Predicting Semantic Categories in Text Based on Knowledge Graph Combined with Machine Learning Techniques

Figures & data

Figure 1. Hadith categorization model

Figure 2. Hadith knowledge graph

Table 1. The weighted pivot terms in Hadith corpus

Hadith	Term #1	Term #2	…	Term #m	Category
H #1	0.23	0.53	…	0.43	C #1
H #2	…	0.53	…	0.43	C #1
H #3	0.23	…	…	0.43	C #2
	…	…	…	…	…
H #n	0.33	…	…	0.35	C #f

Figure 3. Relevant categories in the knowledge graph based on number of gaps

Figure 4. Relevant categories in the knowledge graph based on decay factor ($\alpha$)change.

Figure 5. TF-IDF and MI based pivot terms selection

Figure 6. Performance against number of categories predicted

Table 2. Comparison of categorization accuracy without pre-processing and knowledge-graph

	Precision	Recall	F-measure
RF	87.20%	96.10%	91.43%
DT	84.10%	92.20%	87.96%
NB	81.50%	94.20%	87.39%
AR	80.90%	95.90%	87.76%

Table 3. Classification accuracy with pre-processing phase

	Precision	Recall	F-measure
PP-RF	89.50%	97.90%	93.51%
PP-DT	86.50%	96.20%	91.09%
PP-NB	86.00%	95.30%	90.41%
PP-AR	84.50%	96.90%	90.28%

Table 4. Classification accuracy with pre-processing and knowledge-graph phases

	Precision	Recall	F-measure
PP-KG-RF	93.20%	99.10%	96.06%
PP-KG-DT	90.20%	97.90%	93.89%
PP-KG-NB	89.30%	97.00%	92.99%
PP-AR	84.50%	96.90%	90.28%