The Hidden Web, XML and Semantic Web: A Scientific Data Management Perspective

The World Wide Web no longer consists just of HTML pages. Our work sheds light on a number of trends on the Internet that go beyond simple Web pages. The hidden Web provides a wealth of data in semi-structured form, accessible through Web forms and Web services. These services, as well as numerous other applications on the Web, commonly use XML, the eXtensible Markup Language. XML has become the lingua franca of the Internet that allows customized markups to be defined for specific domains. On top of XML, the Semantic Web grows as a common structured data source. In this work, we first explain each of these developments in detail. Using real-world examples from scientific domains of great interest today, we then demonstrate how these new developments can assist the managing, harvesting, and organization of data on the Web. On the way, we also illustrate the current research avenues in these domains. We believe that this effort would help bridge multiple database tracks, thereby attracting researchers with a view to extend database technology.Comment: EDBT - Tutorial (2011

Challenges and potential of the Semantic Web for tourism

The paper explores tourism challenges and potential of the Semantic Web from a theoretical and industry perspective. It first examines tourism business networks and explores a main theme of network interoperability - data standards- followed by technology deficiencies of Web 1.0 and 2.0 and Semantic Web solutions. It then explicates Semantic opportunities and challenges for tourism, including an industry perspective through a qualitative approach. Industry leaders considered that the new Web era was imminent and heralded benefits for supply and demand side interoperability, although management and technical challenges could impede progress and delay realisation

Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

Linked Data for the Natural Sciences. Two Use Cases in Chemistry and Biology

Wiljes C, Cimiano P. Linked Data for the Natural Sciences. Two Use Cases in Chemistry and Biology. In: Proceedings of the Workshop on the Semantic Publishing (SePublica 2012). 2012: 48-59.The Web was designed to improve the way people work together. The Semantic Web extends the Web with a layer of Linked Data that offers new paths for scientific publishing and co-operation. Experimental raw data, released as Linked Data, could be discovered automatically, fostering its reuse and validation by scientists in different contexts and across the boundaries of disciplines. However, the technological barrier for scientists who want to publish and share their research data as Linked Data remains rather high. We present two real-life use cases in the fields of chemistry and biology and outline a general methodology for transforming research data into Linked Data. A key element of our methodology is the role of a scientific data curator, who is proficient in Linked Data technologies and works in close co-operation with the scientist

Ontology-Based Recommendation of Editorial Products

Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have addressed this issue by creating Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University (OU) in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues. SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products. This is based on the Computer Science Ontology, a very large-scale, automatically generated taxonomy of research areas. SBR also allows users to investigate why a certain publication was suggested by the system. It does so by means of an interactive graph view that displays the topic taxonomy of the recommended editorial product and compares it with the topic-centric characterization of the input conference. An evaluation carried out with seven Springer Nature editors and seven OU researchers has confirmed the effectiveness of the solution

Creating information delivery specifications using linked data

The use of Building Information Management (BIM) has become mainstream in many countries. Exchanging data in open standards like the Industry Foundation Classes (IFC) is seen as the only workable solution for collaboration. To define information needs for collaboration, many organizations are now documenting what kind of data they need for their purposes. Currently practitioners define their requirements often a) in a format that cannot be read by a computer; b) by creating their own definitions that are not shared. This paper proposes a bottom up solution for the definition of new building concepts a property. The authors have created a prototype implementation and will elaborate on the capturing of information specifications in the future

Dissemination of evidence-based standards of care.

Standards of care pertain to crafting and implementing patient-centered treatment interventions. Standards of care must take into consideration the patient's gender, ethnicity, medical and dental history, insurance coverage (or socioeconomic level, if a private patient), and the timeliness of the targeted scientific evidence. This resolves into a process by which clinical decision-making about the optimal patient-centered treatment relies on the best available research evidence, and all other necessary inputs and factors to provide the best possible treatment. Standards of care must be evidence-based, and not merely based on the evidence - the dichotomy being critical in contemporary health services research and practice. Evidence-based standards of care must rest on the best available evidence that emerges from a concerted hypothesis-driven process of research synthesis and meta-analysis. Health information technology needs to become an every-day reality in health services research and practice to ensure evidence-based standards of care. Current trends indicate that user-friendly methodologies, for the dissemination of evidence-based standards of care, must be developed, tested and distributed. They should include approaches for the quantification and analysis of the textual content of systematic reviews and of their summaries in the form of critical reviews and lay-language summaries

Coping with lists in the ifcOWL ontology

Over the past few years, several suggestions have been made of how to convert an EXPRESS schema into an OWL ontology. The conversion from EXPRESS to OWL is of particular use to architectural design and construction industry, because one of the key data models in architectural design and construction industry, namely the Industry Foundation Classes (IFC) is represented using the EXPRESS information modelling language. In each of these conversion options, the way in which lists are converted (e.g. lists of coordinates, lists of spaces in a floor) is key to the structure and eventual strength of the resulting ontology. In this article, we outline and discuss the main decisions that can be made in converting LIST concepts in EXPRESS to equivalent OWL expressions. This allows one to identify which conversion option is appropriate to support proper and efficient information reuse in the domain of architecture and construction

Representing simmodel in the web ontology language

Many building energy performance (BEP) simulation tools, such as EnergyPlus and DOE-2, use custom schema definitions (IDD and BDL respectively) as opposed to standardised schema definitions (defined in XSD, EXPRESS, and so forth). A Simulation Domain Model (SimModel) was therefore proposed earlier, representative for a new interoperable XML-based data model for the building simulation domain. Its ontology aims at moving away from tool-specific, non-standard nomenclature by implementing an industry-validated terminology aligned with the Industry Foundation Classes (IFC). In this paper, we document our ongoing efforts to make building simulation data more interoperable with other building data. In order to be able to better integrate SimModel information with other building information, we have aimed at representing this information in the Resource Description Framework (RDF). A conversion service has been built that is able to parse the SimModel ontology in the form of XSD schemas and output a SimModel ontology in OWL. In this article, we document this effort and give an indication of what the resulting SimModel ontology in OWL can be used for