Δημοσιογραφία δεδομένων και σημασιολογικοί γράφοι γνώσης: Εφαρμογές και καινοτομίες στην επικοινωνία

Large-scale cross-domain knowledge graphs, such as DBpedia or Wikidata, are some of the most popular and widely used datasets of the Semantic Web. In this paper, we introduce some of the most popular knowledge graphs on the Semantic Web. We discuss how machine learning is used to improve those knowledge graphs, and how they can be exploited as background knowledge in popular machine learning tasks, such as recommender systems.

This paper presents the experience gained in the context of a European pilot project funded by the ISA2 programme. It aims at constructing a semantic knowledge graph that establishes a distributed data space for public procurement. We describe the results obtained, the follow up actions and the main lessons learnt from the construction of the knowledge graph. This latter requires to support different data governance scenarios: some partners control, with their own tools, the building process of their portion of the knowledge graph. Other partners participate in the pilot by providing only their open CSV/XML/JSON datasets, in which case transformations are required. These are performed on the infrastructure made available by the European Big Data Test Infrastructure (BDTI). The paper introduces the design and implementation of the knowledge graph construction process within such a BDTI infrastructure. By instantiating an OWL ontology created for this purpose, we are able to provide a declarative description of the whole workflow required to transform input data into RDF output data, which form the knowledge graph. The declarative description is therefore used to provide instructions to a workflow engine we use (Apache Airflow) for knowledge graph construction purposes.

The technologies of knowledge representation and inference in an artificial intelligence system focused on the domain of nuclear physics and nuclear power engineering are considered. The possibilities of description logics and graph databases of nuclear knowledge for the generation of cognitive hypotheses, using in addition to deduction and other ways of reasoning, such as inductive inference and reasoning based on analogies, are discussed. The use of adequate description logic and measures of semantic similarity is substantiated. Interactive visual navigation and reasoning on the knowledge graphs are performed by means of special retrieval widgets and the smart RDF browser. Operations with semantic repositories are implemented on cloud platforms using SPARQL queries and RESTful services. The proposed software solutions are based on cloud computing using DBaaS and PaaS service models to ensure scalability of data warehouses and network services. Example of use of the offered technologies and software has been given.

Every day, big amounts of unstructured data is generated. This data is of the form of essays, research papers, speeches, patents, scholastic articles, book chapters etc. In today’s world, it is very important to extract key patterns from huge text passages or verbal speeches. This paper proposes a novel method for summarizing multilingual vocal as well as written paragraphs and speeches, using semantic Knowledge Graphs. Using the proposed model, big text extracts or speeches can be summarized for better understanding and analysis. The method uses speech recognition as well as Named Entity Recognition to identify entities from spoken content to create optimized Knowledge Graphs in the English Language.

Given an entity represented by a single node q in semantic knowledge graph D, the Graphical Entity Summarisation problem (GES) consists in selecting out of D a very small surrounding graph S that constitutes a generic summary of the information concerning the entity q with given limit on size of S. This article concerns the role of diversity in this quite novel problem. It gives an overview of the diversity concept in information retrieval, and proposes how to adapt it to GES. A measure of diversity for GES, called ALC, is defined and two algorithms presented, baseline, diversity-oblivious PRECIS and diversity-aware DIVERSUM. A reported experiment shows that DIVERSUM actually achieves higher values of the ALC diversity measure than PRECIS. Next, an objective evaluation experiment demonstrates that diversity-aware algorithm is superior to the diversity-oblivious one in terms of fact selection. More precisely, DIVERSUM clearly achieves higher recall than PRECIS on ground truth reference entity summaries extracted from Wikipedia. We also report another intrinsic experiment, in which the output of diversity-aware algorithm is significantly preferred by human expert evaluators. Importantly, the user feedback clearly indicates that the notion of diversity is the key reason for the preference. In addition, the experiment is repeated twice on an anonymous sample of broad population of Internet users by means of a crowd-sourcing platform, that further confirms the results mentioned above.

BackgroundIntensive Care Unit (ICU) readmissions represent both a health risk for patients,with increased mortality rates and overall health deterioration, and a financial burden for healthcare facilities. As healthcare became more data-driven with the introduction of Electronic Health Records (EHR), machine learning methods have been applied to predict ICU readmission risk. However, these methods disregard the meaning and relationships of data objects and work blindly over clinical data without taking into account scientific knowledge and context. Ontologies and Knowledge Graphs can help bridge this gap between data and scientific context, as they are computational artefacts that represent the entities of a domain and their relationships to each other in a formalized way.Methods and resultsWe have developed an approach that enriches EHR data with semantic annotations to ontologies to build a Knowledge Graph. A patient’s ICU stay is represented by Knowledge Graph embeddings in a contextualized manner, which are used by machine learning models to predict 30-days ICU readmissions. This approach is based on several contributions: (1) an enrichment of the MIMIC-III dataset with patient-oriented annotations to various biomedical ontologies; (2) a Knowledge Graph that defines patient data with biomedical ontologies; (3) a predictive model of ICU readmission risk that uses Knowledge Graph embeddings; (4) a variant of the predictive model that targets different time points during an ICU stay. Our predictive approaches outperformed both a baseline and state-of-the-art works achieving a mean Area Under the Receiver Operating Characteristic Curve of 0.827 and an Area Under the Precision-Recall Curve of 0.691. The application of this novel approach to help clinicians decide whether a patient can be discharged has the potential to prevent the readmission of 40% of Intensive Care Unit patients, without unnecessarily prolonging the stay of those who would not require it.ConclusionThe coupling of semantic annotation and Knowledge Graph embeddings affords two clear advantages: they consider scientific context and they are able to build representations of EHR information of different types in a common format. This work demonstrates the potential for impact that integrating ontologies and Knowledge Graphs into clinical machine learning applications can have.

Semantic technologies and knowledge graphs are becoming increasingly important in academia and industry. Nevertheless, they remain a rarity in the manufacturing industry, and the advantages they offer are only partially exploited. The same is true for semantic technologies for simulations within production systems. Simulation serves as an essential tool for virtual manufacturing, but the required models are still created by experts in an often elaborate and mostly manual way. In this paper, we derive the requirements for semantic models to capture production simulation knowledge and present an ontological architecture for a knowledge graph for manufacturing and simulation that meets these requirements. The different ontologies of the architecture are based on established production and simulation standards, namely ISA-95 and VDI guidelines. By implementing the ISA-95 standard in an ontology, heterogeneous data from the production system can be integrated. Furthermore, the connection of ISA-95 models to simulation-specific concepts allows the representation of simulation models within the knowledge graph. The implementation of the knowledge graph is shown within a prototypical simulation framework.

Semantic Web for Health Care and Life Sciences: a review of the state of the art

Digital twins (DTs) and eXtended Reality (XR) are two core technological enablers for engineering in the Metaverse that can accelerate the human-centric Industry 5.0 transformation. The digital twin technology provides a digital representation of a physical asset with data linkages for inspection, monitoring, and prediction of complex processes or systems, while eXtended reality offers real-and-virtual combined environments for human users to interact with machines. However, the synergies between digital twins and eXtended reality remain understudied. This work addresses this research gap by introducing a novel method “TwinXR” that leverages ontology-based descriptions of Digital twins, i.e., digital twin documents, in industrial eXtended reality applications. To ease the use of the TwinXR method, we publish a Unity package that allows data flow and conversion between eXtended reality applications and digital twin documents on the server. Finally, the work applies the TwinXR method in two industrial eXtended reality applications involving overhead cranes and a robot arm to demonstrate the use and indicate the validity of the method. We conclude that the TwinXR method is a promising way to advance the synergies between digital twins and eXtended reality: For eXtended reality, TwinXR enables efficient and scalable eXtended reality development; For digital twins, TwinXR unlocks and demonstrates the potential of digital twins for data interchange and system interoperation. Future work includes introducing more detailed principles of Semantic Web and Knowledge Graph, as well as developing factory-level TwinXR-compatible applications.

This report describes the adaptation, composition and use of natural language processing, machine learning and other computational tools to help make implicit informational structures in very large technical corpora explicit. The tools applied to the corpora automatically build normalized multi-word term structures, which in turn are used to build taxonomies, semantic schema, topic models and knowledge graphs. In our cybersecurity use case, we apply these tools to help us understand the threat landscape as exhibited in the Common Vulnerabilities and Exposures (CVE - https://cve.mitre.org/ and https://nvd.nist.gov/) corpus with the aim of proactively anticipating threats. The use case provides the context for development, use and evaluation of the automated tools and processes based on them. The latter are incrementally and iteratively evaluated and improved as they are developed and used. Local evaluation and improvement come before global evaluation. We believe that performing global evaluation of text processing methodologies and processes currently exemplified by the Text REtrieval Conference (TREC), Document Understanding Conference (DUC) and Text Analysis Conference (TAC) are worth pursuing, and we have done so using TREC. However, we will mostly be focusing on local development, evaluation and improvement in this report. We will articulate various aspects of these approaches by describing and showing 1) our multi-word term based process for topic modeling that can be supported by semantic schema, taxonomic structures and knowledge graphs built out of the same multi-word terms; 2) the heuristic methods used to evaluate the performance of the multi-word term-based topic modeling that includes suggestions for measuring how well a topic is represented in the documents that are indexed to it; 3) how these local heuristic methods might be transformed into a new full-blown rigorous evaluation standard like TREC, DUC and TAC, but with emphasis on their contribution to interpretation and understanding of very large corpora. With respect to 3), we will also briefly explore what parts of the heuristic process would need to be automated and an indication of the algorithms needed to do so.

In contemporary defense training and operations, users regularly encounter complicated and dynamic environments that generate large amounts of knowledge derived from locally acquired data. In order to facilitate collaborative decision making, users need to effectively share and distribute locally learned knowledge in a timely manner. This paper presents a semantic-based knowledge and information sharing system (S-KISS): a forum application for efficient peer-to-peer knowledge sharing. S-KISS enables simple and casual peer-to-peer information exchange, while retaining the quality of widely disseminated content for judicious knowledge consumption. Based on advanced semantic analysis technologies, S-KISS also supports effective semantic-based knowledge searching and semi-automated knowledge management with two knowledge management methods: (1) knowledge similarity searching based on WordNet and BERTScore, and (2) semantic similarity-based knowledge graph construction and knowledge grouping. The searching method focused on the semantics of text instead of word spans. Meanwhile, the grouping method constructs a knowledge graph where each node represents a posting and the links between nodes along with their semantic similarities. Postings can be grouped into multiple clusters of similar topics using Markov clustering algorithm, which allows users to look up related content quickly and effectively. The feasibility and effectiveness of S-KISS is demonstrated via a web-based prototype using practical scenarios and a real-world benchmark dataset curated from the sub-Reddit online forum ‘r/newtothenavy’. With broad and generic language models, the capabilities developed in S-KISS are applicable for knowledge information management in any space, air, sea, marine, and cyber domains. S-KISS can be utilized in other relevant software applications such as collaborative communication platforms and e-training discussion forums.

Accurate statistical information is critical for understanding, describing, and managing socio-economic systems. While data availability has increased, often it does not meet the quality requirements for effective governance. Administrative registers are crucial for statistical information production, but their potential is hampered by quality issues stemming from administrative inconsistencies. This paper explores the integration of semantic technologies, including ontologies and knowledge graphs, with administrative databases to improve data quality. We discuss the development of large language models (LLMs) that enable a robust, queryable framework, facilitating the integration of disparate data sources. This approach ensures high-quality administrative data, essential for statistical reuse and the development of comprehensive, dynamic knowledge graphs and LLMs tailored for administrative applications.

With the scale and complexity of healthcare data expanding more than ever, the need for efficient, secure, and intelligent data management systems is more important than ever. This paper provides a comprehensive overview of traditional and contemporary medical data management methods. The paper begins with traditional relational database management methods and provides an overview of these classical methods and discusses some advantages of relational databases such as structured data integrity and consistency. It then reviews emerging methods such as NoSQL databases, cloud computing methods, and AI-enabled systems that address issues associated with relational listing methods, including weaknesses handling unstructured data with structured schemas, managing scalability secondary to growth, and enabling advanced analytics based on querying and ingest. It also separately reviews hybrid approaches and forward-looking technologies, including blockchain, federated learning, edge computing, and semantic knowledge graphs that are largely shaping the next generation of healthcare data demography. It then contrasts the core features, trade-offs and challenges of implementing each system, and outlines current trends and for future guidance objectives for design of medical data systems. The aim is to provide useful references for practitioners in the design and selection of adaptable systems for the broad applicability of clinical needs.

We present AGNES – a novel algorithm for presenting diversified graphical entity summarisations on semantic knowledge graphs. The main idea is to compute the positions of the vertices in radial system of coordinates based on recursive, in-order traversal of the BST spanning tree of the graph. The implementation of the algorithm is compared on real data with other, existing visualisation tools. We also report a successful user evaluation crowdsourcing experiment that indicates superiority of our algorithm over one of the existing competitors.Keywordsgraphical entity summarisationsemantic knowledge graphsvisualisationdiversityuser evaluation

Anomaly detection in chemical processes with semantic knowledge graphs: an approach to reduce cause-effect diagrams