The International Society for the Study of Vascular Anomalies (ISSVA) ontology

The International Society for the Study of Vascular Anomalies (ISSVA) provides a classification for vascular anomalies that enables specialists to unambiguously classify diagnoses. This classification is only available in PDF format and is not machine-readable, nor does it provide unique identifiers that allow for structured registration. In this paper, we describe the process of transforming the ISSVA classification into an ontology. We also describe the structure of this ontology, as well as two applications of the ontology using examples from the domain of rare disease research. We used the expertise of an ontology expert and clinician during the development process. We semi-automatically added mappings to relevant external ontologies using automated ontology matching systems and manual assessment by experts. The ISSVA ontology should contribute to making data for vascular anomaly research more Findable, Accessible, Interoperable, and Reusable (FAIR). The ontology is available at https://bioportal.bioontology.org/ontologies/ISSVA.     

A synergistic strategy for combining thesaurus-based and corpus-based approaches in building ontology for multilingual search engines

In this article we illustrate a methodology for building cross-language search engine. A synergistic approach between thesaurus-based approach and corpus-based approach is proposed. First, a bilingual ontology thesaurus is designed with respect to two languages: English and Spanish, where a simple bilingual listing of terms, phrases, concepts, and subconcepts is built. Second, term vector translation is used – a statistical multilingual text retrieval techniques that maps statistical information about term use between languages (Ontology co-learning). These techniques map sets of t f id f term weights from one language to another. We also applied a query translation method to retrieve multilingual documents with an expansion technique for phrasal translation. Finally, we present our findings.     

Augmented context-based recommendation service framework using knowledge over the Linked Open Data cloud

This research proposes ACARDS (Augmented-Context bAsed RecommenDation Service) framework that is able to utilize knowledge over the Linked Open Data (LOD) cloud to recommend context-based services to users. To improve the level of user satisfaction with the result of the recommendation, the ACARDS framework implements a novel recommendation algorithm that can utilize the knowledge over the LOD cloud. In addition, the noble algorithm is able to use new concepts like the enriched tags and the augmented tags that originate from the hashtags on the SNSs materials. These tags are utilized to recommend the most appropriate services in the user’s context, which can change dynamically. Last but not least, the ACARDS framework implements the context-based reshaping algorithm on the augmented tag cloud. In the reshaping process, the ACARDS framework can recommend the highly receptive services in the users’ context and their preferences. To evaluate the performance of the ACARDS framework, we conduct four kinds of experiments using the Instagram materials and the LOD cloud. As a result, we proved that the ACARDS framework contributes to increasing the query efficiency by reducing the search space and improving the user satisfaction on the recommended services.     

Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies’ capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.     

Survey of domestic food purchases and related home handling practices in the Abruzzo region (central Italy): Data collection and analysis through a language-independent classification system

A telephone survey was carried out on 13,486 randomly selected households located in the Abruzzo region (central Italy). Three questionnaires were specifically designed in relation to three different groups of foods (Meat and meat products, Fishery products, Fruit and vegetables). Questions were mainly focused on the amount and number of purchases, type of retailer, and home food handling practices with respect to the week prior to the interview. Data were classified according to a multilingual thesaurus system (LanguaL). Results allowed to estimate domestic purchases (in kg) per capita for different food categories. The category “Red meat/Cattle” accounted for a large part of purchases in the “meat and meat products” group (10.9 kg per capita/year; 32.2% of purchases in the group) while the category “Fish or related organism/Fish” was the most purchased in the “fishery products” group (6.9 kg per capita/year; 63.3%). The aggregation of more detailed characteristics enabled the identification of popular categories of foods, such as “Red meat/Cattle/Divided into pieces” (25% of the “meat and meat products” group). Significant differences (p < 0.05) were found between the four different provinces (L'Aquila, Chieti, Pescara and Teramo) with respect to “Poultry/Chicken” and “Red meat/Cattle” categories. Householders were also asked about post-purchase food handling practices that might be hazardous to food safety. More than 20% of those surveyed stated that they thawed frozen meat at room temperature. The degree of doneness after cooking of different food categories was generally high: over 90% of products purchased in the majority of meat and fish categories were properly cooked. However a noticeable proportion of householders (about 15%) reported medium or rare cooking of “Red meat/Cattle” and “Red meat/Swine”. Differences (p < 0.05) were also found between consumers of different ages, with people over 65 years old being more prone to freeze meat and cook it thoroughly.The survey was carried out in a specific geographic area and on a statistically significant sample of households, thus allowing a collection of data on domestic habits relating to food purchases and home food handling practices. This information should be included in the framework of quantitative microbial risk assessment (QMRA) models as a measure of the actual exposure of consumers to pathogenic microorganism. The LanguaL system also proved to be a practical language-independent method useful not only to identify and describe food items but also to classify them according to specific food safety characteristics.     

Using Web resources to construct multilingual medical thesaurus for cross-language medical information retrieval

Due to the language barrier, non-English users are unable to retrieve the most updated medical information from the U.S. authoritative medical websites, such as PubMed and MedlinePlus. However, currently, there is no any cross-language medical information retrieval (CLMIR) system that can help Chinese-speaking consumers cross the language barrier in finding useful English medical information. A few CLMIR systems utilize MeSH (Medical Subject Headings) to help overcome the language barrier. Unfortunately, the traditional Chinese version of MeSH is currently unavailable.In this paper, we employ a semi-automatic term translation method to construct a Chinese–English MeSH by exploiting abundant multilingual Web resources, including Web anchor texts and search–result pages. Through this method, we have developed a Chinese–English Mesh Compilation System to assist knowledge engineers in compiling a Chinese–English medical thesaurus with more than 19,000 entries. Furthermore, this thesaurus has been used to develop a prototypical system for cross-language medical information retrieval, MMODE, which can help consumers retrieve top-quality English medical information using Chinese terms.     

Swoop: A Web Ontology Editing Browser

In this paper, we describe Swoop, a hypermedia inspired Ontology Browser and Editor based on OWL, the recently standardized Web-oriented ontology language. After discussing the design rationale and architecture of Swoop, we focus mainly on its features, using illustrative examples to highlight its use. We demonstrate that with its Web-metaphor, adherence to OWL recommendations and key unique features, such as Collaborative Annotation using Annotea, Swoop acts as a useful and efficient Web Ontology development tool. We conclude with a list of future plans for Swoop, that should further increase its overall appeal and accessibility.     

异构空间信息的分级共识模型研究

Internet上异构、分布的空间信息因各自独立、相对封闭而无法相互沟通和协作,从而形成了空间信息孤岛。论文利用计算机的知识表示理论和Ontology技术能够明确表达相关领域共识的特点,探索性地提出了异构空间信息的分级共识模型。该模型为解决上述问题提供了一条切实可行的新思路。     

本体在Web服务发现中的应用

现在人们主要通过UDDI来查找Web服务,但其仅仅基于关键字,这决定了它不能根据所需功能的语义来更好地发现Web服务。本文先试图用DAML-S语言来描述Web服务的功能本体,然后提出了一个Web服务匹配框架-DAML-S/UDDI匹配器框架（DUMF）。虽然这一框架本身存在一些不完善的地方,但随着进一步改进,预计将在一定程度上解决Web服务发现的问题。     

基于本体的数字卡通项目知识共享与维护

针对数字卡通项目开发过程中知识管理和共享所面临的难题,提出了一种基于本体的数字卡通项目知识共享和维护解决方案。探讨了如何构造面向数字卡通项目管理的本体COPMO,给出了一种基于本体的知识共享和推送框架CKSF,并且研究了COPMO本体的演化和维护问题。