Semantic networks for engineering design: State of the art and future directions

This is the author accepted manuscript. The final version is available from the American Society of Mechanical Engineers via the DOI in this recordIn the past two decades, there has been increasing use of semantic networks in engineering design for supporting various activities, such as knowledge extraction, prior art search, idea generation and evaluation. Leveraging large-scale pre-trained graph knowledge databases to support engineering design-related natural language processing (NLP) tasks has attracted a growing interest in the engineering design research community. Therefore, this paper aims to provide a survey of the state-of-the-art semantic networks for engineering design and propositions of future research to build and utilize large-scale semantic networks as knowledge bases to support engineering design research and practice. The survey shows that WordNet, ConceptNet and other semantic networks, which contain common-sense knowledge or are trained on non-engineering data sources, are primarily used by engineering design researchers to develop methods and tools. Meanwhile, there are emerging efforts in constructing engineering and technical-contextualized semantic network databases, such as B-Link and TechNet, through retrieving data from technical data sources and employing unsupervised machine learning approaches. On this basis, we recommend six strategic future research directions to advance the development and uses of large-scale semantic networks for artificial intelligence applications in engineering design

Searching COVID-19 Clinical Research Using Graph Queries: Algorithm Development and Validation

Background: Since the beginning of the COVID-19 pandemic, >1 million studies have been collected within the COVID-19 Open Research Dataset, a corpus of manuscripts created to accelerate research against the disease. Their related abstracts hold a wealth of information that remains largely unexplored and difficult to search due to its unstructured nature. Keyword-based search is the standard approach, which allows users to retrieve the documents of a corpus that contain (all or some of) the words in a target list. This type of search, however, does not provide visual support to the task and is not suited to expressing complex queries or compensating for missing specifications. Objective: This study aims to consider small graphs of concepts and exploit them for expressing graph searches over existing COVID-19-related literature, leveraging the increasing use of graphs to represent and query scientific knowledge and providing a user-friendly search and exploration experience. Methods: We considered the COVID-19 Open Research Dataset corpus and summarized its content by annotating the publications' abstracts using terms selected from the Unified Medical Language System and the Ontology of Coronavirus Infectious Disease. Then, we built a co-occurrence network that includes all relevant concepts mentioned in the corpus, establishing connections when their mutual information is relevant. A sophisticated graph query engine was built to allow the identification of the best matches of graph queries on the network. It also supports partial matches and suggests potential query completions using shortest paths. Results: We built a large co-occurrence network, consisting of 128,249 entities and 47,198,965 relationships; the GRAPH-SEARCH interface allows users to explore the network by formulating or adapting graph queries; it produces a bibliography of publications, which are globally ranked; and each publication is further associated with the specific parts of the query that it explains, thereby allowing the user to understand each aspect of the matching. Conclusions: Our approach supports the process of query formulation and evidence search upon a large text corpus; it can be reapplied to any scientific domain where documents corpora and curated ontologies are made available

Conceptualization of Computational Modeling Approaches and Interpretation of the Role of Neuroimaging Indices in Pathomechanisms for Pre-Clinical Detection of Alzheimer Disease

With swift advancements in next-generation sequencing technologies alongside the voluminous growth of biological data, a diversity of various data resources such as databases and web services have been created to facilitate data management, accessibility, and analysis. However, the burden of interoperability between dynamically growing data resources is an increasingly rate-limiting step in biomedicine, specifically concerning neurodegeneration. Over the years, massive investments and technological advancements for dementia research have resulted in large proportions of unmined data. Accordingly, there is an essential need for intelligent as well as integrative approaches to mine available data and substantiate novel research outcomes. Semantic frameworks provide a unique possibility to integrate multiple heterogeneous, high-resolution data resources with semantic integrity using standardized ontologies and vocabularies for context- specific domains. In this current work, (i) the functionality of a semantically structured terminology for mining pathway relevant knowledge from the literature, called Pathway Terminology System, is demonstrated and (ii) a context-specific high granularity semantic framework for neurodegenerative diseases, known as NeuroRDF, is presented. Neurodegenerative disorders are especially complex as they are characterized by widespread manifestations and the potential for dramatic alterations in disease progression over time. Early detection and prediction strategies through clinical pointers can provide promising solutions for effective treatment of AD. In the current work, we have presented the importance of bridging the gap between clinical and molecular biomarkers to effectively contribute to dementia research. Moreover, we address the need for a formalized framework called NIFT to automatically mine relevant clinical knowledge from the literature for substantiating high-resolution cause-and-effect models

Evaluating Biological Systems for Their Potential in Engineering Design

A team of biologists, engineers, and cognitive scientists has been working together for the past five years, teaching an upper level undergraduate course in biologically inspired design where half the class of forty students are biologists and other physical scientists and the other half are engineers (mechanical, materials, industrial, others). From this experience, we provide insights on how to teach students to evaluate biological systems for their potential in engineering design. We have found that at first, students are not familiar with developing their own question since, in most engineering design classes, the problem is prescribed along with clients who would like to have them solved. In our class, we challenge the students with defining a significant problem. The students with common challenges then are placed together in an interdisciplinary team with at least one biologist and one engineer. A detailed problem decomposition follows, identifying the hierarchy of systems and clearly specifying functions. This is essential for the next step of analogical reasoning. Analogical reasoning as applied to BID is a process of matching biological functions to engineered functions and transferring functions and mechanisms from biology to engineering. For each desired function, students may ask: what mechanisms does nature use for achieving the function? This question guides the exploration of the wealth of knowledge in biology by asking them to clearly define the function of interest, then search for natural processes that perform this function. To expand on this search space, the students next make a list of the same function performed by other organisms for a comparative analysis to deepen their understanding and extract key biological principles. Students then invert the function and identify keywords to search. They also must refer to general biology books to identify key organisms that perform the function the best (and hence are included in textbooks). Using databases, such as the Web of Science functions, they can try to select the ‘best’ articles. If one is lucky, a single biological system may serve as a near perfect match to lead to a successful BID. However, some of the most innovative designs are built from more than one biological system, something that evolution cannot always do. We call these compound analogies. At this point, the design iteration can take on a different approach, namely solution based rather than problem based. Here, the team takes a natural system and asks, how can this biological principle improve an engineered design or function. These twin processes: solution vs problem-based approaches both have led to innovative and creative design concepts in this interdisciplinary class. Key words: Biological systems; engineering design; interdisciplinary clas

Opportunity Identification for New Product Planning: Ontological Semantic Patent Classification

Intelligence tools have been developed and applied widely in many different areas in engineering, business and management. Many commercialized tools for business intelligence are available in the market. However, no practically useful tools for technology intelligence are available at this time, and very little academic research in technology intelligence methods has been conducted to date. Patent databases are the most important data source for technology intelligence tools, but patents inherently contain unstructured data. Consequently, extracting text data from patent databases, converting that data to meaningful information and generating useful knowledge from this information become complex tasks. These tasks are currently being performed very ineffectively, inefficiently and unreliably by human experts. This deficiency is particularly vexing in product planning, where awareness of market needs and technological capabilities is critical for identifying opportunities for new products and services. Total nescience of the text of patents, as well as inadequate, unreliable and untimely knowledge derived from these patents, may consequently result in missed opportunities that could lead to severe competitive disadvantage and potentially catastrophic loss of revenue. The research performed in this dissertation tries to correct the abovementioned deficiency with an approach called patent mining. The research is conducted at Finex, an iron casting company that produces traditional kitchen skillets. To \u27mine\u27 pertinent patents, experts in new product development at Finex modeled one ontology for the required product features and another for the attributes of requisite metallurgical enabling technologies from which new product opportunities for skillets are identified by applying natural language processing, information retrieval, and machine learning (classification) to the text of patents in the USPTO database. Three main scenarios are examined in my research. Regular classification (RC) relies on keywords that are extracted directly from a group of USPTO patents. Ontological classification (OC) relies on keywords that result from an ontology developed by Finex experts, which is evaluated and improved by a panel of external experts. Ontological semantic classification (OSC) uses these ontological keywords and their synonyms, which are extracted from the WordNet database. For each scenario, I evaluate the performance of three classifiers: k-Nearest Neighbor (k-NN), random forest, and Support Vector Machine (SVM). My research shows that OSC is the best scenario and SVM is the best classifier for identifying product planning opportunities, because this combination yields the highest score in metrics that are generally used to measure classification performance in machine learning (e.g., ROC-AUC and F-score). My method also significantly outperforms current practice, because I demonstrate in an experiment that neither the experts at Finex nor the panel of external experts are able to search for and judge relevant patents with any degree of effectiveness, efficiency or reliability. This dissertation provides the rudiments of a theoretical foundation for patent mining, which has yielded a machine learning method that is deployed successfully in a new product planning setting (Finex). Further development of this method could make a significant contribution to management practice by identifying opportunities for new product development that have been missed by the approaches that have been deployed to date

INTEROPERABLE MODEL FOR BIORESOURCE DISTRIBUTED DATABASES

Recently, numerous frameworks and tools are being developed for enhancing access to data and services with a standardized view to communicate the advances in open information sharing. Another emerging field of data exploration is encountered in the coordination, examination and perception of bioresource data and are prompting corresponding new innovations. The bioresource information team aims to develop standards for nationwide data exchange by the establishment of a catalog service to locate and access biological data and information from across the country and information tool for decision makers. With the growth of open data sharing initiatives, the sharing of data among different and myriad sources has increased significantly, but major challenge lies in addressing the issues of interoperability during exchange and use since the data sources are heterogeneous and the data being organization specific is prepared with different (organization) specific data standards and platforms. This paper presents the model based on the study of different metadata standards and to develop a recommended standard for biodiversity information to support interoperability among heterogeneous databases under the umbrella of Indian Bioresource Information Network (IBIN) portal. The paper presents the mapping of different data standards into the IBIN standard for sharing species data in the form of distributed and interoperable web services to set the stage for interoperability

A systematic review of natural language processing applied to radiology reports

NLP has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses recent literature in NLP applied to radiology reports. Our automated literature search yields 4,799 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics. We present a comprehensive analysis of the 164 publications retrieved with each categorised into one of 6 clinical application categories. Deep learning use increases but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results. Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process but reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication

Mapping Nanomedicine Terminology in the Regulatory Landscape

A common terminology is essential in any field of science and technology for a mutual understanding among different communities of experts and regulators, harmonisation of policy actions, standardisation of quality procedures and experimental testing, and the communication to the general public. It also allows effective revision of information for policy making and optimises research fund allocation. In particular, in emerging scientific fields with a high innovation potential, new terms, descriptions and definitions are quickly generated, which are then ambiguously used by stakeholders having diverse interests, coming from different scientific disciplines and/or from various regions. The application of nanotechnology in health -often called nanomedicine- is considered as such emerging and multidisciplinary field with a growing interest of various communities. In order to support a better understanding of terms used in the regulatory domain, the Nanomedicines Working Group of the International Pharmaceutical Regulators Forum (IPRF) has prioritised the need to map, compile and discuss the currently used terminology of regulatory scientists coming from different geographic areas. The JRC has taken the lead to identify and compile frequently used terms in the field by using web crawling and text mining tools as well as the manual extraction of terms. Websites of 13 regulatory authorities and clinical trial registries globally involved in regulating nanomedicines have been crawled. The compilation and analysis of extracted terms demonstrated sectorial and geographical differences in the frequency and type of nanomedicine related terms used in a regulatory context. Finally 31 relevant and most frequently used terms deriving from various agencies have been compiled, discussed and analysed for their similarities and differences. These descriptions will support the development of harmonised use of terminology in the future. The report provides necessary background information to advance the discussion among stakeholders. It will strengthen activities aiming to develop harmonised standards in the field of nanomedicine, which is an essential factor to stimulate innovation and industrial competitiveness.JRC.F.2-Consumer Products Safet