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Vyhodnocení dopadu úderu je důležité pro optimální trénink, kondici a taktické využití. Proto bylo cílem této studie vyhodnotit pozemní a librové údery z hlediska variability čisté síly napříč pohlavími a výkonnostními úrovněmi. 81 účastníků, profesionální muži (n = 8, 37 ± 6 let, 195 ± 7 cm, 113 ± 27 kg), pokročilí muži (n = 47, 26 ± 8 let, 180 ± 7 cm, 76 ± 11 kg) , a pokročilé ženy (n = 26, 21 ± 1 rok, 167 ± 6 cm, 61 ± 7 kg) provedly tři údery z kleku do silové desky na zemi. Úder loktem měl za následek nejvyšší impuls a úder dlaní nejvyšší špičkovou sílu pro všechny tři kategorie. Tyto výsledky podporují doporučení, které bylo dříve učiněno pro výuku úderu dlaní začátečníky a pokročilé taktické a bojové sportovce. Síla přímého úderu úderem a loktem byla charakterizována dvojitou vrcholovou křivkou, kde první vrcholová variabilita vysvětlovala 70,2–84 % čisté síly. Druhý vrchol byl zaznamenán u profesionálních mužů při úderech loktem, což vysvětlovalo 16 % variability čisté síly. Typ úderu určuje síťovou sílu úderu a její charakteristiky, kde úder dlaní je typický nejvyšší tolerancí špiček nárazu a úder loktem dvojnásobnou špičkou síly s vysokým pulzem čisté síly The evaluation of strike impact is important for optimal training, conditioning and tactical use. Therefore, the aim of this study was to evaluate ground and pound strikes, in terms of net force variability, across genders and performance levels. Eighty-one participants, professional men (n = 8, 37 ± 6 years , 195 ± 7 cm, 113 ± 27 kg), advanced men (n = 47, 26 ± 8 years, 180 ± 7 cm, 76 ± 11 kg), and advanced women (n = 26, 21 ± 1 years, 167 ± 6 cm, 61 ± 7 kg) performed three strikes from a kneeling position into a force plate on the ground. The elbow strike resulted in the highest impulse and the palm strike in the highest peak force for all three categories. These results support the recommendation that has previously been made to teach the palm strike to beginners and advanced tactical and combat athletes. The direct punch and elbow strike net force were characterized by a double peak curve, where the first peak variability explained 70.2–84% of the net force. The second peak was pronounced in professional men during elbow strikes, which explained 16% of net force variability. The strike type determines the impact net force and its characteristics, where palm strike is typical by highest peak impact tolerance and elbow strike by double force peak with high net force impulse.

International audience; The scarcity of high-quality annotations in many application scenarios has recently led to an increasing interest in devising learning techniques that combine unlabeled data with labeled data in a network. In this work, we focus on the label propagation problem in multilayer networks. Our approach is inspired by the heat diffusion model, which shows usefulness in machine learning problems such as classification and dimensionality reduction. We propose a novel boundary-based heat diffusion algorithm that guarantees a closed-form solution with an efficient implementation. We experimentally validated our method on synthetic networks and five real-world multilayer network datasets representing scientific coauthorship, spreading drug adoption among physicians, two bibliographic networks, and a movie network. The results demonstrate the benefits of the proposed algorithm, where our boundary-based heat diffusion dominates the performance of the state-of-the-art methods.

Motivation Computational approaches for predicting drug–target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) or unintended (off-target) effects of drugs. However, existing models face several challenges. Many can only process a limited number of drugs and/or have poor proteome coverage. The current approaches also often suffer from high false positive prediction rates. Results We propose a novel computational approach for predicting drug target proteins. The approach is based on formulating the problem as a link prediction in knowledge graphs (robust, machine-readable representations of networked knowledge). We use biomedical knowledge bases to create a knowledge graph of entities connected to both drugs and their potential targets. We propose a specific knowledge graph embedding model, TriModel, to learn vector representations (i.e. embeddings) for all drugs and targets in the created knowledge graph. These representations are consequently used to infer candidate drug target interactions based on their scores computed by the trained TriModel model. We have experimentally evaluated our method using computer simulations and compared it to five existing models. This has shown that our approach outperforms all previous ones in terms of both area under ROC and precision–recall curves in standard benchmark tests. Availability and implementation The data, predictions and models are available at: drugtargets.insight-centre.org. Supplementary information Supplementary data are available at Bioinformatics online.

Knowledge graphs became a popular means for modeling complex biological systems where they model the interactions between biological entities and their effects on the biological system. They also provide support for relational learning models which are known to provide highly scalable and accurate predictions of associations between biological entities. Despite the success of the combination of biological knowledge graph and relation learning models in biological predictive tasks, there is a lack of unified biological knowledge graph resources. This forced all current efforts and studies for applying a relational learning model on biological data to compile and build biological knowledge graphs from open biological databases. This process is often performed inconsistently across such efforts, especially in terms of choosing the original resources, aligning identifiers of the different databases, and assessing the quality of included data. To make relational learning on biomedical data more standardised and reproducible, we propose a new biological knowledge graph which provides a compilation of curated relational data from open biological databases in a unified format with common, interlinked identifiers. We also provide a new module for mapping identifiers and labels from different databases which can be used to align our knowledge graph with biological data from other heterogeneous sources. Finally, to illustrate the practical relevance of our work, we provide a set of benchmarks based on the presented data that can be used to train and assess the relational learning models in various tasks related to pathway and drug discovery.

Polypharmacy is the use of drug combinations and is commonly used for treating complex and terminal diseases. Despite its effectiveness in many cases, it poses high risks of adverse side effects. Polypharmacy side-effects occur due to unwanted interactions of combined drugs, and they can cause severe complications to patients which results in increasing the risks of morbidity and leading to new mortalities. The use of drug polypharmacy is currently in its early stages; thus, the knowledge of their probable side-effects is limited. This encouraged multiple works to investigate machine learning techniques to efficiently and reliably predict adverse effects of drug combinations. In this context, the Decagon model is known to provide state-of-the-art results. It models polypharmacy side-effect data as a knowledge graph and formulates finding possible adverse effects as a link prediction task over the knowledge graph. The link prediction is solved using an embedding model based on graph convolutions. Despite its effectiveness, the Decagon approach still suffers from a high rate of false positives. In this work, we propose a new knowledge graph embedding technique that uses multi-part embedding vectors to predict polypharmacy side-effects. Like in the Decagon model, we model polypharmacy side effects as a knowledge graph. However, we perform the link prediction task using an approach based on tensor decomposition. Our experimental evaluation shows that our approach outperforms the Decagon model with 12% and 16% margins in terms of the area under the ROC and precision recall curves, respectively.

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is timeconsuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder).Author SummaryLinkPhinder is a new approach to prediction of protein signalling networks based on kinase-substrate relationships that outperforms existing approaches. Phosphorylation networks govern virtually all fundamental biochemical processes in cells, and thus have moved into the centre of interest in biology, medicine and drug development. Fundamentally different from current approaches, LinkPhinder is inherently network-based and makes use of the most recent AI de-velopments. We represent existing phosphorylation data as knowledge graphs, a format for large-scale and robust knowledge representation. Training a link prediction model on such a structure leads to novel, biologically valid phosphorylation network predictions that cannot be made with competing tools. Thus our new conceptual approach can lead to establishing a new niche of AI applications in computational biology.

Timely identification of adverse drug reactions (ADRs) is highly important in the domains of public health and pharmacology. Early discovery of potential ADRs can limit their effect on patient lives and also make drug development pipelines more robust and efficient. Reliable in silico prediction of ADRs can be helpful in this context, and thus, it has been intensely studied. Recent works achieved promising results using machine learning. The presented work focuses on machine learning methods that use drug profiles for making predictions and use features from multiple data sources. We argue that despite promising results, existing works have limitations, especially regarding flexibility in experimenting with different data sets and/or predictive models. We suggest to address these limitations by generalization of the key principles used by the state of the art. Namely, we explore effects of: (1) using knowledge graphs machine-readable interlinked representations of biomedical knowledge as a convenient uniform representation of heterogeneous data; and (2) casting ADR prediction as a multi-label ranking problem. We present a specific way of using knowledge graphs to generate different feature sets and demonstrate favourable performance of selected off-the-shelf multi-label learning models in comparison with existing works. Our experiments suggest better suitability of certain multi-label learning methods for applications where ranking is preferred. The presented approach can be easily extended to other feature sources or machine learning methods, making it flexible for experiments tuned toward specific requirements of end users. Our work also provides a clearly defined and reproducible baseline for any future related experiments. The TOMOE project funded by Fujitsu Laboratories Ltd., Japan and Insight Centre for Data Analytics at National University of Ireland Galway (supported by the Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289). peer-reviewed 2018-08-18

Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and predictive tasks. Despite the high predictive accuracy of these approaches, they have limited scalability due to their dependency on time-consuming path exploratory procedures. In recent years, owing to the rapid advances of computational technologies, new approaches for modelling graphs and mining them with high accuracy and scalability have emerged. These approaches, i.e. knowledge graph embedding (KGE) models, operate by learning low-rank vector representations of graph nodes and edges that preserve the graph s inherent structure. These approaches were used to analyse knowledge graphs from different domains where they showed superior performance and accuracy compared to previous graph exploratory approaches. In this work, we study this class of models in the context of biological knowledge graphs and their different applications. We then show how KGE models can be a natural fit for representing complex biological knowledge modelled as graphs. We also discuss their predictive and analytical capabilities in different biology applications. In this regard, we present two example case studies that demonstrate the capabilities of KGE models: prediction of drug target interactions and polypharmacy side effects. Finally, we analyse different practical considerations for KGEs, and we discuss possible opportunities and challenges related to adopting them for modelling biological systems. The work presented in this paper was supported by the CLARIFY project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 875160, and by Insight research centre supported by the Science Foundation Ireland (SFI) grant (12/RC/2289_2) peer-reviewed 2021-02-17

The field of drug discovery has entered a plateau stage lately. It is increasingly more expensive and time-demanding to introduce new drugs into the market. One of the main reasons is the slow progress in finding novel targets for drug candidates and the lack of insight in terms of the associated mechanisms of action. Current works in this area mainly utilise different chemical, genetic and proteomic methods, which are limited in terms of the scalability of experimentation and the scope of studied drugs and targets per experiment. This is mainly due to their dependency on laboratory experiments and available physical resource. This has led to an increasing importance of computational methods for the identification of candidate drug targets. In this work, we introduce a novel computational approach for predicting drug target proteins. We approach the problem as a link prediction task on knowledge graphs. We process drug and target information as a knowledge graph of interconnected drugs, proteins, disease, pathways and other relevant entities. We then apply knowledge graph embedding (KGE) models over this data to enable scoring drug-target associations, where we employ a customised version of state-of-the-art KGE model ComplEx. We generate a benchmarking dataset based on KEGG database to train and evaluate our method. Our experiments show that our method achieves best results in comparison to other traditional KGE models. Specifically, the method predicts drug target links with mean reciprocal rank (MRR) of 0.78 and Hits@10 of 0.88. This provides a promising basis for further experimentation and comparisons with domain-specific predictive models. This work has been supported by Insight Centre for Data Analytics at National University of Ireland Galway, Ireland (supported by the Science Foundation Ireland grant 12/RC/2289). The GPU card used in our experiments is granted to us by the Nvidia GPU Grant Program. peer-reviewed 2019-04-08

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder)., Author summary LinkPhinder is a new approach to prediction of protein signalling networks based on kinase-substrate relationships that outperforms existing approaches. Phosphorylation networks govern virtually all fundamental biochemical processes in cells, and thus have moved into the centre of interest in biology, medicine and drug development. Fundamentally different from current approaches, LinkPhinder is inherently network-based and makes use of the most recent AI developments. We represent existing phosphorylation data as knowledge graphs, a format for large-scale and robust knowledge representation. Training a link prediction model on such a structure leads to novel, biologically valid phosphorylation network predictions that cannot be made with competing tools. Thus our new conceptual approach can lead to establishing a new niche of AI applications in computational biology.

Graph feature models facilitate efficient and interpretable predictions of missing links in knowledge bases with network structure (i.e. knowledge graphs). However, existing graph feature models-e.g. Subgraph Feature Extractor (SFE) or its predecessor, Path Ranking Algorithm (PRA) and its variants-depend on a limited set of graph features, connecting paths. This type of features may be missing for many interesting potential links, though, and the existing techniques cannot provide any predictions at all then. In this paper, we address the limitations of existing works by introducing a new graph-based feature model - Distinct Subgraph Paths (DSP). Our model uses a richer set of graph features and therefore can predict new relevant facts that neither SFE, nor PRA or its variants can discover by principle. We use a standard benchmark data set to show that DSP model performs better than the state-of-the-art - SFE (ANYREL) and PRA - in terms of mean average precision (MAP), mean reciprocal rank (MRR) and Hits@5, 10, 20, with no extra computational cost incurred. peer-reviewed

Learning embeddings of entities and relations using neural architectures is an effective method of performing statistical learning on large-scale relational data, such as knowledge graphs. In this paper, we consider the problem of regularizing the training of neural knowledge graph embeddings by leveraging external background knowledge. We propose a principled and scalable method for leveraging equivalence and inversion axioms during the learning process, by imposing a set of model-dependent soft constraints on the predicate embeddings. The method has several advantages: i) the number of introduced constraints does not depend on the number of entities in the knowledge base; ii) regularities in the embedding space effectively reflect available background knowledge; iii) it yields more accurate results in link prediction tasks over non-regularized methods; and iv) it can be adapted to a variety of models, without affecting their scalability properties. We demonstrate the effectiveness of the proposed method on several large knowledge graphs.Our evaluation shows that it consistently improves the predictive accuracy of several neural knowledge graph embedding models (for instance,the MRR of TransE on WordNet increases by 11%) without compromising their scalability properties. This work was supported by the TOMOE project funded by Fujitsu Laboratories Ltd., Japan and Insight Centre for Data Analytics at National University of Ireland Galway (supported by the Science Foundation Ireland grant 12/RC/2289). peer-reviewed

Relation paths are sequences of relations with inverse that allow for complete exploration of knowledge graphs in a two-way unconstrained manner. They are powerful enough to encode complex relationships between entities and are crucial in several contexts, such as knowledge base verification, rule mining, and link prediction. However, fundamental forms of reasoning such as containment and equivalence of relation paths have hitherto been ignored. Intuitively, two relation paths are equivalent if they share the same extension, i.e., set of source and target entity pairs. In this paper, we study the problem of containment as a means to find equivalent relation paths and show that it is very expensive in practice to enumerate paths between entities. We characterize the complexity of containment and equivalence of relation paths and propose a domain-independent and unsupervised method to obtain approximate equivalences ranked by a tri-criteria ranking function. We evaluate our algorithm using test cases over real-world data and show that we are able to find semantically meaningful equivalences efficiently. peer-reviewed

Search engines used in contemporary online scientific publishing mostly exploit raw publication data (bags of words) and shallow metadata (authors, key words, citations, etc.). Exploitation of the knowledge contained implicitly in published texts is still largely not utilized. Following our long-term ambition to take advantage of such knowledge, we have implemented CORAAL (COntent extended by emeRgent and Asserted Annotations of Linked publication data), an enhanced-search prototype and the second-prize winner of the Elsevier Grand Challenge. CORAAL extracts asserted publication metadata together with the knowledge implicitly present in the relevant text, integrates the emergent content, and displays it using a multiple-perspective search&browse interface. This way we enable semantic querying for individual publications, and convenient exploration of the knowledge contained within them. In other words, recalling the metaphor in the article title, we let the users dive into publications more easily, and allow them to freely bathe in the related unlocked knowledge.

We introduce KI2NA-LHC (Linked Health Care) a system for data and knowledge integration in life sciences. In particular, we focus on linking clinical resources (electronic patient records) with scientific documents and data (research articles, biomedical ontologies and databases). Our motivation is two-fold. Firstly, we aim to instantly provide scientific context of particular patient cases for clinicians in order for them to propose treatments in a more informed way. Secondly, we want to build a technical infrastructure for researchers that will allow them to semi-automatically formulate and evaluate their hypothesis against longitudinal patient data. This paper outlines the proposed system and its services in a broader context of KI2NA, an ongoing collaboration between the DERI research institute and Fujitsu Laboratories.

Unlike full reading, 'skim-reading' involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text using superficial text parsing / processing techniques. We provide a preliminary web-based tool (called 'SKIMMR') that generates a network of inter-related concepts from a set of documents. In SKIMMR, a user may browse the network to investigate the lexically-driven information space extracted from the documents. When a particular area of that space looks interesting to a user, the tool can then display the documents that are most relevant to the displayed concepts. We present this as a simple, viable methodology for browsing a document collection (such as a collection scientific research articles) in an attempt to limit the information overload of examining that document collection. This paper presents a motivation and overview of the approach, outlines technical details of the preliminary SKIMMR implementation, describes the tool from the user's perspective and summarises the related work.

We aim at providing a complementary layer for the web semantics, catering for bottom-up phenomena that are empirically observable on the Semantic Web rather than being merely asserted by it. We focus on meaning that is not associated with particular semantic descriptions, but emerges from the multitude of explicit and implicit links on the web of data. We claim that the current approaches are mostly top-down and thus lack a proper mechanisms for capturing the emergent aspects of the web meaning. To fill this gap, we have proposed a framework based on distributional semantics (a successful bottom-up approach to meaning representation in computational linguistics) that is, however, still compatible with the top-down Semantic Web principles due to inherent support of rules. We evaluated our solution in a knowledge consolidation experiment, which confirmed the promising potential of our approach.

The paper presents CORAAL, a novel solution for life science publication search that exploits knowledge locked within unstructured publication texts (apart of possessing the traditional full text functionalities). In contrast to most related state of the art solutions, CORAAL integrally addresses acquisition (i.e., extraction), processing (i.e., integration and extension) and dissemination (i.e., convenient exposure) of the publication knowledge. After detailing the motivations of our research, we outline the representation and processing framework that allows CORAAL to tackle the rather noisy and sparse automatically extracted knowledge. The architecture and core features of the CORAAL prototype itself are described then. Most importantly, we report on an extensive evaluation of the CORAAL tool performed with an assistance of actual sample users. The evaluation illustrates the practical benefits brought by our solution already in the early research prototype stage.

Search engines used in contemporary online scientific publishing mostly exploit raw publication data (bags of words) and shallow metadata (authors, key words, citations, etc.). Exploitation of the knowledge contained implicitly in published texts is still largely not utilized. Following our long-term ambition to take advantage of such knowledge, we have implemented CORAAL (COntent extended by emeRgent and Asserted Annotations of Linked publication data), an enhanced-search prototype and the second-prize winner of the Elsevier Grand Challenge. CORAAL extracts asserted publication metadata together with the knowledge implicitly present in the relevant text, integrates the emergent content, and displays it using a multiple-perspective search & browse interface. This way we enable semantic querying for individual publications, and convenient exploration of the knowledge contained within them. In other words, recalling the metaphor in the article title, we let the users dive into publications more easily, and allow them to freely bathe in the related unlocked knowledge.

Despite being a flourishing field, the contemporary online scientific publishing properly exploits mostly raw publication data (rather meaningless bags of words) and shallow meta-data (authors, keywords, citations, etc.) regarding search. The much needed economical mass exploitation of the knowledge implicitly contained in publication texts is still largely an uncharted territory. The way towards filling this gap leads through (1) extraction of asserted publication meta-data together with the knowledge implicitly present in the respective text; (2) integration, refinement and extension of the emergent content; (3) release of the processed content via a meaning-sensitive search&browse interface catering for services complementary to the current full-text search. This chapter addresses the scientific and engineering challenges related to the suggested approach and introduces a particular solution that tackles them – CORAAL, a prototype for knowledge-based life science publication search.

We present a lightweight framework for processing uncertain emergent knowledge that comes from multiple resources with varying relevance. The framework is essentially RDF-compatible, but allows also for direct representation of contextual features (e.g., provenance). We support soft integration and robust querying of the represented content based on well-founded notions of aggregation, similarity and ranking. A proof-of-concept implementation is presented and evaluated within large scale knowledge-based search in life science articles. peer-reviewed

Prominent biomedical literature search tools like ScienceDirect, PubMed Central or MEDLINE allow for efficient retrieval of resources based on key words. Due to vast amounts of data available in life sciences, key word search is not always sufficient, though. One would often welcome more intelligent search for knowledge, i.e., for concepts and their mutual relations. This is, however, still a major challenge, since getting the necessary machine-readable knowledge manually is virtually impossible in large scale, while its automatic extraction is not particularly reliable. We have researched a novel framework actually enabling practical exploitation of automatically extracted knowledge, though. On the top of the framework, we implemented CORAAL, a prototype for knowledge-based biomedical literature search. This paper describes its essential principles, innovative capabilities and current results. peer-reviewed

We present a novel ontology integration technique that explicitly takes the dynamics and data-intensiveness of e-health and biomedicine application domains into account. Changing and growing knowledge, possibly contained in unstructured natural language resources, is handled by application of cutting-edge Semantic Web technologies. In particular, semi-automatic integration of ontology learning results into a manually developed ontology is employed. This integration bases on automatic negotiation of agreed alignments, inconsistency resolution and natural language generation methods. Their novel combination alleviates the end-user effort in the incorporation of new knowledge to large extent. This allows for efficient application in many practical use cases, as we show in the paper. peer-reviewed

We present new results of our research on integration of ontologies created automatically by means of Human Language Technologies. The research is related to OLE (Ontology LEarning) – a project aimed at bottom-up generation and merging of ontologies. It utilises a proposal of expressive uncertain knowledge representation framework called ANUIC (Adaptive Net of Universally Interrelated Concepts). We discuss our recent achievements in taxonomy acquisition and show how even simple application of the principles of ANUIC can improve the results of initial knowledge extraction methods. We also suggest an algorithm for large-scale automatic annotation of natural language documents, applying uncertain knowledge bases created using our approach.

Ontologies are commonly considered as one of the essential parts of the Semantic Web vision, providing a theoretical basis and implementation framework for conceptual integration and information sharing among various domains. In this paper, we present the main principles of a new ontology acquisition framework applied for semi-automatic generation of scientific portals. Extracted ontological relations play a crucial role in the structuring of the information at the portal pages, automatic classification of the presented documents as well as for personalisation at the presentation level.

The significance of uncertainty representation has become obvious in the Semantic Web community recently. This paper presents our research on uncertainty handling in automatically created ontologies. A new framework for uncertain information processing is proposed. The research is related to OLE (Ontology LEarning) — a project aimed at bottom–up generation and merging of domain–specific ontologies. Formal systems that underlie the uncertainty representation are briefly introduced. We discuss the universal internal format of uncertain conceptual structures in OLE then and offer a utilisation example then. The proposed format serves as a basis for empirical improvement of initial knowledge acquisition methods as well as for general explicit inference tasks.