Prototype Network for Text Entity Relationship Recognition in Metallurgical Field Based on Integrated Multi-class Loss Functions

It is of great significance to recognize the metallurgical entity relations in order to construct the Knowledge graph of Metallurgical Literature and to further understand the metallurgical literature. However, there are few researches on the textual entity relations in metallurgical fields either few marked Corpora. The syntactic structure of the same entity relationship category is relatively simple and has strong domain characteristics. The traditional entity relationship model can not identify the domain entity relationship well. Meanwhile the syntactic structure of the same entity relations class is relatively simple, and the syntactic structure is relatively simple in the recognition of entity relations in metallurgy field. Furthermore, the entities with similar syntactic structure often have the same entity relations and the different words in the sentence have different contribution to the entity relations. In order to solve the mentioned problems, this paper will combine the algorithm that can highlight the syntactic structure in sentences and improve the accuracy of the model with the Algorithm that can highlight the contribution of words in sentences and the loss function level integration is carried out in the framework of small sample prototype network, so as to maximize the advantages of each algorithm and improve the accuracy –firstly, in the coding layer of the prototype network, we use the CNN algorithm which can highlight the important words in the sentences and the TreeLSTM algorithm which can parse the sentences in the text so that the syntactic relations between the words in the sentences can be acted on in the relation recognition, the sentences are coded together by two algorithms, then, the EUCLIDEAN distance loss is calculated by using this high quality coding and the prototype coding, finally, the traditional entity relation recognition model with Attention Mechanism is integrated into the loss function, further highlighting the decisive role of important words in text sentences in relation recognition and improving the generalization of the model. The results showed that compared with the traditional methods such as CNN, RNN, PCNN and Bi-LSTM, the proposed method in this paper has better performance in the case of small sample data set.

Comparison of Key Entities Within Bibliographic Conceptual Models and Implementations: Definitions, Evolution, and Relationships

With the introduction of FRBR (Functional Requirements of a Bibliographic Record) in 1998, IFLA (the International Federation of Library Associations and Institutes) introduced a new conceptual entity relationship model. FRBR was soon followed by FRAD (Functional Requirements of Authority Data) and FRSAD (Functional Requirements of Subject Authority Data). With LRM (IFLA Library Reference Model) and two descriptive standards, the RDA Toolkit and BIBFRAME to follow, it helps catalogers to have a greater understanding of the entity relationship models they use for bibliographic description. The authors compare the models and descriptive standards. Differences among the entities, their definitions, and properties are examined and analyzed.

Using Ontology and Rule-Based Reasoning for Conceptual Data Models Synonyms Detection

Conceptual data models can change during the information system development and teamwork phases, which require constantly monitoring with synonyms detection. This study elaborates on an approach for detecting synonyms in an entity-relationship model based on mapping with ontological elements. The use of a specific data model validator (DMV) tool enables formalization of the ontology and ER models, as well as their integration with the set of reasoning rules. The reasoning rules enable mapping between formalized elements of the ontology and ER model, and the extraction of synonyms. Formalized elements and reasoning rules are processed within Prolog for the extraction of synonyms. An empirical study conducted by using university student exams demonstrates usability of the proposed approach. The results show effectiveness in extraction of synonyms in all types of conceptual data model elements.