Employing use-cases and domain knowledge for comprehending resource usage-experience report

Author(s):  
R.L. Krikhaar ◽  
M.P. Pennings ◽  
J. Zonneveld
2019 ◽  
Vol 1 (3) ◽  
pp. 201-223 ◽  
Author(s):  
Guohui Xiao ◽  
Linfang Ding ◽  
Benjamin Cogrel ◽  
Diego Calvanese

In this paper, we present the virtual knowledge graph (VKG) paradigm for data integration and access, also known in the literature as Ontology-based Data Access. Instead of structuring the integration layer as a collection of relational tables, the VKG paradigm replaces the rigid structure of tables with the flexibility of graphs that are kept virtual and embed domain knowledge. We explain the main notions of this paradigm, its tooling ecosystem and significant use cases in a wide range of applications. Finally, we discuss future research directions.


2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.


Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 140
Author(s):  
Tristan Langer ◽  
Tobias Meisen

Exploratory data analysis (EDA) is an iterative process where data scientists interact with data to extract information about their quality and shape as well as derive knowledge and new insights into the related domain of the dataset. However, data scientists are rarely experienced domain experts who have tangible knowledge about a domain. Integrating domain knowledge into the analytic process is a complex challenge that usually requires constant communication between data scientists and domain experts. For this reason, it is desirable to reuse the domain insights from exploratory analyses in similar use cases. With this objective in mind, we present a conceptual system design on how to extract domain expertise while performing EDA and utilize it to guide other data scientists in similar use cases. Our system design introduces two concepts, interaction storage and analysis context storage, to record user interaction and interesting data points during an exploratory analysis. For new use cases, it identifies historical interactions from similar use cases and facilitates the recorded data to construct candidate interaction sequences and predict their potential insight—i.e., the insight generated from performing the sequence. Based on these predictions, the system recommends the sequences with the highest predicted insight to data scientist. We implement a prototype to test the general feasibility of our system design and enable further research in this area. Within the prototype, we present an exemplary use case that demonstrates the usefulness of recommended interactions. Finally, we give a critical reflection of our first prototype and discuss research opportunities resulting from our system design.


2016 ◽  
Vol 55 (06) ◽  
pp. 488-494 ◽  
Author(s):  
Amnon Shabo ◽  
Enea Parimbelli ◽  
Silvana Quaglini ◽  
Carlo Napolitano ◽  
Mor Peleg

SummaryBackground: Implementing a decision-support system within a healthcare organization requires integration of clinical domain knowledge with resource constraints. Computer-interpretable guidelines (CIG) are excellent instruments for addressing clinical aspects while business process management (BPM) languages and Workflow (Wf) engines manage the logistic organizational constraints.Objectives: Our objective is the orchestra -tion of all the relevant factors needed for a successful execution of patient’s care pathways, especially when spanning the contin -uum of care, from acute to community or home care.Methods: We considered three strategies for integrating CIGs with organizational work-flows: extending the CIG or BPM languages and their engines, or creating an interplay between them. We used the interplay approach to implement a set of use cases arising from a CIG implementation in the domain of Atrial Fibrillation. To provide a more scalable and standards-based solution, we explored the use of Cross-Enterprise Document Workflow Integration Profile.Results: We describe our proof-of-concept implementation of five use cases. We utilized the Personal Health Record of the MobiGuide project to implement a loosely-coupled approach between the Activiti BPM engine and the Picard CIG engine. Changes in the PHR were detected by polling. IHE profiles were used to develop workflow documents that orchestrate cross-enterprise execution of cardioversion.Conclusions: Interplay between CIG and BPM engines can support orchestration of care flows within organizational settings.


2011 ◽  
pp. 880-908
Author(s):  
Alain Léger ◽  
Johannes Heinecke ◽  
Lyndon J.B. Nixon ◽  
Pavel Shvaiko ◽  
Jean Charlet ◽  
...  

Semantic Web technology is being increasingly applied in a large spectrum of applications in which domain knowledge is conceptualized and formalized (e.g., by means of an ontology) in order to support diversified and automated knowledge processing (e.g., reasoning) performed by a machine. Moreover, through an optimal combination of (cognitive) human reasoning and (automated) machine processing (mimicking reasoning); it becomes possible for humans and machines to share more and more complementary tasks. The spectrum of applications is extremely large and to name a few: corporate portals and knowledge management, e-commerce, e-work, e-business, healthcare, e-government, natural language understanding and automated translation, information search, data and services integration, social networks and collaborative filtering, knowledge mining, business intelligence and so on. From a social and economic perspective, this emerging technology should contribute to growth in economic wealth, but it must also show clear cut value for everyday activities through technological transparency and efficiency. The penetration of Semantic Web technology in industry and in services is progressing slowly but accelerating as new success stories are reported. In this chapter we present ongoing work in the cross-fertilization between industry and academia. In particular, we present a collection of application fields and use cases from enterprises which are interested in the promises of Semantic Web technology. The use cases are focused on the key knowledge processing components that will unlock the deployment of the technology in industry. The chapter ends with the presentation of the current state of the technology and future trends as seen by prominent actors in the field.


Author(s):  
Gregory K. W. K. Chung ◽  
Eva L. Baker ◽  
David G. Brill ◽  
Ravi Sinha ◽  
Farzad Saadat ◽  
...  

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.


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