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

The Smart Book Recommender: An Ontology-Driven Application for Recommending Editorial Products

Promoting books and journals to the relevant research communities is an important task for major academic publishers. Unfortunately, identifying which are the best editorial products to market at a certain academic venue is a time-consuming and error-prone process. Here we present the Smart Book Recommender (SBR), an ontology-based recommender that supports the Springer Nature editorial team in selecting the editorial products to market at specific venues. SBR provides an interactive visualisation for analysing the topics characterizing conference series and books. It builds on a dataset of 27K books, journals, and conference proceedings annotated with topics from the Computer Science Ontology, a large-scale ontology of research areas. A user study showed that SBR is able to produce useful recommendations for both editors and researchers

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

Collaborative Research on Academic History using Linked Open Data: A Proposal for the Heloise Common Research Model

International audienceThe paper presents a proposal for the Heloise Common Research Model (HCRM), to be implemented for the European research network on digital academic history – Heloise. The objective of Heloise is to interlink databases and other digital resources stemming from several research projects in the field of academic history, to provide an integrated database for federated research on the network databases. The HCRM defines three layers: the Repository Layer, the Application Layer and the Research Interface Layer, which are presented in detail. As part of the application and research interface layer, essential concepts are the symogih.org ontology and a Heloise network-specific thesaurus. The concepts have been tested on a sample of Heloise network’s datasets as a part of a prototype of the envisaged platform that the authors have started implementing. The paper concludes with future developments to be accomplished within the Heloise network

Effectiveness of Domain Ontologies to Facilitate Shared Understanding and Cross-Understanding

Shared cognition constructs such as shared understanding and cross-understanding are important factors in team performance. Although research has focused on understanding the effects of these constructs, little emphasis has been placed on improving their development. In Information Systems and related fields shared understanding of a domain is said to be facilitated by the use of a domain ontology, however there is a lack of empirical evidence to support this claim. Accordingly, in this research-in-progress paper, we report our efforts to develop a deep understanding of the benefits of domain ontology use at the group level. Specifically, we propose a model that theorizes the relationships between domain ontology use and the development of shared understanding and cross-understanding of domains. Additionally, we provide details of operationalization and empirical validation of our model, and the current state of this research

Combining data mining and ontology engineering to enrich ontologies and linked data

In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts

Special Issue on Smart Data and Semantics in a Sensor World

Introduction Since its first inception in 2001, the application of the Semantic Web [1, 2] has carried out an extensive use of ontologies [3–5], reasoning, and semantics in diverse fields, such as Information Integration, Software Engineering, Bioinformatics, eGovernment, eHealth, and social networks. This widespread use of ontologies has led to an incredible advance in the development of techniques to manipulate, share, reuse, and integrate information across heterogeneous data sources. In recent years, the growth of the IoT (Internet of Things) required to face the challenges of “Big Data” [6–10]. The cost of sensors is decreasing, while their use is expanding. Moreover, the use of multiple personal smart devices is an emerging trend and all of them can embed sensors to monitor the surrounding environment. Therefore, the number of available sensors is exploding. On the one hand, the flows of sensor data are massive and continuous, and the data could be obtained in real time or with a delay of just a few seconds. Then, the volume of sensor data is increasing continuously every day. On the other hand, the variety of data being generated is also increasing, due to plenty of different devices and different measures to record. There are many kinds of structured and unstructured sensor data in diverse formats. Moreover, data veracity, which is the degree of accuracy or truthfulness of a data set, is an important aspect to consider. In the context of sensor data, it represents the trustworthiness of the data source and the processing of data. The need for more accurate and reliable data was always declared, but often overlooked for the sake of larger and cheaper..

Employing Process Models for Surgical Training

The exponential rise in minimally invasive procedures throughout the last three decades shifted the focus from individual manual skills to complex engineering solutions. To streamline the delivery of these novel techniques, Surgical Process Models (SPMs) have been under development. SPMs provide the basis for machine learning algorithms to frame the surgical procedure and anchor themselves into the workflow. Process recording is an essential tool to create an accurate representation of the SPM. Process recording, continued with human expert evaluation have been used to assess operator skills and compare interventional approaches. In this paper, we present a web-based surgical process recording tool which is evaluated in a surgical training scenario. Our aim is to involve the trainees in process recording, therefore actively exploring the generic process model of laparoscopic cholecystectomy. Along with training we also use the process records to identify the most accurately represented time points of process transitions, therefore providing target events for future monitoring systems

Towards an Automated Semantic Data-driven Decision Making Employing Human Brain

[EN] Decision making is time-consuming and costly, as it requires direct intensive involvement of the human brain. The variety of expertise of highly qualified experts is very high, and the available experts are mostly not available on a short notice: they might be physically remotely located, and/or not being able to address all the problems they could address time-wise. Further, people tend to base more of their intellectual labour on rapidly increasing volumes of online data, content and computing resources, and the lack of corresponding scaling in availability of the human brain resources poses a bottleneck in the intellectual labour. We discuss enabling direct interoperability between the Internet and the human brain, developing "Internet of Brains", similar to "Internet of Things", where one can semantically model, interoperate and control real life objects. The Web, "Internet of Things" and "Internet of Brains" will be connected employing the same kind of semantic structures, and work in interoperation. Applying Brain Computer Interfaces (BCIs), psychology and behavioural science, we discuss the feasibility of a possible decion making infrastructure for semantic transfer of human thoughts, thinking processes, communication directly to the InternetThis work has been partially funded by project DALICC, supported by the Austrian Research Promotion Agency (FFG) within the program “Future ICT”.Fensel, A. (2018). Towards an Automated Semantic Data-driven Decision Making Employing Human Brain. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 167-175. https://doi.org/10.4995/CARMA2018.2018.8338OCS16717