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The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC)personalized recommendation method to help learners efficiently obtain MOOC resources.

This study introduced ontology construction technology and a new semantic association algorithm to form a new MOOC resource personalized recommendation idea. On the one hand, by constructing a learner model and a MOOC resource ontology model, based on the learner’s characteristics, the learner’s MOOC resource learning preference is predicted, and a recommendation list is formed. On the other hand, the semantic association algorithm is used to calculate the correlation between the MOOC resources to be recommended and the learners’ rated resources and predict the learner’s learning preferences to form a recommendation list. Finally, the two recommendation lists were comprehensively analyzed to form the final MOOC resource personalized recommendation list.

The semantic association algorithm based on hierarchical correlation analysis and attribute correlation analysis introduced in this study can effectively analyze the semantic similarity between MOOC resources. The hybrid recommendation method that introduces ontology construction technology and performs semantic association analysis can effectively realize the personalized recommendation of MOOC resources.

This study has formed an effective method for personalized recommendation of MOOC resources, solved the problems existing in the personalized recommendation that is, the recommendation relies on the learner’s rating of the resource, the recommendation is specialized, and the knowledge structure of the recommended resource is static, and provides a new idea for connecting MOOC learners and resources.

This paper aims to solve the disadvantages of content-based domain ontology (CBDO) and metadata-based domain ontology (MDO) and improve organization and discovery efficiency of library resources by resource ontology (RO).

The paper constructed an RO model. Methods of informetrics are utilized to reveal semantic relationships among library resources. Methods of ontology, ontology-relational database mapping (O-R mapping) and relational database modelling are utilized to construct RO. Take author co-occurrence for example, the paper demonstrated the capability of RO model.

RO not only revealed the deep-level semantic relationships of metadata of library resources but also realized totally computer-automated processing. RO improved the efficiency of knowledge organization and discovery.

Semantic relationships revealed by RO are limited to simple metadata, which makes it difficult to reveal fine-grained semantic relationships. Ongoing research focuses on the revelation of semantic relationships based on the title and abstract.

The paper includes implications for utilizing methods of Informetrics to construct ontology.

This paper proposed a standardized process of ontology construction in library resources. It may be of potential interest for anyone who needs to effectively organize library resources.

Pertinent information sharing across various government agencies, as well as non‐governmental and private organizations, is essential to assess the incident situation, identify the needed resources for emergency response and generate response plans. However, each agency may have incident management systems of its choice with valuable information in its own format, posing difficulty in effective information sharing. Application‐to‐application sharing cross agency boundaries will significantly reduce human efforts and delay in emergency response. Information sharing from disparate systems and organizations, however, requires solving of the interoperability issue. The purpose of this paper is to present the UICDS™‐based resource sharing framework as a step toward addressing the afore‐mentioned challenges.

A prototype middleware system is developed using a standards‐based information sharing infrastructure called UICDS™ (Unified Incident Command and Decision Support™), an initiative led by the Department of Homeland Security (DHS) Science and Technology division. This standards‐based middleware, resource management plug‐in utilizes the ontology of organizational structure, workflow activities and resources, and the inference rules to discover and share resource information and interoperability from different incident management applications.

The middleware prototype implementation shows that the UICDS™‐based interoperability between heterogeneous incident management applications is feasible. Specifically, the paper shows that the resource data stored in the Resource Directory Database (RDDB) of the NJ Office of Emergency Management (NJOEM), Hippocrates of the New Jersey Department of Health and Senior Services (NJDHSS) can be discovered and shared with other incident management systems using the ontology and inference rules.

This study illustrates the possible solutions to the application to application interoperability problem using the DHS initiated interoperability platform called UICDS™.

The resource discovery and emergency response planning can be automated using the incident domain ontology and inference rules to dynamically generate the location‐based incident response workflows.

The purpose of this study is to construct a Chinese information science resource ontology and to explore a new method for semiautomatic ontology construction.

More than 8,290 articles indexed in the Chinese Social Science Citation Index (CSSCI), covering the years 2001 to 2010, were included in this study. Statistical analysis, co-occurrence analysis, and semantic similarity methods were applied to the selected articles. The ontology was built using existing construction principles and methods, as well as categories and hierarchy definitions based on CSSCI indexing fields.

Seven categories were found to be relevant for the Chinese information science resource ontology, which, in this study, consists of a three-tier architecture, 78,291 instances, and 182,109 pairs of semantic relations. These results indicate the following: further improvements are required in ontology construction methods; resource ontology is a breakthrough concept in ontology studies; the combination of semantic similarities and co-occurrence analysis can quantitatively describe relationships between concepts.

This study pioneers the resource ontology concept. It is one of the first to combine informetric methods with semantic similarity to reveal deep relationships in textual data.

From knowledge management point of view, business process models and ontologies are two essential knowledge artifacts for organizations that consume similar information sources. In this study, the PROMPTUM method for integrated process modeling and ontology development that adheres to well-established practices is presented. The method is intended to guide practitioners who develop both ontologies and business process models in the same or similar domains.

The method is supported by a recently developed toolset, which supports the modeling of relations between the ontologies and the labels within the process model collections. This study introduces the method and its companion toolset. An explanatory study, that includes two case studies, is designed and conducted to reveal and validate the benefits of using the method. Then, a follow-up semi-structured interview identifies the perceived benefits of the method.

Application of the method revealed several benefits including the improvements observed in the consistency and completeness of the process models and ontologies. The method is bringing the best practices in two domains together and guiding the use of labels within process model collections in ontology development and ontology resources in business process modeling.

The proposed method with its tool support is a pioneer in enabling to manage the labels and terms within the labels in process model collections consistently with ontology resources. Establishing these relations enables the definition and management of process model elements as resources in domain ontologies. Once the PROMPTUM method is utilized, a related resource is managed as a single resource representing the same real-world object in both artifacts. An explanatory study has shown that improvement in consistency and completeness of process models and ontologies is possible with integrated process modeling and ontology development.

The paper's aims is to present research that evaluates a technology that assists in organizing and retrieving knowledge stored in a variety of forms (books, papers, models, decision support systems, databases) through a real world application.

Ontology has been used to manage the Water Conservation Digital Library in Florida, USA, which holds a dynamic collection of various types of digital resources in the domain of urban water conservation. The ontology based back‐end powers a fully operational web interface, available at: http://library.conservefloridawater.org Findings – The system has demonstrated numerous benefits of the ontology application, including easier and more precise finding of resources, information sharing and reuse, and has proved to effectively facilitate information management.

A large and dynamic number of concepts makes it difficult to keep the ontology consistent and to accurately manually catalog resources. To address these issues, ongoing research focuses on the area of information extraction with the aid of natural language processing techniques.

The paper presents a real‐world‐verified application of ontology to a digital library. It may be of potential interest for anyone who needs to effectively manage a collection of digital resources.

The booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation.

To solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules.

An organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources.

The case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry.

To improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.

Web site recommendation systems help to get high quality information. The modelling of recommendation systems involves the combination of many features: metrics of quality, quality criteria, recommendation criteria, user profile, and specific domain concepts, among others. At the moment of the specification of a recommendation system it must be guaranteed a right interrelation of all of these features. The purpose of this paper is to model a web site quality‐based recommendation system by an ontology network.

In this paper, the authors propose an ontology network based process for web site recommendation modelling. The ontology network conceptualizes the different domains (web site domain, quality assurance domain, user context domain, recommendation criteria domain, specific domain) in a set of interrelated ontologies. Particularly, this approach is illustrated for the health domain.

Basically, this work introduces the semantic relationships that were used to construct this ontology network. Moreover, it shows the usefulness of this ontology network for the detection of possible inconsistencies when specifying recommendation criteria.

Recommendation systems based on ontologies that model the user profile and the domain of resources to be recommended are quite common. However, it is uncommon to find models that explicitly represent the criteria used by the recommender systems, that express the quality dimensions of resources and on which criteria are applied, and consider the user context at the moment of the query.

Topic segmentation is one of the active research fields in natural language processing. Also, many topic segmenters have been proposed. However, the current challenge of researchers is the improvement of these segmenters by using external resources. Therefore, the purpose of this paper is to integrate study and evaluate a new external semantic resource in topic segmentation.

New topic segmenters (TSS-Onto and TSB-Onto) are proposed based on the two well-known segmenters C99 and TextTiling. The proposed segmenters integrate semantic knowledge to the segmentation process by using a domain ontology as an external resource. Subsequently, an evaluation is made to study the effect of this resource on the quality of topic segmentation along with a comparative study with related works.

Based on this study, the authors showed that adding semantic knowledge, which is extracted from a domain ontology, improves the quality of topic segmentation. Moreover, TSS-Ont outperforms TSB-Ont in terms of quality of topic segmentation.

The main limitation of this study is the used test corpus for the evaluation which is not a benchmark. However, we used a collection of scientific papers from well-known digital libraries (ArXiv and ACM).

The proposed topic segmenters can be useful in different NLP applications such as information retrieval and text summarizing.

The primary original contribution of this paper is the improvement of topic segmentation based on semantic knowledge. This knowledge is extracted from an ontological external resource.