This paper presents an ontology-based approach to benefit automatic fertilization management for citrus orchards located in mountainous region. The core of the fertilization approach is the citrus fertilization ontology, which covers knowledge about citrus fertilizers and fertilization application. Specially, our approach can provide not only the yearly fertilization quantities of required pure nitrogen, phosphorus, and potassium according to their disease symptoms, but also the suitable fertilizing recommendations for the citrus orchards with different soil properties. The current version of the ontology (ver. 2.9.10) contains 103 classes, 34 properties, 800 instances, which are defined by 3056 RDF triples and is evaluated by using 90 competency questions. Furthermore, we run experiments with our proposal targeting at four citrus orchards in Chongqing, and compare its outputs with the reference values advised by the agri-professionals of citrus planting.

The explosive growth of RDF data makes it difficult to be efficiently queried, understood and used. RDF graph (RDFG) summarization aims to extract the most relevant and crucial data as summaries according to different criteria. Current summarization approaches mainly apply single strategies such as graph structure, pattern mining or relevance metrics to calculate RDFG summaries. Different to the existing approaches, this paper proposes a summarization approach to automatically generating RDFG summary, which can capture both structure and centrality information. Specifically, we present three algorithms, SumW (merging nodes based on node characteristics or similar types), SumS (merging nodes based on typed node characteristics) and SummaryFL (retrieving central nodes by combining node frequency and bridging coefficient). The three algorithms can be used by two summarization strategies: SumS or SumW only, and SumS+SummaryFL or SumW+SummaryFL. We conducted experiments over large and real-world RDF datasets to verify the effectiveness of our method with respect to time complexity, compression capability and coverage of the summary. The experiment results demonstrate that our approach outperformed the comparative algorithms.

RDF graphs have been widely used in various cognitive and intelligent services in industry. Recent tremendous growth in knowledge base data volumes has made searching and querying RDF graphs increasingly difficult. Summarization is an effective solution for managing large RDF graphs by extracting critical data into summary graphs. Improving the accuracy of the summary relative to the original graph and the relevance to actual user demands improves the efficiency and usefulness of the queries against the summary. In this paper, we present a hybrid summarization method that takes into account both the graph structure and user query history. Specifically, we define a hybrid metric of node importance that captures both the structural importance and user query preferences. We propose two algorithms to extract summaries of a given RDF graph based on this hybrid metric. We evaluate our approach in experiments using three public datasets (DBpedia, YAGO, and Freebase), and the results demonstrate the efficiency of our approach.

Ontology technology has been investigated in a wide range of areas and is currently being utilized in many fields. In the e-learning context, many studies have used ontology to address problems such as the interoperability in learning objects, modeling and enriching learning resources, and personalizing educational content recommendations. We systematically reviewed research on ontology for e-learning from 2008 to 2020. The review was guided by 3 research questions: “How is ontology used for knowledge modeling in the context of e-learning?”, “What are the design principles, building methods, scale, level of semantic richness, and evaluation of current educational ontologies?”, and “What are the various ontology-based applications for e-learning?” We classified current educational ontologies into 6 types and analyzed them by 5 measures: design methodology, building routine, scale of ontology, level of semantic richness, and ontology evaluation. Furthermore, we reviewed 4 types of ontology-based e-learning applications and systems. The observations obtained from this survey can benefit researchers in this area and help to guide future research.

In this paper, we propose an approach to deal with the change management for service oriented business processes. Beyond existing work, the proposed approach highlights the dependencies between services and business processes. A service oriented business process model is devised for capturing the major characteristics of change management in service oriented context. The taxonomy for the changes associated with services and business processes is presented. A set of change impact patterns are specified and the functions for calculating impact scopes of a change are defined. With the help of the change taxonomy and the change impact patterns, the ripple effect of changes of the business processes and services can be clearly analyzed. This research provides a step progress for change management in the service oriented environment.