# StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks
## 4 Experiments
### Effectiveness Experiments
#### 4.3.1 Entity Resolution Task  
Entity resolution refers to finding the same entity pairs with different descriptions, which can achieve the purpose of data cleaning. For example, in YAGO, _Presidency of [PERSON]_ and _[PERSON]_ denote the same person but have different descriptions. Entity resolution aims to find _Presidency of [PERSON]_ and _[PERSON]_ so as to clean the data by deduplicating this entry. We extracted experiment data from YAGO for an entity resolution task. To be specific, we leverage yago-fact tuples and entity types to obtain 2687 pairs of objects satisfying the semantic meaning revealed by the meta path \(p_{1}\) in Figure 4. These extracted data, in total, included 4521 different people. Then, we manually labeled the data with the help of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Template of Triples** & **\# Triples** & **\# Entities/Types** \\ \hline yagoFacts & \textless{entity relationship entity}\textgreater{} & 4,484,914 & 1,369,931 \\ yagoSimpleTypes & \textless{entity rdf.type wordnet type} & 5,437,179 & 6543 \\ yagoTaxonomy & \textless{wordnet type} & 69,826 & 388,606 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Yago-Core dataset statistics. Wikipedia and obtained 46 pairs of entities which denoted the same object. The remaining 2641 pairs of entities were regarded as negative samples. For the entity resolution task, we employed the meta structure \(S_{1}\) and meta paths \(p_{1}\), \(p_{2}\) in Figure 4 to compute the similarity between objects. For each object, we regarded it as a source node to find the duplicated target node. Based on the similarity score, we ranked the other entities except for the source node. The higher the similarity score, the more likely it was to belong to the same object. Here, the same object was the pairs of objects that referred to the same entity. For example, _Presidency of [PERSON]_ and _[PERSON]_ refer to the same entity. We drew the ROC curve through adapting the threshold of the similarity score and computed the AUC value. Table 3 demonstrates the results of different similarity measures in the entity resolution task. \(s=\beta s_{p_{1}}+(1-\beta)s_{p_{2}}\) is the similarity formula of a linear combination of the meta path; \(s_{p_{1}}\) and \(s_{p_{2}}\) denote the similarity score based on the meta path \(p_{1}\) and \(p_{2}\), respectively. From Table 3, we can observe that (1) the similarity measures based on the meta structure have a better performance compared with those based on the meta path, which reveals that the meta structure can capture more complex semantic meaning than the meta path. Here, \(S_{1}\) means two people are not only subordinate to the same organization but also have a marital relationship with the same people. However, both \(p_{1}\) and \(p_{2}\) cannot express the complicated semantics. (2) StructSim performs better than StructCount, SCSE, BSCSE and MGP, which indicates that StructSim considers more rich semantics by incorporating the out-neighbor and in-neighbor information when expanding the subgraph, rather than just considering the out-neighbor information that most similarity measures do. (3) The linear combination of the meta path outperforms the approaches based on the meta path but is inferior to meta-structure-based approaches. This demonstrates that the linear combination can capture more comprehensive information than a single meta path; nevertheless, it cannot capture more complicated semantics than the meta structure. (4) \(p_{1}\) has some superiority over \(p_{2}\) in the entity resolution task, which may be due to the fact that two objects with a martial relationship with the same person have higher probability to be the same people than two objects being subordinate to the same organization. Figure 4: Meta path and meta structure on entity resolution and clustering tasks.