Relation extraction is an important information extraction task that must be solved in order to transform data into Knowledge Graph (KG), as semantic relations between entities form KG edges of the graph. Although much effort has been devoted to solve this task during the last three decades, but the results achieved are not as good yet. For instance, winner at Text Analysis Conference's (TAC) Knowledge Base Population (KBP) 2015 slot filling task, the Stanford's system, achieves F1 score of 60.5 on standard Relation Extraction (RE) dataset (Zhang et al., in: Position-aware attention and supervised data improve slot_lling. In: EMNLP 2017-Conference on Empirical Methods in Natural Language Processing, Proceedings, (2017). https://doi.org/10.18653/v1/d17-1004). The RE task therefore needs better solutions. This paper presents our system, CustRE, for better identification and classification of family relations from English text. CustRE is a rule based system, that uses regular expressions for pattern matching to extract family relations explicitly mentioned in text, and uses co-reference and propagation rules to extract family relations implicitly implied in the text. The proposed system, its implementation and the results obtained are presented in this paper. The results show that our approach makes a great improvement over existing methods by achieving F1 scores of 79.7 and 76.6 on TACRED family relations and CustFRE datasets respectively, which are 6.3 and 18.5 points higher than LUKE, the best score reporter on TACRED.
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