Automating the expansion of a knowledge graph

Yoo, SoYeop; Jeong, Ok‐Ran

doi:10.1016/j.eswa.2019.112965

Cited by 36 publications

(11 citation statements)

References 12 publications

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“…In the pretraining stage, it is trained on existing unlabeled text in advance and is released as a general language model. In the fine-tuning stage, it can be fine-tuned using learning data, according to the task to be performed [42,43].…”

Section: Word Vector Generation Based On Bertmentioning

confidence: 99%

Bert-Based Latent Semantic Analysis (Bert-LSA): A Case Study on Geospatial Data Technology and Application Trend Analysis

et al. 2021

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Geospatial data is an indispensable data resource for research and applications in many fields. The technologies and applications related to geospatial data are constantly advancing and updating, so identifying the technologies and applications among them will help foster and fund further innovation. Through topic analysis, new research hotspots can be discovered by understanding the whole development process of a topic. At present, the main methods to determine topics are peer review and bibliometrics, however they just review relevant literature or perform simple frequency analysis. This paper proposes a new topic discovery method, which combines a word embedding method, based on a pre-trained model, Bert, and a spherical k-means clustering algorithm, and applies the similarity between literature and topics to assign literature to different topics. The proposed method was applied to 266 pieces of literature related to geospatial data over the past five years. First, according to the number of publications, the trend analysis of technologies and applications related to geospatial data in several leading countries was conducted. Then, the consistency of the proposed method and the existing method PLSA (Probabilistic Latent Semantic Analysis) was evaluated by using two similar consistency evaluation indicators (i.e., U-Mass and NMPI). The results show that the method proposed in this paper can well reveal text content, determine development trends, and produce more coherent topics, and that the overall performance of Bert-LSA is better than PLSA using NPMI and U-Mass. This method is not limited to trend analysis using the data in this paper; it can also be used for the topic analysis of other types of texts.

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Section: Word Vector Generation Based On Bertmentioning

confidence: 99%

Bert-Based Latent Semantic Analysis (Bert-LSA): A Case Study on Geospatial Data Technology and Application Trend Analysis

et al. 2021

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“…By using the generated knowledge graph to answer questions about the different contributions covered by the survey, and to evaluate how the query answers meet the reader's information needs compared with letting readers extract the same information from reading survey papers, to demonstrate the utility of the knowledge graph. Some companies have used knowledge graphs to improve existing products and have developed prominent examples of large-scale knowledge graphs, including DBpedia [13], Google knowledge graph [14], Microsoft's Satori [15], Freebase, YAGO, and Wikidata [16]. Jayaram and Khan et al [17] established a "Graph Query by Example" system, automatically finds the weighted hidden maximum query graph based on the input query tuple to obtain the user's query intent.…”

Section: Knowledge Graphmentioning

confidence: 99%

Process Knowledge Graph Modeling Techniques and Application Methods for Ship Heterogeneous Models

Liu

Dong

Jing

et al. 2021

Preprint

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In the process design and reuse of marine component products, there are a lot of heterogeneous models, causing the problem that the process knowledge and process design experience contained in them are difficult to express and reuse. Therefore, a process knowledge representation model for ship heterogeneous model is proposed in this paper. Firstly, the multi-element process knowledge graph is constructed, and the heterogeneous ship model is described in a unified way. Then, the multi-strategy ontology mapping method is applied, and the semantic expression between the process knowledge graph and the entity model is realized. Finally, by obtaining implicit semantics based on case-based reasoning and checking the similarity of the matching results, the case knowledge reuse is achieved, to achieve rapid design of the process. This method provides reliable technical support for the design of ship component assembly and welding process, greatly shortens the design cycle, and improves the working efficiency. In addition, a case study of the test model is carried out to verify the feasibility and efficiency of the proposed method.

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“…However, KG has limitations in scope and size. Yoo and Jeong [68] introduced a model namely as PolarisX to enlarge concept net KG automatically. PolarisX is flexible in understanding common situations and analyzing the relations between two entities.…”

Section: B Knowledge Graphmentioning

confidence: 99%

A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

et al. 2020

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Context and background: Complex fuzzy theory has a strong practical implication in many real-world applications. Complex Fuzzy Inference System (CFIS) is a powerful technique to overcome the challenges of uncertain, periodic data. However, a question is raised for CFIS: How can we deduce and predict the result in case there is little knowledge about data information and rule base? This is significance because many real applications do not have enough knowledge of rule base for inference so that the performance of systems may be low. Thus, it is necessary to have an approximate reasoning method to represent and derive final results. Motivation: Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been proposed with a specific inference mechanism according to the Mamdani type. A new improvement so-called the Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R) has been designed to utilize granular computing with complex similarity measures to reduce the rule base so as to gain better performance in decision-making problems. However in M-CFIS-R, testing data are checked by matching with each rule in the rule base, which leads to a high cost of computational time. Besides, if the testing data contain records that are not inferred by the rule base, the output cannot be generated. This happens in real commerce systems in which the rule base is small at the time of creation and needs to feed with new rules. Methodology: In order to handle those issues, this paper first time proposes the Fuzzy Knowledge Graph to represent the rule base in terms of linguistic labels and their relationships according to the rule set. An adjacent matrix of Fuzzy Knowledge Graph is generated for inference. When a record in the Testing dataset is given, it would be fuzzified and labelled. Each component in the record is checked with the Fuzzy Knowledge Graph by the inference mechanism in approximate reasoning called Fast Inference Search Algorithm. Then, we derive the label of the new record by the Max-Min operator. Besides, we also propose four extensions of

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Automating the expansion of a knowledge graph

Cited by 36 publications

References 12 publications

Bert-Based Latent Semantic Analysis (Bert-LSA): A Case Study on Geospatial Data Technology and Application Trend Analysis

Bert-Based Latent Semantic Analysis (Bert-LSA): A Case Study on Geospatial Data Technology and Application Trend Analysis

Process Knowledge Graph Modeling Techniques and Application Methods for Ship Heterogeneous Models

A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

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