Concept Based Dynamic Ontology Creation for Job Recommendation System

Kethavarapu, Uma Pavan Kumar; Saraswathi, S.

doi:10.1016/j.procs.2016.05.282

Cited by 19 publications

(8 citation statements)

References 8 publications

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“…The comparisons between the LDA model algorithm and the LSI model algorithm and the TF-IDF model algorithm are shown in Figure 6. As determined by formulas (9)- (11), the comparisons between the edit distance and the Jaccard coefficient algorithm and the Euclidean metric algorithm are shown in Figure 5. The comparisons between the LDA model algorithm and the LSI model algorithm and the TF-IDF model algorithm are shown in Figure 6.…”

Section: Water Affair Information Recommendation Results Based On Thementioning

confidence: 99%

“…In addition, Figure 6 show that the recall rate R of the LDA model algorithm is lower than that of the LSI model algorithm, which occurs because the LSI model more fully considers the synonym problem, so the value of the recall rate R is increased. However, because the LDA model contains three layers of As determined by formulas (9)- (11), the comparisons between the edit distance and the Jaccard coefficient algorithm and the Euclidean metric algorithm are shown in Figure 5. The comparisons between the LDA model algorithm and the LSI model algorithm and the TF-IDF model algorithm are shown in Figure 6.…”

Section: Water Affair Information Recommendation Results Based On Thementioning

confidence: 99%

“…To solve the ambiguity problem, Run et al [10] proposed a financial news recommendation algorithm to help users find interesting articles. Uma et al [11] built a job recommendation system for job seekers by collecting work portal data. Castells et al [12] presented a comprehensive personalized retrieval framework where the advantages of ontologies are exploited in different parts of the retrieval cycle.…”

Section: Introductionmentioning

confidence: 99%

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Construction and Recommendation of a Water Affair Knowledge Graph

Yan

2018

Sustainability

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Water affair data mainly consists of structured data and unstructured data, and the storage methods of data are diverse and heterogeneous. To meet the needs of water affair information integration, a method of constructing a knowledge graph using a combination of water affair structured and unstructured data is proposed. To meet the needs of a water information search, an information recommendation system for constructing a water affair knowledge graph is proposed. In this paper, the edit distance algorithm and latent Dirichlet allocation (LDA) algorithm are used to construct a water affair structured data and unstructured data combination knowledge graph, and this graph is validated based on the semantic distance algorithm. Finally, this paper uses the recall rate, accuracy rate, and F comprehensive results to compare the algorithms. The evaluation results of the edit distance algorithm and the LDA algorithm exceed 90%, which is greater than the comparison algorithm, thus confirming the validity and accuracy of the construction of a water affair knowledge graph. Furthermore, a set of water affair verification sets is used to verify the recommendation method, which proves the effectiveness of the recommended method.

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Section: Water Affair Information Recommendation Results Based On Thementioning

confidence: 99%

Section: Water Affair Information Recommendation Results Based On Thementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Construction and Recommendation of a Water Affair Knowledge Graph

Yan

2018

Sustainability

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“…Early collaborative filtering algorithms can be divided into two categories: user-based and item-based [8,9]. At that time, PHOAKS, Typestry, etc., which were very popular at the time, all used this method to build recommendation systems [10].…”

Section: Related Workmentioning

confidence: 99%

Research of Personalized Recommendation Technology Based on Knowledge Graphs

et al. 2021

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Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs (KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

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“…Therefore, dynamic ontology was designed. In [50], dynamic ontology was proposed manage large number of concepts which human beings couldn't achieve alone. Reference [51] proposed a goal-driven dynamic ontology for business process.…”

Section: A Tatget Ontologymentioning

confidence: 99%

Top-Down Human-Cyber-Physical Data Fusion Based on Reinforcement Learning

Chen

Wang

et al. 2020

IEEE Access

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With the development of industrial Internet and artificial intelligence, data fusion in crossdomains and cross-layers have become an inevitable trend. Most of the data fusion involved in the production process of hot rolling are concentrated on the level of sensors, Internet of Things (IoT) and the Internet; but human data are not well integrated. In order to avoid the human factor from becoming the bottleneck of the entire production schedule, this paper proposes a ternary data fusion model based on reinforcement learning algorithm. The related data source from human-cyber-physical space includes: social network, Internet and IoT. By merging the ternary data, a variety of data (including humans') can be quickly calculated to obtain better and faster decisions. In order to achieve automated fusion from ternary data, this paper proposes a method based on reinforcement learning: firstly, the domain ontology used for associating ternary data is reduced and tessellated (dimension reduction), and then the reinforcement learning model is used to form "the new ontology". Compared with resource-intensive global calculations (which may cost a few days), the new method can complete the calculations in minutes. This means that the new method optimizes the data source required for decision-making and improves the efficiency. Finally, the production scheduling of hot rolled steel is used as an example to verify the feasibility of the proposed method. INDEX TERMS human-cyber-physical data fusion, ternary data fusion, cyber-physical system, domain ontology, reinforcement learning

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Concept Based Dynamic Ontology Creation for Job Recommendation System

Cited by 19 publications

References 8 publications

Construction and Recommendation of a Water Affair Knowledge Graph

Construction and Recommendation of a Water Affair Knowledge Graph

Research of Personalized Recommendation Technology Based on Knowledge Graphs

Top-Down Human-Cyber-Physical Data Fusion Based on Reinforcement Learning

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