Using latent semantic indexing for literature based discovery

Latent semantic indexing (LSI) is a statistical technique for improving information retrieval effectiveness. Here, we use LSI to assist in literature-based discoveries. The idea behind literature-based discoveries is that different authors have already published certain underlying scientific ideas that, when taken together, can be connected to hypothesize a new discovery, and that these connections can be made by exploring the scientific literature. We explore latent semantic indexing's effectiveness on two discovery processes: uncovering “nearby” relationships that are necessary to initiate the literature based discovery process; and discovering more distant relationships that may genuinely generate new discovery hypotheses. © 1998 John Wiley & Sons, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34255/1/2_ftp.pd

Quantum Information Dynamics and Open World Science

One of the fundamental insights of quantum mechanics is that complete knowledge of the state of a quantum system is not possible. Such incomplete knowledge of a physical system is the norm rather than the exception. This is becoming increasingly apparent as we apply scientific methods to increasingly complex situations. Empirically intensive disciplines in the biological, human, and geosciences all operate in situations where valid conclusions must be drawn, but deductive completeness is impossible. This paper argues that such situations are emerging examples of {it Open World} Science. In this paradigm, scientific models are known to be acting with incomplete information. Open World models acknowledge their incompleteness, and respond positively when new information becomes available. Many methods for creating Open World models have been explored analytically in quantitative disciplines such as statistics, and the increasingly mature area of machine learning. This paper examines the role of quantum theory and quantum logic in the underpinnings of Open World models, examining the importance of structural features of such as non-commutativity, degrees of similarity, induction, and the impact of observation. Quantum mechanics is not a problem around the edges of classical theory, but is rather a secure bridgehead in the world of science to come

KNOWLEDGE DISCOVERY FOR IDENTIFICATION OF ENZYME WITH A PRIORY SPECIFIED PROPERTIES

Development of new products with the given properties from the known raw materials is one of the common research tasks in process engineering. Usually the first step of research is a literature survey based on the search for the specific keywords. Nowadays there exist many vast databases of articles and patents, and the traditional, keywords-based, searching tools are not always sufficient to find the desired information. The main objective of this paper is to develop methodology for identification of new materials, based on knowledge discovery. As an example, the proposed methodology is applied for identification of new enzyme of microbial origin capable of polymerizing lactose in aqueous solution, with the number of required criteria

Identifying Moderator Variables Through Requirements Elicitation Experiments Limitations

Interviews are the most widely used elicitation technique in Requirements Engineering (RE). Despite its importance, research in interviews is quite limited, in particular from an experimental perspective. We have performed a series of experiments exploring the relative effectiveness of structured and unstructured interviews. This line of research has been active in Information Systems in the past years, so that our experiments can be aggregated together with existing ones to obtain guidelines for practice. Experimental aggregation is a demanding task. It requires not only a large number of experiments, but also considering the influence of the existing moderators. However, in the current state of the practice in RE, those moderators are unknown. We believe that analyzing the threats to validity in interviewing experiments may give insight about how to improve further replications and the corresponding aggregations. It is likely that this strategy may be applied in other Software Engineering areas as well

Powerful tool to expand business intelligence: Text mining

With the extensive inclusion of document, especially text, in the business systems, data mining does not cover the full scope of Business Intelligence. Data mining cannot deliver its impact on extracting useful details from the large collection of unstructured and semi-structured written materials based on natural languages. The most pressing issue is to draw the potential business intelligence from text. In order to gain competitive advantages for the business, it is necessary to develop the new powerful tool, text mining, to expand the scope of business intelligence. In this paper, we will work out the strong points of text mining in extracting business intelligence from huge amount of textual information sources within business systems. We will apply text mining to each stage of Business Intelligence systems to prove that text mining is the powerful tool to expand the scope of BI. After reviewing basic definitions and some related technologies, we will discuss the relationship and the benefits of these to text mining. Some examples and applications of text mining will also be given. The motivation behind is to develop new approach to effective and efficient textual information analysis. Thus we can expand the scope of Business Intelligence using the powerful tool, text mining

The synthesis: An innovative approach to student research

Background: Although there is an expectation for health practitioners to be able to use research to inform their practice, opportunities for students to actually plan, conduct and report a research project are diminishing. Students are less able to gain first-hand experience of research as a result of demands to include more in curricula, and increasingly rigorous and time-consuming ethical review procedures. It is important, therefore, for health educators to explore different research methods and approaches. Content: This article proposes the synthesis, an entirely literature-based approach, as a method to enable students to plan and conduct a research project. It is more than a literature review in that it requires students to synthesize material from two previously unrelated fields. As a result it is possible to shine a new light on issues facing health-care professionals and their patients/clients, by bringing new ways of thinking to issues. Conclusions: Although the synthesis is a nascent concept in student health professional education, it has the potential to offer students first-hand experience of the research process, and so contribute to their development as research aware professionals.Full Tex

AI for Open Science: A Multi-Agent Perspective for Ethically Translating Data to Knowledge

AI for Science (AI4Science), particularly in the form of self-driving labs, has the potential to sideline human involvement and hinder scientific discovery within the broader community. While prior research has focused on ensuring the responsible deployment of AI applications, enhancing security, and ensuring interpretability, we also propose that promoting openness in AI4Science discoveries should be carefully considered. In this paper, we introduce the concept of AI for Open Science (AI4OS) as a multi-agent extension of AI4Science with the core principle of maximizing open knowledge translation throughout the scientific enterprise rather than a single organizational unit. We use the established principles of Knowledge Discovery and Data Mining (KDD) to formalize a language around AI4OS. We then discuss three principle stages of knowledge translation embedded in AI4Science systems and detail specific points where openness can be applied to yield an AI4OS alternative. Lastly, we formulate a theoretical metric to assess AI4OS with a supporting ethical argument highlighting its importance. Our goal is that by drawing attention to AI4OS we can ensure the natural consequence of AI4Science (e.g., self-driving labs) is a benefit not only for its developers but for society as a whole.Comment: NeurIPS AI For Science Workshop 2023. 11 pages, 2 figure

EpiphaNet: An Interactive Tool to Support Biomedical Discoveries

Background. EpiphaNet (http://epiphanet.uth.tmc.edu) is an interactive knowledge discovery system, which enables researchers to explore visually sets of relations extracted from MEDLINE using a combination of language processing techniques. In this paper, we discuss the theoretical and methodological foundations of the system, and evaluate the utility of the models that underlie it for literature‐based discovery. In addition, we present a summary of results drawn from a qualitative analysis of over six hours of interaction with the system by basic medical scientists. Results: The system is able to simulate open and closed discovery, and is shown to generate associations that are both surprising and interesting within the area of expertise of the researchers concerned. Conclusions: EpiphaNet provides an interactive visual representation of associations between concepts, which is derived from distributional statistics drawn from across the spectrum of biomedical citations in MEDLINE. This tool is available online, providing biomedical scientists with the opportunity to identify and explore associations of interest to them

Literature based discovery: Techniques and tools

Literature Based Discovery (LBD) was initially proposed by Don R. Swanson in 1980 as a method to establish relationships between disease and remedy from disjoint science literature. Consequently, he established a link between magnesium and migraines. Since then literature based discovery has been a subject of research and development for discovery in online medical publications. It has further been investigated in both chemistry and mathematics; In this thesis, we give an overview of LBD and the software tools necessary to automate this technique. We further provide an implementation of this technique that is intended to be used for computer science subject matter