Resulting Knowledge Base Research Articles

Cultural heritage digital twin: modeling and representing the visual narrative in Leonardo Da Vinci’s Mona Lisa

In this paper, Artificial Intelligence/Knowledge Representation methods are used for the digital modeling of cultural heritage elements. Accordingly, the new concept of digital cultural heritage twin is presented as composed of a physical component and an immaterial component of the cultural entity. The former concerns the physical aspects, i.e. style, name of the artist, execution time, dimension, etc. The latter represents the emotional and intangible aspects transmitted by the entity, i.e. emotions, thoughts, opinions. In order to digitally model the physical and immaterial components of the twin, the Narrative Knowledge Representation Language has been formally introduced and described. It is particularly suitable for representing the immaterial aspects of the cultural entity, as it is capable of modeling in a simple but rigorous and efficient way complex situations and events, behaviours, attitudes, etc. As an experiment, NKRL has been adopted for representing some of the most relevant intangible items of the visual narrative underlying the hidden painting that lies beneath the Mona Lisa (La Gioconda) image painted by Leonardo Da Vinci on the same poplar panel. Real-time application of the resulting knowledge base opens up novel possibilities for the development of virtual objects, chatbots and expert systems, as well as the definition of semantic search platforms related to cultural heritage.

Ethical considerations in utilizing artificial intelligence for analyzing the NHGRI’s History of Genomics and Human Genome Project archives

Understanding “how to optimize the production of scientific knowledge” is paramount to those who support scientific research—funders as well as research institutions—to the communities served, and to researchers. Structured archives can help all involved to learn what decisions and processes help or hinder the production of new knowledge. Using artificial intelligence (AI) and large language models (LLMs), we recently created the first structured digital representation of the historic archives of the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health. This work yielded a digital knowledge base of entities, topics, and documents that can be used to probe the inner workings of the Human Genome Project, a massive international public-private effort to sequence the human genome, and several of its offshoots like The Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE). The resulting knowledge base will be instrumental in understanding not only how the Human Genome Project and genomics research developed collaboratively, but also how scientific goals come to be formulated and evolve. Given the diverse and rich data used in this project, we evaluated the ethical implications of employing AI and LLMs to process and analyze this valuable archive. As the first computational investigation of the internal archives of a massive collaborative project with multiple funders and institutions, this study will inform future efforts to conduct similar investigations while also considering and minimizing ethical challenges. Our methodology and risk-mitigating measures could also inform future initiatives in developing standards for project planning, policymaking, enhancing transparency, and ensuring ethical utilization of artificial intelligence technologies and large language models in archive exploration.Author Contributions: Mohammad Hosseini: Investigation; Project Administration; Writing – original draft; Writing – review & editing. Spencer Hong: Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. Thomas Stoeger: Conceptualization; Investigation; Project Administration; Supervision; Writing – original draft; Writing – review & editing. Kristi Holmes: Funding acquisition, Supervision, Writing – review & editing. Luis A. Nunes Amaral: Funding acquisition, Supervision, Writing – review & editing. Christopher Donohue: Conceptualization, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing. Kris Wetterstrand: Conceptualization, Funding acquisition, Project administration.

The Facets of Intangible Heritage in Southern Chinese Martial Arts: Applying a Knowledge-driven Cultural Contact Detection Approach

Investigating the intangible nature of a cultural domain can take multiple forms, addressing, for example, the aesthetic, epistemic, and social dimensions of its phenomenology. The context of Southern Chinese martial arts is of particular significance, as it carries immaterial components of all these aspects: The technical and stylistic framework of a martial art system; the imagery associated to movements; and the transmission of knowledge orally, practically, or through influence, are but examples of intangible characteristics that can and should be captured, not unlike cultural artifacts. The latter case—the one of formalizing cultural influence through its various forms of evidenceis emblematic as well as largely untrodden ground. A previous attempt at detecting cultural influence computationally was made in the context of Roman archaeology, though the binding of that early effort with the domain model was tight; also, there has hardly been any prior dedicated effort to model the martial arts domain through ontologies. In this article, we present the realization of the full cycle of a computational approach to investigating cultural contact in Southern Chinese martial arts. The entire approach is predicated upon the usage of standards and techniques of the Semantic Web and formal knowledge. Starting from a modular domain ontology, which models martial arts independently of the goal of capturing cultural influence, we perform knowledge extraction from archival material from the Hong Kong Martial Arts Living Archive and generate a dataset of the results modeled after said ontology. Then, we combine the resulting knowledge base with a rule model that represents ways to infer knowledge of potential contact between cultures based on the evidence present in the knowledge base. The results offer an insight into how an inference-based computational model can be applied to detect interesting facts even in the as-yet underexplored domain of intangible cultural heritage. The implemented workflow shows that the full-cycle employment of semantic technologies can offer the ground truth required for largely different approaches, such as statistical and machine learning ones, to operate.

Сценарная модель оценки и прогнозирования рисков опасности электроустановок на основе анализа человекомашинной системы

Currently, the search for effective decision-making methods based on the use of modern information technologies is an important factor in the management of semi-structured systems. The choice of the optimal solution, as a rule, is carried out in conditions of incomplete initial data, a plurality of opinions about goals, criteria and their preference; it is also necessary to take into account the factors of uncertainty and subjectivity of specialist experts and their relationship with the decision maker (LNR). It seems timely to create expert systems for solving the multicriteria problem of risk optimization under conditions of uncertainty. The resulting knowledge base should combine the mathematical decision-making apparatus (DM) with non-strict heuristic expert methods. Keywords: MAN-MADE FIRES, LEAKAGE CURRENTS IN ELECTRICAL INSTALLATIONS, THERMAL OVERLOAD, HUMAN-MACHINE SYSTEM

Crowdsourcing the Historical Record: Creating Linked Open Data for Chinese History at Scale

An important part of the historical record of premodern China is recorded in historical works such as the standard dynastic histories. These works are a key source of knowledge about many aspects of premodern Chinese civilization, including persons, events, bureaucratic structures, literature, geography and astronomical observations. While many such sources have been digitized, typically these digitized texts encode only literal textual content and do not attempt to model the semantic content of the text. Similarly, while some of the historical data contained in some of these sources has been entered into specialist scholarly databases, an even greater proportion of the information does not yet exist in any machine-readable form. Producing such a machine-readable dataset of these materials requires the effort of many individuals working together due to the large scale of the task. This article introduces a crowdsourced approach in which annotation and knowledge base construction are carried out in parallel, with a knowledge base continually expanded through multi-user contributions to textual annotation immediately and automatically feeding back to provide improved assistance with subsequent annotation. The resulting knowledge base is dynamically exposed through Linked Open Data interfaces, creating a continually expanding machine-readable dataset covering around 3,000 years of recorded history.

More Than Open Space! The Case for Green Infrastructure Teaching in Planning Curricula

Since the mid-1990s, the concept of Green Infrastructure (GI) has been gaining traction in fields such as ecology and forestry, (landscape) architecture, environmental and hydrological engineering, public health as well as urban and regional planning. Definitions and aims ascribed to GI vary. Yet, agreement broadly exists on GI’s ability to contribute to sustainability by means of supporting, for example, biodiversity, human and animal health, and storm water management as well as mitigating urban heat island effects. Given an acknowledged role of planners in delivering sustainable cities and towns, professional bodies have highlighted the need for spatial planners to understand and implement GI. This raises questions of what sort of GI knowledge planners may require and moreover by whom and how GI knowledge and competencies may be conveyed? Examining knowledge and skills needs vis-à-vis GI education opportunities indicates a provision reliant primarily on continued professional education and limited ad hoc opportunities in Higher Education. The resulting knowledge base appears fragmented with limited theoretical foundations leading the authors to argue that a systematic inclusion of green infrastructure knowledges in initial planning education is needed to promote and aid effective GI implementation.

Frame-based terminology applied to military science: transforming a glossary into a knowledge resource

This paper describes a Frame-Based Terminology approach to the military terminology of the Spanish Armed Forces. The alphabetically organized (PD0-000) glossary of military terms of the Spanish Armed Forces was transformed into MiliMarco [MiliFrame], a bilingual terminological knowledge base in which each concept appears within a hierarchy of conceptual categories and a semantic network. Frame-based resources enhance access to domain knowledge in a contextualized way, since embedding concepts in a knowledge structure activates associative information in semantic memory and promotes context availability. The design and population of MiliMarco involved the analysis and transformation of the content in the glossary entries as well as the extraction of new information. For this purpose, specialized knowledge structures were elaborated from the definitions in the glossary and from the lexicalization of semantic relations in the corpus. New concepts were added to fill the gaps in the glossary and additional data categories were included, such as images, collocations, and contexts. Previous work on military ontologies, usually in the form of controlled, structured vocabularies, is limited to a specific domain (e.g., military intelligence). MiliMarco has the advantage of providing an expanded view of the military domain in the form of conceptual networks combined with linguistic contexts that go far beyond simple hierarchies. Although still an ongoing project, the resulting knowledge base is currently a concept-oriented resource where users can browse through the conceptual hierarchy and semantic networks based on their cognitive and communicative needs.

A Rule-Based Model for Compliance of Medical Devices Applied to the European Market

There is a growing producer and consumer interest in medical devices and the commensurate need for regulatory frameworks to ensure the quality of medical devices marketed locally and globally. This work focuses on formalizing the clauses enacted by Regulation (EU) 2017/745 for risk-based classification and class-based conformity assessment regarding marketability of medical devices. The resulting knowledge base (KB) represents clauses in Positional-Slotted Object-Applicative (PSOA) RuleML by integrating F-logic-like frames with Prolog-like relationships for atoms used as facts and in the conclusions and conditions of rules. Rules can apply polyadic functions, define polyadic relations, and augment conclusions with actions and conditions with events. The PSOA RuleML-implemented Medical Devices Rules KB was tested by querying in the open-source Java-implemented PSOATransRun system, which has provided a feedback loop for refinement and extension. This prototype can contribute to the licensing process of stakeholders and the registration of medical devices with a CE conformity mark.

PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

BackgroundPharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typically composed of units that have the form of ternary relationships gene variant – drug – adverse event. Such a relationship states that an adverse event may occur for patients having the specified gene variant and being exposed to the specified drug. State-of-the-art knowledge in PGx is mainly available in reference databases such as PharmGKB and reported in scientific biomedical literature. But, PGx knowledge can also be discovered from clinical data, such as Electronic Health Records (EHRs), and in this case, may either correspond to new knowledge or confirm state-of-the-art knowledge that lacks “clinical counterpart” or validation. For this reason, there is a need for automatic comparison of knowledge units from distinct sources.ResultsIn this article, we propose an approach, based on Semantic Web technologies, to represent and compare PGx knowledge units. To this end, we developed PGxO, a simple ontology that represents PGx knowledge units and their components. Combined with PROV-O, an ontology developed by the W3C to represent provenance information, PGxO enables encoding and associating provenance information to PGx relationships. Additionally, we introduce a set of rules to reconcile PGx knowledge, i.e. to identify when two relationships, potentially expressed using different vocabularies and levels of granularity, refer to the same, or to different knowledge units. We evaluated our ontology and rules by populating PGxO with knowledge units extracted from PharmGKB (2701), the literature (65,720) and from discoveries reported in EHR analysis studies (only 10, manually extracted); and by testing their similarity. We called PGxLOD (PGx Linked Open Data) the resulting knowledge base that represents and reconciles knowledge units of those various origins.ConclusionsThe proposed ontology and reconciliation rules constitute a first step toward a more complete framework for knowledge comparison in PGx. In this direction, the experimental instantiation of PGxO, named PGxLOD, illustrates the ability and difficulties of reconciling various existing knowledge sources.

Integrating Semantic Run-Time Modelsfor Adaptive Software Systems

Software-intensive systems work in ever-changing environments requiring expensive technical efforts to manage their evolution. In order to mitigate their risks and costs they should dynamically self-adapt to any modification of their environment. MAPE-K (Monitor, Analyze, Plan, Execute – Knowledge) is the basic architectural pattern for building software-intensive self-adaptable systems. In this paper we propose an approach in which all the information about a system and its environment is unified by using Semantic Web technologies into a set of semantic run-time models which enhance the Knowledge in MAPE-K. Ontologies are used to manage the interaction and integration of these models with disparate data sources. The resulting knowledge base is then used to drive adaptation activities exploiting well known languages and notations. We discuss how MAPE-K can be exploited in order to take advantage of ontological representations, along with Semantic Web languages and tools, by studying a real-word case study: a legacy system that was not designed to perform automatic adaptation. We discuss merits and limits of our approach based on semantic runtime models both in the context of this specific case study and in a broader scope.

A multicriteria resource allocation model for the redesign of services following birth

BackgroundMany healthcare services are under considerable pressure to reduce costs while improving quality. This is particularly true in the United Kingdom’s National Health Service where postnatal care is sometimes viewed as having a low priority. There is much debate about the service’s redesign and the reallocation of resources, both along care pathways and between groups of mothers and babies with different needs. The aim of this study was to develop a decision support tool that would encourage a systemic approach to service redesign and that could assess the various quality and financial implications of service change options making the consequent trade-offs explicit. The paper describes the development process and an initial implementation as a preliminary exploration of the possible merits of this approach.MethodsOther studies have suggested that combining multicriteria decision analysis with programme budgeting and marginal analysis might offer a suitable basis for resource allocation decisions in healthcare systems. The Postnatal care Resource Allocation Model incorporated this approach in a decision support tool to analyse the consequences of varying design parameters, notably staff contacts and time, on the various quality domains and costs. The initial phase of the study focussed on mapping postnatal care, involving interviews and workshops with a variety of stakeholders. This was supplemented with a literature review and the resultant knowledge base was encoded in the decision support tool. The model was then tested with various stakeholders before being used in an NHS Trust in England.ResultsThe model provides practical support, helping staff explore options and articulate their proposals for the redesign of postnatal care. The integration of cost and quality domains facilitates trade-offs, allowing staff to explore the benefits of reallocating resources between hospital and community-based care, and different patient-categories.ConclusionsThe main benefits of the model include its structure for assembling the key data, sharing evidence amongst multi-professional teams and encouraging constructive, systemic debate. Although the model was developed in the context of the routine maternity services for mothers and babies in the days following birth it could be adapted for use in other health care services.

Testing-Based Approach to Determining the Divergence Speed of Slung Loads

When a rotorcraft carries an external slung load, flight speed is often limited by the fear of divergent oscillations, rather than vehicle performance. Since slung objects can be of any shape, incorporating the aerodynamics with sufficient accuracy to predict safe speed has been a problem. The uncertainty forces certifying authorities to set conservative limits on speed to avoid divergence. Obtaining the aerodynamic coefficients of bluff bodies was excessively time-consuming in experiments, and impractical in computations. This review traces the evolution of progress in the area. Prior thinking was to use computations for prediction, with the computational codes validated using a few samples of experiments. This approach has not led to valid general predictions. Data were sparse and a-priori predictions were rarer. A continuous rotation approach has enabled swift measurements of 6-degrees-of-freedom aerodynamic load maps with high resolution about several axes of rotation. The resulting knowledge base in turn permits a swift determination of dynamics up to divergence, with wind tunnel tests where necessary to fill interpolation gaps in the knowledge base. The essence of efficient and swift dynamics simulation with a few well-tested assumptions is described. Under many relevant conditions, the vehicle flight dynamics can be safely decoupled from those of the slung load. While rotor wake swirl causes the payload to rotate at liftoff and landing, this effect can be incorporated into the simulation. Recent success in explaining two well-documented flight test cases provides strong evidence that predictions can be made for most missions swiftly.

Modelling and Monitoring Environmental Risks through a Semantic Framework

The widespread availability of IoT technologies allows, among others, detailed monitoring of environmental parameters that can be used for effective prevention and forecasts of natural disasters. When such events occur, or are about to occur, the primary concern of authorities responsible for the public safety is to organise and enforce the relief in an as quick as possible and effective way. However, the huge amount of information that an IoT infrastructure is able to provide is useless if not correlated and contextualised on the territory where the hazard has occurred. In this paper the authors present the design of a semantic information model which integrates different information domains into a unified framework. In particular, some ontologies have been defined to represent, respectively, the information domain of hazards, the domain of sensors (which are responsible for measuring the phenomena that may trigger hazards), the geographic domain and the environmental risk domain, also including the potential damage and dangerousness of such events. In the proposed semantic framework, a place (be it a single building or an entire region) can be characterised from the environmental risk point of view. The resulting knowledge base can then be used to build monitoring services capable of identifying on the territory those sensing units whose sensed data are really useful for the setup of a relief plan. A prototype of such a monitoring tool was also implemented. Finally, two simple yet complete use case examples focusing on the wildfire hazard and on the flood risk respectively are discussed in the paper.

A Computational Method for Enforcing Knowledge that Cannot be Subsumed

We introduce a practical computational method that enforces some incoming piece of information [Formula: see text] in a clausal Boolean logic knowledge base [Formula: see text] in such a way that [Formula: see text] is not strictly subsumed by the resulting knowledge base. [Formula: see text] is not strictly subsumed by [Formula: see text] iff for every piece of information [Formula: see text] that is entailed by [Formula: see text] and that is such that [Formula: see text] entails [Formula: see text], we have that [Formula: see text] entails [Formula: see text], too. We claim that this paradigm is a useful form of reasoning for both human and artificial intelligence systems. Under a usual minimal change policy, it amounts to computing one cardinality-maximal satisfiable subset of [Formula: see text] that contains [Formula: see text] but that does not strictly subsume [Formula: see text]. Although this task is intractable in the worst case, we provide a practical method that appears experimentally efficient very often, even for large and complex knowledge bases.

An OWL ontology library representing judicial interpretations

The article introduces JudO, an OWL2 ontology library of legal knowledge that relies on the metadata contained in judicial documents. JudO represents the interpretations performed by a judge while conducting legal reasoning towards the adjudication of a case. To the aim of this application, judicial interpretation is intended in the restricted sense of the acts of judicial subsumption performed by the judge when he considers a material instance (token in Searle's terminology), and assigns it to an abstract category (type). The ontology library is based on a theoretical model and on some specific patterns that exploit some new features introduced by OWL2. JudO provides meaningful legal semantics, while retaining a strong connection to source documents (fragments of legal texts). The application task is to enable detection and modeling of jurisprudence-related information directly from the text, and to perform shallow reasoning on the resulting knowledge base. The ontology library is also supposed to support a defeasible rule set for legal argumentation on the groundings of judicial decisions.

Participatory approach to the development of a knowledge base for problem-solving in diabetes self-management

ObjectiveTo develop an expandable knowledge base of reusable knowledge related to self-management of diabetes that can be used as a foundation for patient-centric decision support tools. Materials and methodsThe structure and components of the knowledge base were created in participatory design with academic diabetes educators using knowledge acquisition methods. The knowledge base was validated using scenario-based approach with practicing diabetes educators and individuals with diabetes recruited from Community Health Centers (CHCs) serving economically disadvantaged communities and ethnic minorities in New York. ResultsThe knowledge base includes eight glycemic control problems, over 150 behaviors known to contribute to these problems coupled with contextual explanations, and over 200 specific action-oriented self-management goals for correcting problematic behaviors, with corresponding motivational messages. The validation of the knowledge base suggested high level of completeness and accuracy, and identified improvements in cultural appropriateness. These were addressed in new iterations of the knowledge base. DiscussionThe resulting knowledge base is theoretically grounded, incorporates practical and evidence-based knowledge used by diabetes educators in practice settings, and allows for personally meaningful choices by individuals with diabetes. Participatory design approach helped researchers to capture implicit knowledge of practicing diabetes educators and make it explicit and reusable. ConclusionThe knowledge base proposed here is an important step towards development of new generation patient-centric decision support tools for facilitating chronic disease self-management. While this knowledge base specifically targets diabetes, its overall structure and composition can be generalized to other chronic conditions.

Validation of a Crowdsourcing Methodology for Developing a Knowledge Base of Related Problem-Medication Pairs.

Clinical knowledge bases of problem-medication pairs are necessary for many informatics solutions that improve patient safety, such as clinical summarization. However, developing these knowledge bases can be challenging. We sought to validate a previously developed crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large, non-university health care system with a widely used, commercially available electronic health record. We first retrieved medications and problems entered in the electronic health record by clinicians during routine care during a six month study period. Following the previously published approach, we calculated the link frequency and link ratio for each pair then identified a threshold cutoff for estimated problem-medication pair appropriateness through clinician review; problem-medication pairs meeting the threshold were included in the resulting knowledge base. We selected 50 medications and their gold standard indications to compare the resulting knowledge base to the pilot knowledge base developed previously and determine its recall and precision. The resulting knowledge base contained 26,912 pairs, had a recall of 62.3% and a precision of 87.5%, and outperformed the pilot knowledge base containing 11,167 pairs from the previous study, which had a recall of 46.9% and a precision of 83.3%. We validated the crowdsourcing approach for generating a knowledge base of problem-medication pairs in a large non-university health care system with a widely used, commercially available electronic health record, indicating that the approach may be generalizable across healthcare settings and clinical systems. Further research is necessary to better evaluate the knowledge, to compare crowdsourcing with other approaches, and to evaluate if incorporating the knowledge into electronic health records improves patient outcomes.

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Introduction: Clinical research is the foundation of quality patient care. Randomized controlled clinical trials (RCTs) provide the most reliable evidence of efficacy or harm, but are costly and time-consuming to conduct. As a result, in many fields, the majority of RCTs are initiated and run by the pharmaceutical or medical device industry. Concerns have been voiced that the resulting knowledge base is unduly influenced by commercial considerations. At the same time, influential trials have been increasingly led by investigator-led trials groups from around the world. We sought to understand the influence of funding model on the impact of large ICU-based clinical trials. Methods: We conducted a systematic review of RCTs published in five high impact general interest medical journals, five critical care journals, and one pediatrics journal. We included all RCTs published between 1990 and 2009 that had randomized 100 or more patients and reported mortality and/or length of stay as an outcome. We excluded cluster RCTs and secondary publications from a parent study. We determined the funding model (industry vs peer review vs mixed) and the administrative model (single center, 2-5 multicenter, multicenter run by ad hoc group, multicenter led by a formally-constituted trials group, or industry run). Citation rates were obtained from ISI Web of Science in July 2013, and expressed as citations/year. Data are expressed as means ± SD or medians + IQR; statistical analysis used GraphPad Prism. Results: We included 327 trials that recruited 159,869 patients (median 280; IQR 146-532). Both the number of trials (31; 74; 100; 122) and the median citation rates (3.9; 6.5; 10.6; 11.9) increased significantly over time, expressed in 5 year increments. Comparative effectiveness trials were more common than trials evaluating novel technologies (57.4 vs 42.6%), and cited more frequently (27.6+/-51.7 vs 15.4+/-27.0 citations, p=0.01). Industry funding was the primary support for 87 (26.8%) studies, while 159 (48.9%) received peer review funding or were unfunded; the remaining 79 (24.3%) were funded through blended models; data were not obtainable for 2 trials. The funding source did not influence citation rates. Citation rates were, however, strongly influenced by the organizational model. Reports from formally constituted trials groups were cited a median of 44.9 (IQR 12-77) times a year, significantly more frequently (p<0.0001) than single center studies (5.4; IQR 3-10), small multicenter studies (9.6; IQR 4-20), studies conducted by ad hoc groups (16.1; IQR 6-25), or studies conducted by industry (10.6; IQR 5-23). Of 11 trials cited more than 100 times per year, 5 were conducted by trials groups, whereas only 1 was led by industry. Conclusions: Investigator-initiated research is the most common model for critical care RCTs, with a primary focus on comparative effectiveness research. Studies conducted by dedicated trials groups have the highest impact reflected in annual citation rates. Expansion of this model should be actively promoted, and new funding models developed to reflect the impact that investigator-led research has in defining best practice in the care of critically ill patients.

Semantic Multidimensional Scaling for Open-Domain Sentiment Analysis

The ability to understand natural language text is far from being emulated in machines. One of the main hurdles to overcome is that computers lack both the common and common-sense knowledge that humans normally acquire during the formative years of their lives. To really understand natural language, a machine should be able to comprehend this type of knowledge, rather than merely relying on the valence of keywords and word co-occurrence frequencies. In this article, the largest existing taxonomy of common knowledge is blended with a natural-language-based semantic network of common-sense knowledge. Multidimensional scaling is applied on the resulting knowledge base for open-domain opinion mining and sentiment analysis.

Spatial data management priorities for assessment of Europe’s coasts and seas

Systemic understanding of marine and coastal environment needs data integration following a respective concept e.g. multi-dimensional and functional mapping. A number of new activities will improve data supply for coasts and seas. This data needs to be integrated and combined with socio-economic drivers and resulting pressures. Resulting knowledge base should be able to inform effectively ecosystem-based management actions, such as integrated coastal zone management, maritime spatial planning, extension of Natura 2000 areas or climate change adaptation in coastal regions and maritime sectors. Assessment that aims to inform such processes will require rethinking of priorities for spatial data collection and analysis, in particular building on data sharing and standardization, improved spatial data integration, promoting interoperability of relevant information systems and possibility of assimilating different data types in to models. Different aspects of spatial data should be addressed in coherent implementation of spatial data infrastructure.