlation between similar events, fostering high coherence in paths. Additionally, LSH-GEFR also draws inspiration from EventKG by constructing a timeline of big national events. It transforms blurry time elements into clear event timespans, such as converting \"post-Reform and Opening Up\" in a life narrative to \"after 1979\", enhancing the precision of event time representations. Coupled with the optimization of event features within LSH-GEFR, it achieves superior path coherence compared to EventKG.