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.

Ontologies play a crucial role in supporting data and knowledge management in industry by promoting data standardization, interoperability, and facilitating knowledge sharing. However, the growing number of ontologies available in the repositories has made it challenging for developers to select the appropriate ontology for ensuring data interoperability in specific domains. To address this, we propose a method based on artificial intelligence to evaluate how well an ontology covers a particular domain. The method begins by using a text corpus, which represents the domain knowledge. It first identifies noun phrases within the text, which are then matched with the classes in the ontology under evaluation. The alignment process uses a scoring function that combines a Levenshtein-based similarity metric together with FastText and Bidirectional Encoder Representations from Transformers (BERT). These models capture the contextual meaning of both the noun phrases and the ontology classes. The method was tested across four health engineering subdomains—genetics, food, medicine, and law—by selecting domain-specific ontologies and text corpora for each. The results demonstrated that our method effectively identified the most appropriate ontology for each domain. The incorporation of language models in the method enabled it to overcome the limitations of traditional approaches, which often depend on exact string matches. Our method has proven to be an effective tool for assessing how well ontologies cover specific domains, thereby supporting the identification and selection of the most suitable ontologies for intelligent engineering applications. • Method for supporting decisions of which ontology to use based on domain coverage. • Novel application of language models to measure domain coverage. • Satisfactory results in four health engineering subdomains: genetics, medicine, food and law.

Knowledge Graphs integrate data from multiple, heterogeneous sources, using ontologies to facilitate data interoperability. Ontology development is a resource-consuming task that requires the collaborative work of domain experts and ontology engineers. Therefore, companies invest considerable resources in order to generate and maintain Enterprise Knowledge Graphs and ontologies from large and complex datasets, most of which can be unfamiliar for ontology engineers. In this work, we study the use of Large Language Models to aid in the development of ontologies from datasets, ultimately increasing the automation of the generation of ontology-based Knowledge Graphs. As a result we have developed a structured workflow that leverages Large Language Models to enhance ontology engineering through data pre-processing, ontology planning, building, and entity improvement. Our method is also able to generate mappings and RDF data, but in this work we focus on the ontologies. The pipeline has been implemented in the OntoGenix tool. In this work we show the results of the application of OntoGenix to six datasets related to commercial activities. The findings indicate that the ontologies produced exhibit patterns of coherent modeling, and features that closely resemble those created by humans, although the most complex situations are better reflected by the ontologies developed by humans. • OntoGenix is an LLM-powered pipeline for automating ontology development. • Ontologies developed by OntoGenix are similar to human-generated ontologies for most quality metrics applied. • Ontologies developed by OntoGenix are limited for capturing complex modeling situations. • Our experiments on e-commerce datasets show that OntoGenix modeling is consistent.

Enterprise knowledge graphs combine data from multiple sources, and the structure and granularity of the resulting graphs can vary widely. The data in knowledge graphs is typically structured by schemas, mainly ontologies. Finding the alignments between content from different graphs allows to reduce redundancy, detect inconsistencies, and improve the use of the data. Recently, many graph alignment methods have been proposed, usually tested on general knowledge bases, so there is very little knowledge about the effectiveness of the methods in specific domains. Moreover, we are also interested in studying how these methods capture the semantic similarity between the schemas of the knowledge graphs. In this article, we study both aspects using 25 graph alignment methods on datasets related to e-commerce activities of organizations, such as product sales or customer satisfaction. We have developed a pipeline for generating and evaluating the results. The results show that the alignment methods can be used as a semantic similarity system between the ontology-dataset pairs that generate the knowledge graphs, that AttrE and BootEA are the most effective and robust methods across the different datasets, that the complexity of the structure of the ontology has a clear impact on the effectiveness of the methods, and that the results of the alignment experiments generate information about the similarity of the ontologies and reveal which parts of them should be better modeled to increase the performance of the methods. • Methodological framework for systematic analysis of entity alignment experiments. • Evaluation of 25 methods for supporting semantic similarity analysis. • AttrE and BootEA are the best performing methods based on standardized metrics. • The structure of the ontology influences the results of the graph alignment methods. • Not all class attributes or relations have equal influence on the alignment results.

The effect of mutations has been traditionally predicted by studying what may happen due to the substitution of one amino acid for another one. This approach may be effective for mutations with impact in the function of the protein, but ineffective for mutations in the translation initiation codon. Such mutation might avoid the generation of the protein. Consequently, specific methods for predicting the effect of mutations in the translation initiation codon are needed. We propose a method for predicting the effect of mutations in the canonical translation initiation codon based on a biological model that considers specific features of such mutations, like the distance to a potential alternative initiation codon. Our predictor has been developed using tree-based machine learning algorithms and data extracted from Ensembl. Our final model is able to detect whether a mutation in the canonical initiation codon is deleterious or benign with a precision of 44.28% and an accuracy of 98.32%, which improves the results of state of the art tools such as PolyPhen, SIFT, or CADD for this type of mutation.

The development and application of biological ontologies have increased significantly in recent years. These ontologies can be retrieved from different repositories, which do not provide much information about quality aspects of the ontologies. In the past years, some ontology structural metrics have been proposed, but their validity as measurement instrument has not been sufficiently studied to date. In this work, we evaluate a set of reproducible and objective ontology structural metrics. Given the lack of standard methods for this purpose, we have applied an evaluation method based on the stability and goodness of the classifications of ontologies produced by each metric on an ontology corpus. The evaluation has been done using ontology repositories as corpora. More concretely, we have used 119 ontologies from the OBO Foundry repository and 78 ontologies from AgroPortal. First, we study the correlations between the metrics. Second, we study whether the clusters for a given metric are stable and have a good structure. The results show that the existing correlations are not biasing the evaluation, there are no metrics generating unstable clusterings and all the metrics evaluated provide at least reasonable clustering structure. Furthermore, our work permits to review and suggest the most reliable ontology structural metrics in terms of stability and goodness of their classifications. Availability: http://sele.inf.um.es/ontology-metrics.

Hospital Information Systems (H.I.S) use Electronic Health Record to store heterogeneous data from the patients. One important goal in this kind of systems is that the information must be, normalized and codify with a clinical terminology to represent exactly the healthcare meaning. Usually this process need human experts to identify and map the correct concept, this is a slow and tedious task. One of the most widespread clinical terminologies with more projection is Snomed-CT. This is an ontology multilingual clinical terminology that represent the clinical concepts with a unique code. We introduce in this paper Snomed2Vec, new approach of semantic search tool to find the most similar concepts using Snomed-CT. This is an ontology based named entity recognition system using word embedding, that suggest what is the most similar concept, that appear in a text. To evaluate the tool we suggest two kind of validations, one against a corpus gold with diagnostic from clinical reports, and a social validation, with a public free web access. We publish an access web to the academic world to use, test and validate the tool. The results of validation shows that this process help to the specialist to the election of choose the correct concepts from Snomed-CT. The paper illustrates 1) how create the initial big corpus of texts, to train the word2vec models, 2) how we use this vector space model to create our final Snomed2Vec vector space model, 3) The use of the cosine similarity distance, to obtain the most similar concepts, grouping by the hierarchies from Snomed-CT. We publish to the academic world: https://github.com/NachusS/Snomed2Vec access to the public web tool, and the notebook, for develop and test this paper.

The Quest for Orthologs (QfO) is an open collaboration framework for experts in comparative phylogenomics and related research areas who have an interest in highly accurate orthology predictions and their applications. We here report highlights and discussion points from the QfO meeting 2015 held in Barcelona. Achievements in recent years have established a basis to support developments for improved orthology prediction and to explore new approaches. Central to the QfO effort is proper benchmarking of methods and services, as well as design of standardized datasets and standardized formats to allow sharing and comparison of results. Simultaneously, analysis pipelines have been improved, evaluated and adapted to handle large datasets. All this would not have occurred without the long-term collaboration of Consortium members. Meeting regularly to review and coordinate complementary activities from a broad spectrum of innovative researchers clearly benefits the community. Highlights of the meeting include addressing sources of and legitimacy of disagreements between orthology calls, the context dependency of orthology definitions, special challenges encountered when analyzing very anciently rooted orthologies, orthology in the light of whole-genome duplications, and the concept of orthologous versus paralogous relationships at different levels, including domain-level orthology. Furthermore, particular needs for different applications (e.g. plant genomics, ancient gene families and others) and the infrastructure for making orthology inferences available (e.g. interfaces with model organism databases) were discussed, with several ongoing efforts that are expected to be reported on during the upcoming 2017 QfO meeting.

Motivation: Translation is a key biological process controlled in eukaryotes by the initiation AUG codon. Variations affecting this codon may have pathological consequences by disturbing the correct initiation of translation. Unfortunately, there is no systematic study describing these variations in the human genome. Moreover, we aimed to develop new tools for in silico prediction of the pathogenicity of gene variations affecting AUG codons, because to date, these gene defects have been wrongly classified as missense. Results: Whole-exome analysis revealed the mean of 12 gene variations per person affecting initiation codons, mostly with high (>0.01) minor allele frequency (MAF). Moreover, analysis of Ensembl data (December 2017) revealed 11 261 genetic variations affecting the initiation AUG codon of 7205 genes. Most of these variations (99.5%) have low or unknown MAF, probably reflecting deleterious consequences. Only 62 variations had high MAF. Genetic variations with high MAF had closer alternative AUG downstream codons than did those with low MAF. Besides, the high-MAF group better maintained both the signal peptide and reading frame. These differentiating elements could help to determine the pathogenicity of this kind of variation. Availability and implementation: Data and scripts in Perl and R are freely available at https://github.com/fanavarro/hemodonacion. Supplementary information: Supplementary data are available at Bioinformatics online.

Ontologies and terminologies have been identified as key resources for the achievement of semantic interoperability in biomedical domains. The development of ontologies is performed as a joint work by domain experts and knowledge engineers. The maintenance and auditing of these resources is also the responsibility of such experts, and this is usually a time-consuming, mostly manual task. Manual auditing is impractical and ineffective for most biomedical ontologies, especially for larger ones. An example is SNOMED CT, a key resource in many countries for codifying medical information. SNOMED CT contains more than 300000 concepts. Consequently its auditing requires the support of automatic methods. Many biomedical ontologies contain natural language content for humans and logical axioms for machines. The 'lexically suggest, logically define' principle means that there should be a relation between what is expressed in natural language and as logical axioms, and that such a relation should be useful for auditing and quality assurance. Besides, the meaning of this principle is that the natural language content for humans could be used to generate the logical axioms for the machines. In this work, we propose a method that combines lexical analysis and clustering techniques to (1) identify regularities in the natural language content of ontologies; (2) cluster, by similarity, labels exhibiting a regularity; (3) extract relevant information from those clusters; and (4) propose logical axioms for each cluster with the support of axiom templates. These logical axioms can then be evaluated with the existing axioms in the ontology to check their correctness and completeness, which are two fundamental objectives in auditing and quality assurance. In this paper, we describe the application of the method to two SNOMED CT modules, a 'congenital' module, obtained using concepts exhibiting the attribute Occurrence - Congenital, and a 'chronic' module, using concepts exhibiting the attribute Clinical course - Chronic. We obtained a precision and a recall of respectively 75% and 28% for the 'congenital' module, and 64% and 40% for the 'chronic' one. We consider these results to be promising, so our method can contribute to the support of content editors by using automatic methods for assuring the quality of biomedical ontologies and terminologies.

Nowadays, one of the main challenges in biology is to make use of several sources of data to improve our understanding of life. When analyzing experimental data, researchers aim at clustering genes that show a similar behavior through specific external conditions. Thus, the functional interpretation of genes is crucial and involves making use of the whole subset of terms that annotate these genes and which can be relatively large and redundant. The manual expertise to clearly decipher the main functions that may be related to the gene set is timeconsuming and becomes impracticable when the number of gene sets increases, like in the case of vaccine/drug trials. To overcome this drawback, it may be necessary to reduce the dataset with the aim to apply visualization approaches. In this paper, we propose a new pipeline combining enrichment and annotation terms simplification to produce a synthetic visualization of several gene sets simultaneously. We illustrate the efficiency of our method on a case study aiming at analyzing the immune response in diseases.

INTRODUCTION: The semantic interoperability of electronic healthcare records (EHRs) systems is a major challenge in the medical informatics area. International initiatives pursue the use of semantically interoperable clinical models, and ontologies have frequently been used in semantic interoperability efforts. The objective of this paper is to propose a generic, ontology-based, flexible approach for supporting the automatic transformation of clinical models, which is illustrated for the transformation of Clinical Element Models (CEMs) into openEHR archetypes. METHODS: Our transformation method exploits the fact that the information models of the most relevant EHR specifications are available in the Web Ontology Language (OWL). The transformation approach is based on defining mappings between those ontological structures. We propose a way in which CEM entities can be transformed into openEHR by using transformation templates and OWL as common representation formalism. The transformation architecture exploits the reasoning and inferencing capabilities of OWL technologies. RESULTS: We have devised a generic, flexible approach for the transformation of clinical models, implemented for the unidirectional transformation from CEM to openEHR, a series of reusable transformation templates, a proof-of-concept implementation, and a set of openEHR archetypes that validate the methodological approach. CONCLUSIONS: We have been able to transform CEM into archetypes in an automatic, flexible, reusable transformation approach that could be extended to other clinical model specifications. We exploit the potential of OWL technologies for supporting the transformation process. We believe that our approach could be useful for international efforts in the area of semantic interoperability of EHR systems.

The advent of Web 2.0, also called the Social Web, has changed the way people interact with the Web. Assisted by the technologies associated with this new trend, users now play a much more active role as content providers. This Web paradigm shift has also changed how companies operate and interact with their employees, partners and customers. The challenge for companies and research institutions is now to develop (semi-) automated tools for gathering usable and explicit knowledge from such content. With the aim of facilitating the achievement of such a challenge, in this work a platform architecture for informal learning, which is based on semantic technologies, is proposed. Such platform permits to perform expertise mining from Social Web-generated content. Given a topic of interest, the system carries out semantically enhanced operations on blog and microblog posts to identify experts in that specific topic area. The comprehensive evaluation of the tool has demonstrated very promising results and is also presented in this article.

Most Semantic Web technology-based applications need users to have a deep background on the formal underpinnings of ontology languages and some basic skills in these technologies. Generally, only experts in the field meet these requirements. In this paper, we present OWLPath, a natural language-query editor guided by multilanguage OWL-formatted ontologies. This application allows nonexpert users to easily create SPARQL queries that can be issued over most existing ontology storage systems. Our approach is a fully fledged solution backed with a proof-of-concept implementation and the empirical results of two challenging use cases: one in the domain of e-finance and the other in e-tourism.

BACKGROUND: There exist several information resources about orthology of genes and proteins, and there are also systems for querying those resources in an integrated way. However, caveats with current approaches include lack of integration, since results are shown sequentially by resource, meaning that there is redundant information and the users are required to combine the results obtained manually. RESULTS: In this paper we have applied the Ontological Gene Orthology approach, which makes use of a domain ontology to integrate the information output from selected orthology resources. The integrated information is stored in a knowledge base, which can be queried through semantic languages. A friendly user interface has been developed to facilitate the search; consequently, users do not need to have knowledge on ontologies or ontological languages to obtain the relevant information. CONCLUSION: The development and application of our approach allows users to retrieve integrated results when querying orthology information, providing a gene product-oriented output instead of a traditional information resource-oriented one. Besides this benefit for users, it also allows a better exploitation and management of orthology information and knowledge.

Abstract: Vast amounts of medical information reside within text documents, so that the automatic retrieval of such information would certainly be beneficial for clinical activities. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semi‐automatic methods to build ontologies. Most techniques for learning domain ontologies from free text have important limitations. Thus, they can extract concepts so that only taxonomies are generally produced although there are other types of semantic relations relevant in knowledge modelling. This paper presents a language‐independent approach for extracting knowledge from medical natural language documents. The knowledge is represented by means of ontologies that can have multiple semantic relationships among concepts.

In any modelling activity, a framework to determine the maturity of a developed model before its use is highly advantageous. Such a framework would save modellers expensive time in many areas of information systems. It would also lower the risk of users relying on an incomplete or inaccurate model. In this paper, we develop a framework which uses internal inconsistencies as a quantitative indicator for estimating the completeness and correctness of a model as it is cooperatively evolved. Whilst internal inconsistencies are due to bad fit between different parts of a model, we argue that they are also correlated with how the evolved model fits with the "world". This argument underpins our framework to evaluate integrated models. Contributions of this paper are three folds: firstly, it presents a theoretically grounded framework for integrating models. We extend an existing incremental modelling framework, NRDR, which represents multiple hierarchical restricted domains (MHRD), with automatic concept integration to allow NRDR to deal with multiple experts. Secondly, we couple this integration framework with a theoretically grounded monitoring process to assess the quality of the cooperatively developed model. Thirdly, we illustrate an initial empirical study of our evaluation and integration framework in a computer hardware administration domain. We capture and integrate computer hardware models from several experts and we use our modelling evaluation framework to evaluate the resultant cooperative model.

An ontology can be seen as an approximation to what exists in the real world. An ontological model is the kernel used for specifying ontologies so that how close an ontology can be from the real world depends on the possibilities offered by the ontological model. In this paper, an ontological model that permits the specification of concepts, attributes, mereological and taxonomic relationships, and structural axioms, and that has already been applied to different application domains is extended to contain topological relationships as well. It is demonstrated in this paper that topological relations can be established on ontologies built with that ontological model, and the topological relations are not established by the user but inferred from the knowledge contained in the particular ontology. The domain chosen for this work is the relation between drugs consumption and cancer diagnoses.