Concept discovery innovations in law enforcement: a perspective.

In the past decades, the amount of information available to law enforcement agencies has increased significantly. Most of this information is in textual form, however analyses have mainly focused on the structured data. In this paper, we give an overview of the concept discovery projects at the Amsterdam-Amstelland police where Formal Concept Analysis (FCA) is being used as text mining instrument. FCA is combined with statistical techniques such as Hidden Markov Models (HMM) and Emergent Self Organizing Maps (ESOM). The combination of this concept discovery and refinement technique with statistical techniques for analyzing high-dimensional data not only resulted in new insights but often in actual improvements of the investigation procedures.Formal concept analysis; Intelligence led policing; Knowledge discovery;

State of the art document clustering algorithms based on semantic similarity

The constant success of the Internet made the number of text documents in electronic forms increases hugely. The techniques to group these documents into meaningful clusters are becoming critical missions. The traditional clustering method was based on statistical features, and the clustering was done using a syntactic notion rather than semantically. However, these techniques resulted in un-similar data gathered in the same group due to polysemy and synonymy problems. The important solution to this issue is to document clustering based on semantic similarity, in which the documents are grouped according to the meaning and not keywords. In this research, eighty papers that use semantic similarity in different fields have been reviewed; forty of them that are using semantic similarity based on document clustering in seven recent years have been selected for a deep study, published between the years 2014 to 2020. A comprehensive literature review for all the selected papers is stated. Detailed research and comparison regarding their clustering algorithms, utilized tools, and methods of evaluation are given. This helps in the implementation and evaluation of the clustering of documents. The exposed research is used in the same direction when preparing the proposed research. Finally, an intensive discussion comparing the works is presented, and the result of our research is shown in figures

Molecular causality in the advent of foundation models

Correlation is not causation. As simple as this widely agreed-upon statement may seem, scientifically defining causality and using it to drive our modern biomedical research is immensely challenging. In this perspective, we attempt to synergise the partly disparate fields of systems biology, causal reasoning, and machine learning, to inform future approaches in the field of systems biology and molecular networks.Comment: 22 pages, 0 figures, 87 references; submitted to MS

Quantitative Perspectives on Fifty Years of the Journal of the History of Biology

Journal of the History of Biology provides a fifty-year long record for examining the evolution of the history of biology as a scholarly discipline. In this paper, we present a new dataset and preliminary quantitative analysis of the thematic content of JHB from the perspectives of geography, organisms, and thematic fields. The geographic diversity of authors whose work appears in JHB has increased steadily since 1968, but the geographic coverage of the content of JHB articles remains strongly lopsided toward the United States, United Kingdom, and western Europe and has diversified much less dramatically over time. The taxonomic diversity of organisms discussed in JHB increased steadily between 1968 and the late 1990s but declined in later years, mirroring broader patterns of diversification previously reported in the biomedical research literature. Finally, we used a combination of topic modeling and nonlinear dimensionality reduction techniques to develop a model of multi-article fields within JHB. We found evidence for directional changes in the representation of fields on multiple scales. The diversity of JHB with regard to the representation of thematic fields has increased overall, with most of that diversification occurring in recent years. Drawing on the dataset generated in the course of this analysis, as well as web services in the emerging digital history and philosophy of science ecosystem, we have developed an interactive web platform for exploring the content of JHB, and we provide a brief overview of the platform in this article. As a whole, the data and analyses presented here provide a starting-place for further critical reflection on the evolution of the history of biology over the past half-century.Comment: 45 pages, 14 figures, 4 table

Emergence of nano S&T in Germany : network formation and company performance

This article investigates the emergence of nano S&T in Germany. Using multiple longitudinal data sets, we describe the complete set of research institutions and companies that entered this science-based technology field and the development of their inter-organisational networks between 1991 and 2000. We demonstrate that the co-publication network is a core-periphery structure in which some companies were key players at an early stage of field formation, whereas later universities and other extra-university institutes took over as the central drivers of scientific progress. Further differentiating among types of firms and research organisations, we find that in the co-patent network collaboration is most intense between high-technology firms and use-inspired basic research institutes. While many companies co-patent with several universities or other public institutes, some succeed in establishing almost exclusive relationships with public research units. It is shown that co-patent and co-publication ties are most effective at strengthening the technological performance of firms, that multiple interaction channels increase company performance, and that companies benefit from collaborating with scientifically central universities and institutes. --nanotechnology,network analysis,company performance,public research sector,innovation system,science industry cooperation,Germany

Normalization of Disease Mentions with Convolutional Neural Networks

Normalization of disease mentions has an important role in biomedical natural language processing (BioNLP) applications, such as the construction of biomedical databases. Various disease mention normalization systems have been developed, though state-of-the-art systems either rely on candidate concept generation, or do not generalize to new concepts not seen during training. This thesis explores the possibility of building a disease mention normalization system that both generalizes to unseen concepts and does not rely on candidate generation. To this end, it is hypothesized that modern neural networks are sophisticated enough to solve this problem. This hypothesis is tested by building a normalization system using deep learning approaches, and evaluating the accuracy of this system on the NCBI disease corpus. The system leverages semantic information in the biomedical literature by using continuous vector space representations for strings of disease mentions and concepts. A neural encoder is trained to encode vector representations of strings of disease mentions and concepts. This encoder theoretically enables the model to generalize to unseen concepts during training. The encoded strings are used to compare the similarity between concepts and a given mention. Viewing normalization as a ranking problem, the concept with the highest similarity estimated is selected as the predicted concept for the mention. For the development of the system, synthetic data is used for pre-training to facilitate the learning of the model. In addition, various architectures are explored. While the model succeeds in prediction without candidate concept generation, its performance is not comparable to those of the state-of-the-art systems. Normalization of disease mentions without candidate generation while including the possibility for the system to generalize to unseen concepts is not trivial. Further efforts can be focused on, for example, testing more neural architectures, and the use of more sophisticated word representations