Cybersecurity has emerged as a critical concern for modern enterprises due to the increasing complexity and diversity of threats. These risks exploit multiple attack vectors, such as phishing, unpatched vulnerabilities, and malware distribution, necessitating a comprehensive and unified approach to threat modeling. However, cybersecurity data is often siloed across disparate sources–ranging from JSON vulnerability reports (e.g., Amazon Inspector, CycloneDX) and dependency files (e.g., NPM) to relational databases and manual assessments–making integration a significant challenge. Knowledge Graphs offer the technological framework to successfully integrate disparate data. This work presents a KG-based solution for software vulnerability data integration at Siemens Energy, leveraging Enterprise Knowledge Graphs to unify heterogeneous datasets under a shared semantic model. Our approach consists of: (1) a Cybersecurity Ontology Network defining core entities and relationships, (2) an automated pipeline converting diverse data sources into a (3) scalable EKG that enables advanced threat analysis, and (4) competency questions and data quality rules validating the system’s effectiveness. By adopting a Data-Centric Architecture, EKGs provide a flexible, future-proof framework for cybersecurity intelligence, overcoming the limitations of traditional Application-Centric systems, and ultimately providing FAIR data (Findable, Accessible, Interoperable, Reusable). This work offers actionable insights for organizations seeking to enhance cyber threat visibility while managing complex, evolving data landscapes.

Scientific conferences are one of the most common venues for researchers and others to present and exchange new findings.In recent years, "unconferences"-i.e., meetings that promote spontaneous discussions rather than predetermined presentations-have emerged as a more collaborative approach.Unconferences differ from conferences in key ways: while conferences have a predefined set of presenters together with an audience, unconferences promote more collaborative and spontaneous interactions across participants [1].A "hackathon" is a special kind of unconference in which people come together to state, discuss, and solve problems by means of collaborative brainstorming, modeling, design, coding, testing, and documenting [2].Despite the "hack" portion in the term, hackathons welcome not only software developers but anyone involved in creating solutions that can be later consumed or exposed via software.In addition to collaboration, hackathons promote community development around a subject used as the main hackathon topic.Such a topic can correspond to a knowledge domain (e.g., semantics or genomics) or be related to a particular organization (e.g., data and data services offered).There are hackathon-like events that are called by other names, such as "codefests."In this paper, we include such events under the umbrella term "hackathon."Hackathons are especially useful in bringing together interdisciplinary sets of domain experts and specialized computer scientists with various degrees of experience and skills to "hack" solutions related to scientific topics of mutual interest.While traditional conferences focus more on transferring knowledge, hackathons are more about collaboratively generating solutions.The interactions often lead to productive collaborations, professional development opportunities, and a network of resources.Well-run hackathons are very effective at building a community among participants.Hackathons provide an opportunity for researchers and developers to interact and brainstorm with other participants in an appropriate environment to accelerate collaborations on topics of mutual benefit.In addition, they can provide a unique opportunity to think through a problem, without the usual distractions.Therefore, hackathons can be very productive and result in a major impact on the targeted topic and/or community [3].However, organizing a successful large-scale hackathon takes significant time and effort; e.g., organizing committees for the National Bioscience Database Center (NBDC) [4]/Database Center for Life Science (DBCLS) [5] and the ELIXIR Europe BioHackathons start approximately 1 year in advance.We use the term BioHackathon to refer to those hackathons addressing problems in domains related to biomedical and life sciences.BioHackathons are recognized as having a

MOTIVATION: Although full-text articles are provided by the publishers in electronic formats, it remains a challenge to find related work beyond the title and abstract context. Identifying related articles based on their abstract is indeed a good starting point; this process is straightforward and does not consume as many resources as full-text based similarity would require. However, further analyses may require in-depth understanding of the full content. Two articles with highly related abstracts can be substantially different regarding the full content. How similarity differs when considering title-and-abstract versus full-text and which semantic similarity metric provides better results when dealing with full-text articles are the main issues addressed in this manuscript. METHODS: We have benchmarked three similarity metrics - BM25, PMRA, and Cosine, in order to determine which one performs best when using concept-based annotations on full-text documents. We also evaluated variations in similarity values based on title-and-abstract against those relying on full-text. Our test dataset comprises the Genomics track article collection from the 2005 Text Retrieval Conference. Initially, we used an entity recognition software to semantically annotate titles and abstracts as well as full-text with concepts defined in the Unified Medical Language System (UMLS®). For each article, we created a document profile, i.e., a set of identified concepts, term frequency, and inverse document frequency; we then applied various similarity metrics to those document profiles. We considered correlation, precision, recall, and F1 in order to determine which similarity metric performs best with concept-based annotations. For those full-text articles available in PubMed Central Open Access (PMC-OA), we also performed dispersion analyses in order to understand how similarity varies when considering full-text articles. RESULTS: We have found that the PubMed Related Articles similarity metric is the most suitable for full-text articles annotated with UMLS concepts. For similarity values above 0.8, all metrics exhibited an F1 around 0.2 and a recall around 0.1; BM25 showed the highest precision close to 1; in all cases the concept-based metrics performed better than the word-stem-based one. Our experiments show that similarity values vary when considering only title-and-abstract versus full-text similarity. Therefore, analyses based on full-text become useful when a given research requires going beyond title and abstract, particularly regarding connectivity across articles. AVAILABILITY: Visualization available at ljgarcia.github.io/semsim.benchmark/, data available at http://dx.doi.org/10.5281/zenodo.13323.

SUMMARY: BioJS is an open-source project whose main objective is the visualization of biological data in JavaScript. BioJS provides an easy-to-use consistent framework for bioinformatics application programmers. It follows a community-driven standard specification that includes a collection of components purposely designed to require a very simple configuration and installation. In addition to the programming framework, BioJS provides a centralized repository of components available for reutilization by the bioinformatics community. AVAILABILITY AND IMPLEMENTATION: http://code.google.com/p/biojs/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Dasty3 is a highly interactive and extensible Web-based framework. It provides a rich Application Programming Interface upon which it is possible to develop specialized clients capable of retrieving information from DAS sources as well as from data providers not using the DAS protocol. Dasty3 provides significant improvements on previous Web-based frameworks and is implemented using the 1.6 DAS specification. AVAILABILITY: Dasty3 is an open-source tool freely available at http://www.ebi.ac.uk/dasty/ under the terms of the GNU General public license. Source and documentation can be found at http://code.google.com/p/dasty/. CONTACT: [email protected].

BACKGROUND: Centralised resources such as GenBank and UniProt are perfect examples of the major international efforts that have been made to integrate and share biological information. However, additional data that adds value to these resources needs a simple and rapid route to public access. The Distributed Annotation System (DAS) provides an adequate environment to integrate genomic and proteomic information from multiple sources, making this information accessible to the community. DAS offers a way to distribute and access information but it does not provide domain experts with the mechanisms to participate in the curation process of the available biological entities and their annotations. RESULTS: We designed and developed a Collaborative Annotation System for proteins called DAS Writeback. DAS writeback is a protocol extension of DAS to provide the functionalities of adding, editing and deleting annotations. We implemented this new specification as extensions of both a DAS server and a DAS client. The architecture was designed with the involvement of the DAS community and it was improved after performing usability experiments emulating a real annotation task. CONCLUSIONS: We demonstrate that DAS Writeback is effective, usable and will provide the appropriate environment for the creation and evolution of community protein annotation.

SUMMARY: Dasty2 is a highly interactive web client integrating protein sequence annotations from currently more than 40 sources, using the distributed annotation system (DAS). AVAILABILITY: Dasty2 is an open source tool freely available under the terms of the Apache License 2.0, publicly available at http://www.ebi.ac.uk/dasty/.

BACKGROUND: Ontologies such as the Gene Ontology can enable the construction of complex queries over biological information in a conceptual way, however existing systems to do this are too technical. Within the biological domain there is an increasing need for software that facilitates the flexible retrieval of information. OntoDas aims to fulfil this need by allowing the definition of queries by selecting valid ontology terms. RESULTS: OntoDas is a web-based tool that uses information visualisation techniques to provide an intuitive, interactive environment for constructing ontology-based queries against the Gene Ontology Database. Both a comprehensive use case and the interface itself were designed in a participatory manner by working with biologists to ensure that the interface matches the way biologists work. OntoDas was further tested with a separate group of biologists and refined based on their suggestions. CONCLUSION: OntoDas provides a visual and intuitive means for constructing complex queries against the Gene Ontology. It was designed with the participation of biologists and compares favourably with similar tools. It is available at http://ontodas.nbn.ac.za.

BACKGROUND: Incorporation of ontologies into annotations has enabled 'semantic integration' of complex data, making explicit the knowledge within a certain field. One of the major bottlenecks in developing bio-ontologies is the lack of a unified methodology. Different methodologies have been proposed for different scenarios, but there is no agreed-upon standard methodology for building ontologies. The involvement of geographically distributed domain experts, the need for domain experts to lead the design process, the application of the ontologies and the life cycles of bio-ontologies are amongst the features not considered by previously proposed methodologies. RESULTS: Here, we present a methodology for developing ontologies within the biological domain. We describe our scenario, competency questions, results and milestones for each methodological stage. We introduce the use of concept maps during knowledge acquisition phases as a feasible transition between domain expert and knowledge engineer. CONCLUSION: The contributions of this paper are the thorough description of the steps we suggest when building an ontology, example use of concept maps, consideration of applicability to the development of lower-level ontologies and application to decentralised environments. We have found that within our scenario conceptual maps played an important role in the development process.

BACKGROUND: Computational methods for problem solving need to interleave information access and algorithm execution in a problem-specific workflow. The structures of these workflows are defined by a scaffold of syntactic, semantic and algebraic objects capable of representing them. Despite the proliferation of GUIs (Graphic User Interfaces) in bioinformatics, only some of them provide workflow capabilities; surprisingly, no meta-analysis of workflow operators and components in bioinformatics has been reported. RESULTS: We present a set of syntactic components and algebraic operators capable of representing analytical workflows in bioinformatics. Iteration, recursion, the use of conditional statements, and management of suspend/resume tasks have traditionally been implemented on an ad hoc basis and hard-coded; by having these operators properly defined it is possible to use and parameterize them as generic re-usable components. To illustrate how these operations can be orchestrated, we present GPIPE, a prototype graphic pipeline generator for PISE that allows the definition of a pipeline, parameterization of its component methods, and storage of metadata in XML formats. This implementation goes beyond the macro capacities currently in PISE. As the entire analysis protocol is defined in XML, a complete bioinformatic experiment (linked sets of methods, parameters and results) can be reproduced or shared among users. AVAILABILITY: http://if-web1.imb.uq.edu.au/Pise/5.a/gpipe.html (interactive), ftp://ftp.pasteur.fr/pub/GenSoft/unix/misc/Pise/ (download). CONCLUSION: From our meta-analysis we have identified syntactic structures and algebraic operators common to many workflows in bioinformatics. The workflow components and algebraic operators can be assimilated into re-usable software components. GPIPE, a prototype implementation of this framework, provides a GUI builder to facilitate the generation of workflows and integration of heterogeneous analytical tools.