# Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter
## 7 Conclusions and Future Work  
The generation of accurate patterns is a key factor of pattern-based knowledge enrichment. In this paper, a parse tree pattern and semantic filter to remove irrelevant pattern candidates were proposed. The benefit of using parse tree representation for patterns is that long distance dependencies of words are expressed well by a parse tree. Thus, parse tree patterns contain many words that are not located between two entity words. In addition, the benefit of the semantic filter is that it finds irrelevant patterns more accurately than does a frequency-based filter because it reflects the meaning of relations directly.  
The benefits of our system were empirically verified through experiments using the DBpedia ontology and Wikipedia corpus. The proposed system achieved 68% accuracy for pattern generation, which is 16% higher than that of lexical patterns. In addition, the knowledge extracted newly by parse tree patterns showed 60.1% accuracy, which is 27.7% higher than the accuracy of that extracted by lexical patterns and statistical scoring. Although in comparison with previous neural network based methods the proposed method could not achieve state-of-the-art performance, it showed excellent performance considering the simplicity of the model. Especially, it proves that our proposed approach is suitable for the knowledge enrichment task with robustness. These results imply that the proposed knowledge enrichment method populates new knowledge effectively.  
As our future work, we will figure out a more suitable similarity metric between a pattern and a relation. We have shown through several experiments that WordNet and word embedding are appropriate for this task without further huge efforts. Nevertheless, there is still some room for performance improvement. Thus, we will explore a new semantic similarity to capture well the relatedness between a relation and a pattern in the future. Another weakness of the proposed method is that it cannot handle the unseen relations. It is critical to discover unseen relations in order to make a knowledge base as perfect as possible. Recently, translation-based knowledge base embeddings showed some potential for finding absent relations ([PERSON] et al., 2019; [PERSON] et al., 2020). Therefore, in the future, we will investigate a way to discover absent relations and enrich a knowledge base by applying them to the knowledge base.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **100** & **200** & **300** & **Mean** \\ \hline PCNN+ATT ([PERSON] et al. (2019)) & 76.2\% & 73.1\% & 67.4\% & 72.2\% \\ PCNN+nc+cond\_opt ([PERSON] et al. (2020)) & 85.0\% & 82.0\% & 77.0\% & 81.3\% \\ PCNN-ATT+soft-label ([PERSON] et al. (2019)) & 87.0\% & 84.5\% & 77.0\% & 82.8\% \\ PCNN+ATT\_RA+BAG\_ATT ([PERSON] et al. (2020)) & 91.8\% & 84.0\% & 78.7\% & 84.8\% \\ Proposed Method & 82.3\% & 78.5\% & 76.9\% & 79.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison at Precision@\(K\).  
**Author Contributions:** Conceptualization, H.-G.Y.; Data curation, H.-G.Y.; Funding acquisition, S.P.; Methodology, H.-G.Y.; Project administration, S.P. and S.-B.P.; Supervision, S.-B.P.; Validation, S.P. and S.-B.P.; Visualization, H.-G.Y.; Writing--original draft, H.-G.Y.; Writing--review & editing, S.P. and S.-B.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2016-0-0145, Smart Summary Report Generation from Big Data Related to a Topic).  
**Conflicts of Interest:** The authors declare no conflict of interest.