Abstract
In this study, a method about building Deep Knowledge Graph for the Plant Insect Pest and Disease, namely DKG-PIPD, was proposed. Specifically, the semi-automatic extraction of semi-structured and unstructured knowledge was carried out on the basis of domain ontology, and the knowledge graph was stored in the third-party knowledge database according to the corpus characteristic of the plant insect pest and disease, to realize the visual display of entity interactive relationship and knowledge inference. Furthermore, DKG-PIPD performed joint extraction about the entity and the relationship in unstructured knowledge in a corpus tagging method that is suitable for domain data. In this way, the entity and the relationship were annotated synchronically, and the triplet can be obtained directly through label matching and label mapping, which not only effectively improved the annotation efficiency, but also solved the problem of one-versus-many overlapping relation extraction. In addition, DKG-PIPD used a novel end-to-end model to train and predict on the crawled dataset. The experimental contrast results with other classical benchmark methods demonstrated the effectiveness of the proposed method. Moreover, the related work in this paper first introduced the general architecture required for the building of knowledge graph, and then summarized its key points, that is, named entity recognition, entity relationship extraction and knowledge inference using deep learning are emphatically introduced. Finally, the improvement direction of this paper was also introduced in the discussion section.
Highlights
In 2012, Google introduced the concept about knowledge graph, which provides a new way for knowledge management
To further characterize more comprehensive sentence-level semantic features and alleviate the problems of interleaved entity relations and long distances between entities, this study introduces the bidirectional encoder representations from transformers (BERT) pre-trained language model, i.e., uses the BERT-bi-directional long-short term memory (BiLSTM)&conditional random field (CRF) end-to-end model for training and predicting, which extracts word-level features and enables deeper mining and learning of sentence-level semantic features
In view of the problems in the field of plant insect pests and diseases, such as the cross-correlation of entity relations, poor aggregation ability of multi-source heterogeneous data, and difficulty in knowledge sharing, this study uses the advantage of knowledge graph to describe the complex relationship between entities in a structured form, and proposes a novel method about building Deep Knowledge Graph for the Plant Insect Pest and Disease, namely DKG-PIPD
Summary
In 2012, Google introduced the concept about knowledge graph, which provides a new way for knowledge management. Knowledge graph is essentially a structured semantic knowledge base that describes concepts, entities and their relationships in the objective world in a structured form, generally in the form of a triplet of (entity, relationship, entity) or (entity, attribute, attribute value). Knowledge graph can structure the heterogeneous knowledge of the field and it is good at describing the interaction between entities, which makes the field knowledge explicitly precipitated and associated, and well solves the problem of scattered, complex and siloed data in the field. Vertical-domain knowledge graph [6]–[8]. The former focus more on breadth while the latter focus on depth, but due to the lack of annotated training corpus and excessive reliance on experts, the scale is generally small and the construction cost is expensive
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