Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding how these stakeholders collaborate will enable us to improve editing environments that support such collaborations. We uncover how large ontology-engineering projects, such as the ICD in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users subsequently change) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.Comment: Published in the Journal of Biomedical Informatic

WebProt\'eg\'e: A Cloud-Based Ontology Editor

We present WebProt\'eg\'e, a tool to develop ontologies represented in the Web Ontology Language (OWL). WebProt\'eg\'e is a cloud-based application that allows users to collaboratively edit OWL ontologies, and it is available for use at https://webprotege.stanford.edu. WebProt\'ege\'e currently hosts more than 68,000 OWL ontology projects and has over 50,000 user accounts. In this paper, we detail the main new features of the latest version of WebProt\'eg\'e

Flames recognition for opinion mining

The emerging world-wide e-society creates new ways of interaction between people with different cultures and backgrounds. Communication systems as forums, blogs, and comments are easily accessible to end users. In this context, user generated content management revealed to be a difficult but necessary task. Studying and interpreting user generated data/text available on the Internet is a complex and time consuming task for any human analyst. This study proposes an interdisciplinary approach to modelling the flaming phenomena (hot, aggressive discussions) in online Italian forums. The model is based on the analysis of psycho/cognitive/linguistic interaction modalities among web communities' participants, state-of-the art machine learning techniques and natural language processing technology. Virtual communities' administrators, moderators and users could benefit directly from this research. A further positive outcome of this research is the opportunity to better understand and model the dynamics of web forums as the base for developing opinion mining applications focused on commercial applications

Preliminary study of kaonic deuterium X-rays by the SIDDHARTA experiment at DAFNE

The study of the KbarN system at very low energies plays a key role for the understanding of the strong interaction between hadrons in the strangeness sector. At the DAFNE electron-positron collider of Laboratori Nazionali di Frascati we studied kaonic atoms with Z=1 and Z=2, taking advantage of the low-energy charged kaons from Phi-mesons decaying nearly at rest. The SIDDHARTA experiment used X-ray spectroscopy of the kaonic atoms to determine the transition yields and the strong interaction induced shift and width of the lowest experimentally accessible level (1s for H and D and 2p for He). Shift and width are connected to the real and imaginary part of the scattering length. To disentangle the isospin dependent scattering lengths of the antikaon-nucleon interaction, measurements of Kp and of Kd are needed. We report here on an exploratory deuterium measurement, from which a limit for the yield of the K-series transitions was derived: Y(K_tot)<0.0143 and Y(K_alpha)<0.0039 (CL 90%). Also, the upcoming SIDDHARTA-2 kaonic deuterium experiment is introduced.Comment: Accepted by Nuclear Physics

X-ray transition yields of low-Z kaonic atoms produced in Kapton

The X-ray transition yields of kaonic atoms produced in Kapton polyimide (C22H10N2O5) were measured for the first time in the SIDDHARTA experiment. X-ray yields of the kaonic atoms with low atomic numbers (Z = 6, 7, and 8) and transitions with high principal quantum numbers (n = 5-8) were determined. The relative yield ratios of the successive transitions and those of carbon-to-nitrogen (C:N) and carbon-to-oxygen (C:O) were also determined. These X-ray yields provide important information for understanding the capture ratios and cascade mechanisms of kaonic atoms produced in a compound material, such as Kapton.Comment: Accepted in Nucl. Phys. A (2013

Enabling Web-scale data integration in biomedicine through Linked Open Data

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems

First measurement of kaonic helium-3 X-rays

The first observation of the kaonic 3He 3d - 2p transition was made using slow K- mesons stopped in a gaseous 3He target. The kaonic atom X-rays were detected with large-area silicon drift detectors using the timing information of the K+K- pairs of phi-meson decays produced by the DAFNE e+e- collider. The strong interaction shift of the kaonic 3He 2p state was determined to be -2+-2 (stat)+-4 (syst) eV.Comment: Accepted for publication in Phys. Lett.

A More Decentralized Vision for Linked Data

We claim that ten years into Linked Data there are still many unresolved challenges towards arriving at a truly machine-readable and decentralized Web of data. With a focus on the the biomedical domain, currently, one of the most promising adopters of Linked Data, we highlight and exemplify key technical and non-technical challenges to the success of Linked Data, and we outline potential solution strategies

A More Decentralized Vision for Linked Data

In this deliberately provocative position paper, we claim that ten years into Linked Data there are still (too?) many unresolved challenges towards arriving at a truly machine-readable and decentralized Web of data. We take a deeper look at the biomedical domain - currently, one of the most promising "adopters" of Linked Data - if we believe the ever-present "LOD cloud" diagram. Herein, we try to highlight and exemplify key technical and non-technical challenges to the success of LOD, and we outline potential solution strategies. We hope that this paper will serve as a discussion basis for a fresh start towards more actionable, truly decentralized Linked Data, and as a call to the community to join forces.Series: Working Papers on Information Systems, Information Business and Operation