# An Open Relation Extraction Method for Domain Text Based on Hybrid Supervised Learning
## 3 Problem Background  
Traditional relation extraction is based on specific relation, which leads to time-consuming and labor-intensive extraction tasks and high labor costs. Meanwhile, the extraction method only applies to the current field and is difficult to transfer to other fields. Open relation extraction is a new research direction in information extraction. This extraction method combines semantic and morphological features to automatically extract relation in unqualified types of open web texts. Open relation extraction does not require a defined relation type in advance, reducing the cost of manual annotation. The extraction system based on this design is more portable and suitable for open web corpora with complex relation types. The construction of domain knowledge graphs based on open texts on the web proposed in this paper includes the construction of a core entity lexicon, the automatic annotation of corpus entities, the manual annotation of open relation, the training of relation extraction models, and open relation extraction for a given core entity case. One of the core tasks is relation extraction for open texts. An example is the relation extraction of equipment components in an industrial scenario. The inputs and outputs of open relation extraction are shown in Table 1 for a known defined core entity lexicon. This paper employs the TF-IDF [31] and TextRank [32] algorithms as tools for data pre-processing in order to compute the frequency of keywords in texts publicly available on the web, as demonstrated in Table 2.  
As the TF-IDF algorithm solely measures the significance of words based on their \"word frequency\", words remain unconnected and fail to convey any sequence information. For instance, in the case of industrial data, the word \"feature\" may appear frequently in the text but is not a valid entity. As evidenced in Table 2, incorporating the TextRank algorithm as an auxiliary tool furnishes co-occurrence information between words, leading to a marked improvement in the statistical results. Although this approach can replace fundamental manual statistics, it still requires expert review to eliminate non-valid entity words and introduce a limited number of missing entities. The ultimate outcome is a superior-quality core entity candidate word library (partially) as presented in Table 3, which serves as a reference for primary entity matching labeling. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Input Text** & **Output Result** \\ \hline \multirow{8}{*}{\(1\)} & & \(\{\) \\  & & \"text\": \"The vertical graphitization furnace \\  & \(\{\) & includes a support plate, a furnace body and a furnace cover above it.\", \\  & & includes a support plate, a furnace body and a furnace cover above it.\", \\  & & \"sub\": [\"Vertical Graphitization Furnace\", \\  & \"Support plate\", \\  & \"Furnace body\", \"Furnace lid\"] & \"Include\", \\  & \(\}\) & \"Furnace lid\"], [\"Furnace body\", \\  & & \"Above\", \"Furnace lid\"] \\ \hline \multirow{8}{*}{\(2\)} & & \(\{\) \\  & \(\{\) & \"text\": \"The raw material silo, drying furnace and \\  & & carbonization furnace are connected in sequence \\  & & by transportation equipment.\", \\  & & \"spo\": [[\"Raw material silo\", \"Connected\", \\  & \"sub\": [\"Raw material silo\", \\  & \"Drying furnace\", \"Carbonization furnace\", \"Transport equipment\"], [\"Drying furnace\", \\  & \(\}\) & \"Connected\", \\  & & \(\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Open relation extraction sample. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & TFIDF & TextRank & TFIDF-TextRank \\ \hline
**Accuracy** & 75\% & 67\% & 81\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Core entity extraction results.