Reduce False Negative in Distant supervised learning using Dependency tree-LSTM to Construct a Knowledge Graph

Knowledge Graphs(KG) are a fundamental part of a wide variety of NLP applications improving the accuracy and explainability of knowledge. Constructing Knowledge Graphs from unstructured text assists the entity detection task and extracting semantic relations. A relation called a triple is the smallest part of the knowledge graph. A triple includes the subject of the relation; and the object of the relation, relation. However, extracting semantic relations has many difficulties.Supervised RE requires huge amounts of labelled data, which is labour-intensive and time-consuming. Some studies offered Distant supervision (DS). This method generates KG triplets based on the co-occurrence of entities in a sentence. In other words, any sentence containing an entity pair expresses relation. However, these methods struggle to obtain high-quality relations, suffering from False Negatives and False Positives. In our paper, we used a new Encoder-decoder model and multilayer perceptron to detect FN in two popular DS datasets (NYT10, GIDS); the possible FN is unlabeled and a model using Tree Bi-LSTM was trained to allocate new labels to improve previous research results. To summarise, our core contributions are: Construct an Encoder based on entity importance in the Distantly RE dataset. A model to Detect False negatives. Develop an algorithm to predict relation using a combination of dependency tree and tree Bi-LSTM.The result is a significant contribution and in comparison to models has a 25% improvement. False negative Detector filter FN samples from N with logits larger than threshold θ. The model discovered 6,324 FN samples from NYT10, which refer to 4,153 entity pairs; and 324 FN samples from GIDS, which refer to 285 entity pairs. The average precision is 92.0,For further research, we try to reduce FP in distant supervised learning.