Concept Similarity Computation Research Articles

Research on Text Mining of Railway Safety Supervisors Performance Based on BiLSTM and CRF

In the era of big data with unstructured data, the potential value of unstructured data mining is an effective way to solve the industry problem. Based on a large number of data recorded from the actual text of the performance plan of railway security supervisors, this paper proposes a text similarity calculation method based on text mining technology to calculate whether the personnel performance plan matches the reality. First, the text Named Entity Recognition(NER) is realized by using the combination of Bi-directional Long Short Term Memory (BiLSTM) and the Conditional Random Fields (CRF). Then based on the concept similarity calculation of HowNet, we designed the method of calculating the similarity between the same named entity. After that, different weights are given according to the importance of named entity, and a method of computing similarity between named entities is designed.. Finally, according to the given threshold, to obtain whether planning and realistic matching, and the actual personnel working situation, provide the basis for personnel assessment for the management personnel.

A Semantic Information Content Based Method for Evaluating FCA Concept Similarity

Probability information content-based FCA concepts similarity computation method relies on the frequency of concepts in corpus, it takes only the occurrence probability as information content metric to compute FCA concept similarity, which leads to lower accuracy. This article introduces a semantic information content-based method for FCA concept similarity evaluation, in addition to the occurrence probability, it takes the superordinate and subordinate semantic relationship of concepts to measure information content, which makes the generic and specific degree of concepts more accurate. Then the semantic information content similarity can be calculated with the help of an ISA hierarchy which is derived from the domain ontology. The difference between this method and probability information content is that the evaluation of semantic information content is independent of corpus. Furthermore, semantic information content can be used for FCA concept similarity evaluation, and the weighted bipartite graph is also utilized to help improve the efficiency of the similarity evaluation. The experimental results show that this semantic information content based FCA concept similarity computation method improves the accuracy of probabilistic information content based method effectively without loss of time performance.

Analysis of gradient descent ontology iterative algorithm for geological setting

As a computational tool, ontology has wide applications in information retrieval and other disciplines. Ontology concept similarity calculation is an essential problem in these applications. In this paper, the focus is on the ontology setting such that the ontology function maps every vertex in graph into a real value. And the similarity between vertices is measured by the difference of their corresponding scores. We propose the gradient descent ontology algorithm for this setting, and the learning rates with general convex losses are determined.

Piecewise function approximation and vertex partitioning schemes for multi-dividing ontology algorithm in AUC criterion setting (I)

Ontology is a useful tool with wide applications in various fields and attracts widespread attention of scholars, and ontology concept similarity calculation is an essential problem in these application algorithms. An effective method to get similarity between vertices on ontology is based on a function, which maps ontology graph into a line and maps each vertex in graph into a real-value, and the similarity is measured by the difference of their corresponding scores. The area under the receiver operating characteristics curve (AUC) criterion multi-dividing method is suitable for ontology problem. In this paper, we present piecewise constant function approximation approach for AUC criterion multi-dividing ontology algorithm and focus on vertex partitioning schemes. Using the techniques of statistical learning theory, theoretical characteristics of the approximation algorithm are provided with partitioning schemes, and a splitting rule is designed for vertex partitioning.

Learning rate of gradient descent multi-dividing ontology algorithm

As acknowledge representation model, ontology has wide applications in information retrieval and other disciplines. Ontology concept similarity calculation is a key issue in these applications. One approach for ontology application is to learn an optimal ontology score function which maps each vertex in graph into a real-value. And the similarity between vertices is measured by the difference of their corresponding scores. The multi-dividing ontology algorithm is an ontology learning trick such that the model divides ontology vertices into k parts correspond to the k classes of rates. In this paper, we propose the gradient descent multi-dividing ontology algorithm based on iterative gradient computation and yield the learning rates with general convex losses by virtue of the suitable step size and regularisation parameter selection.

A Domain Information Retrieval Method based on Pension Insurance Ontology Concept Similarity

Matching key terms is the main method for most engines for information retrieval, but this method cannot meet the needs of people who want to retrieve useful domain information. This is because that it doesn’t consider the semantic relation of resources in the context. Ontology is an effective method that can solve the problem of domain knowledge sharing and semantic understanding. In this paper, an optimization strategy is presented for information retrieval domain, and an algorithm is also proposed for ontology concept similarity calculation. Experiments are carried out on the audit guidance system, and the results show that this method significantly improves the accuracy of information retrieval based on domain ontology

Research on Domain Ontology-Based Concept Similarity Calculation

Similarity computing of ontological concept has made rapid progress in the field of data mining, information processing and artificial intelligence and becoming one of the hot research field of information technology, particularly the idea of the semantic Web was proposed in 2000, the concept of semantic similarity has gotten more attention, while also facilitating its further development and application in information retrieval. Considering the deficiencies of existing concept similarity algorithm, this paper design the method to reduce the candidate set of domain concept, and put forward a similarity calculation model based on the concept name, instances, properties, and semantic structure of domain ontology. Integrated several main influencing factors, the experiments show the proposed algorithm can express the impact of various factors on the similarity in the calculation concept similarity of domain ontology. By comparing with the traditional similarity method and expertise experience value, the experiment result shows that the effectiveness and correctness of the concept similarity calculation model.

Stability Analysis of Learning Algorithms for Ontology Similarity Computation

Ontology, as a useful tool, is widely applied in lots of areas such as social science, computer science, and medical science. Ontology concept similarity calculation is the key part of the algorithms in these applications. A recent approach is to make use of similarity between vertices on ontology graphs. It is, instead of pairwise computations, based on a function that maps the vertex set of an ontology graph to real numbers. In order to obtain this, the ranking learning problem plays an important and essential role, especiallyk-partite ranking algorithm, which is suitable for solving some ontology problems. A ranking function is usually used to map the vertices of an ontology graph to numbers and assign ranks of the vertices through their scores. Through studying a training sample, such a function can be learned. It contains a subset of vertices of the ontology graph. A good ranking function means small ranking mistakes and good stability. For ranking algorithms, which are in a well-stable state, we study generalization bounds via some concepts of algorithmic stability. We also find that kernel-based ranking algorithms stated as regularization schemes in reproducing kernel Hilbert spaces satisfy stability conditions and have great generalization abilities.

Characteristics of Optimal Function for Ontology Similarity Measure via Multi-dividing

As a powerful tool, ontology has been widely applied in social science, medicine science and computer science. In computer networks, especially, ontology is used for search extension, thus boost the quality of information retrieval. Ontology concept similarity calculation is an essential problem in these applications. A new method to get similarity between vertices on ontology graph is by machine learning, and multi-dividing algorithm is suitable for ontology problem. It is usually get an ontology function which maps the vertices of ontology graph to real numbers. Such function is given by learning a training sample which contains a subset of vertices of ontology graph. In this paper, we study the properties of best ontology function for this method. Some results under different loss functions are given.

A Method of Concept Similarity Computation Based on Semantic Distance

Calculating the similarity between the concepts is the key in the process of the ontology mapping. Based on the analysis of the traditional method of calculating semantic similarity, a semantic distance-based concept similarity computation method is proposed. In order to keep the effectiveness and comprehensiveness of this method, the main factors which impact the semantic distance are considered fully, the factors of concept semantic contact ratio and depth differences between concepts nodes are also taken into account. By calculating test data, the experiment result shows that the method can calculate concepts similarity effectively.