In the last two decades, data regarding engineering design and product development has increased rapidly. Big data exploration and mining offer numerous opportunities for engineering design; however, owing to the multitude of data sources and formats coupled with the high complexity of the design process, these techniques are yet to be utilised to the best of their full potential. In this study, a comprehensive assessment of the state-of-the-art data-driven engineering design (DDED) in the last 20 years was conducted. A scientometric approach was employed wherein first, a systematic article acquisition procedure was performed, where a dataset of 3339 articles related to engineering design and big data analytics applications were extracted from Web of Science (WoS) and Scopus. Thereafter, this dataset was reduced to a dataset of 366 articles based on concise data screening. The resulting articles were used to analyse the dynamics of research in DDED throughout the last 20 years and to detect the primary research topics related to DDED, the most influential authors, and the papers with the highest impact in the DDED domain. Furthermore, the co-occurrence network of keywords/keyphrases and co-authorship networks were constructed and analysed to reveal the interconnection of the research topics and the collaboration between the most prolific authors. Finally, an insight how big data analytics is being applied through product development activities to support decision-making in engineering design was presented.

In this preliminary work we propose an approach to tracking network communities in time. We describe a methodology to study the dynamics and evolution of the MEDLINE bibliographic database using network-based analysis. We explore how the temporal characteristics of the network can be used to provide insight into the historical evolution of the broad field of biomedicine.

In this work we propose a novel approach for semantics-based cross-domain recommendation for research collaboration. First, we construct a large network representing authors, their expertize, current collaborations, and biomedical knowledge in general. We constructed the network from the bibliographic database MEDLINE and from semantic relations extracted from MEDLINE with the SemRep natural language processing system. Then, by using the literature-based discovery paradigm, we recommend novel collaborations, which include not only pairs of authors, but also novel topics for collaboration and an explanation why the collaboration makes sense.

BACKGROUND: The proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved. Classical IR systems usually return a list of documents that have to be read by the user to extract relevant information. This tedious and time-consuming work can be lessened with automatic Question Answering (QA) systems, which aim to provide users with direct and precise answers to their questions. In this work we propose a novel methodology for QA based on semantic relations extracted from the biomedical literature. RESULTS: We extracted semantic relations with the SemRep natural language processing system from 122,421,765 sentences, which came from 21,014,382 MEDLINE citations (i.e., the complete MEDLINE distribution up to the end of 2012). A total of 58,879,300 semantic relation instances were extracted and organized in a relational database. The QA process is implemented as a search in this database, which is accessed through a Web-based application, called SemBT (available at http://sembt.mf.uni-lj.si ). We conducted an extensive evaluation of the proposed methodology in order to estimate the accuracy of extracting a particular semantic relation from a particular sentence. Evaluation was performed by 80 domain experts. In total 7,510 semantic relation instances belonging to 2,675 distinct relations were evaluated 12,083 times. The instances were evaluated as correct 8,228 times (68%). CONCLUSIONS: In this work we propose an innovative methodology for biomedical QA. The system is implemented as a Web-based application that is able to provide precise answers to a wide range of questions. A typical question is answered within a few seconds. The tool has some extensions that make it especially useful for interpretation of DNA microarray results.