Converting Instance Checking to Subsumption: A Rethink for Object Queries over Practical Ontologies

Efficiently querying Description Logic (DL) ontologies is becoming a vital task in various data-intensive DL applications. Considered as a basic service for answering object queries over DL ontologies, instance checking can be realized by using the most specific concept (MSC) method, which converts instance checking into subsumption problems. This method, however, loses its simplicity and efficiency when applied to large and complex ontologies, as it tends to generate very large MSC's that could lead to intractable reasoning. In this paper, we propose a revision to this MSC method for DL SHI, allowing it to generate much simpler and smaller concepts that are specific-enough to answer a given query. With independence between computed MSC's, scalability for query answering can also be achieved by distributing and parallelizing the computations. An empirical evaluation shows the efficacy of our revised MSC method and the significant efficiency achieved when using it for answering object queries

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers

BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results

<p>Abstract</p> <p>Background</p> <p>High-throughput screening (HTS) is one of the main strategies to identify novel entry points for the development of small molecule chemical probes and drugs and is now commonly accessible to public sector research. Large amounts of data generated in HTS campaigns are submitted to public repositories such as PubChem, which is growing at an exponential rate. The diversity and quantity of available HTS assays and screening results pose enormous challenges to organizing, standardizing, integrating, and analyzing the datasets and thus to maximize the scientific and ultimately the public health impact of the huge investments made to implement public sector HTS capabilities. Novel approaches to organize, standardize and access HTS data are required to address these challenges.</p> <p>Results</p> <p>We developed the first ontology to describe HTS experiments and screening results using expressive description logic. The BioAssay Ontology (BAO) serves as a foundation for the standardization of HTS assays and data and as a semantic knowledge model. In this paper we show important examples of formalizing HTS domain knowledge and we point out the advantages of this approach. The ontology is available online at the NCBO bioportal <url>http://bioportal.bioontology.org/ontologies/44531</url>.</p> <p>Conclusions</p> <p>After a large manual curation effort, we loaded BAO-mapped data triples into a RDF database store and used a reasoner in several case studies to demonstrate the benefits of formalized domain knowledge representation in BAO. The examples illustrate semantic querying capabilities where BAO enables the retrieval of inferred search results that are relevant to a given query, but are not explicitly defined. BAO thus opens new functionality for annotating, querying, and analyzing HTS datasets and the potential for discovering new knowledge by means of inference.</p

RegenBase: a knowledge base of spinal cord injury biology for translational research.

Spinal cord injury (SCI) research is a data-rich field that aims to identify the biological mechanisms resulting in loss of function and mobility after SCI, as well as develop therapies that promote recovery after injury. SCI experimental methods, data and domain knowledge are locked in the largely unstructured text of scientific publications, making large scale integration with existing bioinformatics resources and subsequent analysis infeasible. The lack of standard reporting for experiment variables and results also makes experiment replicability a significant challenge. To address these challenges, we have developed RegenBase, a knowledge base of SCI biology. RegenBase integrates curated literature-sourced facts and experimental details, raw assay data profiling the effect of compounds on enzyme activity and cell growth, and structured SCI domain knowledge in the form of the first ontology for SCI, using Semantic Web representation languages and frameworks. RegenBase uses consistent identifier schemes and data representations that enable automated linking among RegenBase statements and also to other biological databases and electronic resources. By querying RegenBase, we have identified novel biological hypotheses linking the effects of perturbagens to observed behavioral outcomes after SCI. RegenBase is publicly available for browsing, querying and download.Database URL:http://regenbase.org

Importancia de la aplicación de retos matemáticos para el desarrollo del pensamiento matemático en estudiantes de secundaria

El presente trabajo plasma avances de una investigación en proceso, cuyo objetivo es potencializar el pensamiento matemático mediante la aplicación de retos matemáticos a estudiantes de secundaria e identificar su nivel de comprensión lectora, para coadyuvar al desempeño académico. La metodología es de corte cuantitativo, se utilizarán dos instrumentos, cuestionario y pruebas de comprensión lectora. Este estudio contribuye al beneficio de los alumnos, porque consiste en desarrollar las competencias matemáticas, habilidades y mejorar su desempeño académico, lo cual produce un impacto positivo en la sociedad, al formar jóvenes capaces de enfrentarse a los problemas de la vida

TopLeague and Bundesliga Manager: New Generation Online Soccer Games

This paper describes a new generation online soccer manager. More than 210,000 users operate TopLeague and the Official Bundesliga Manager, complex real-time soccer simulators that are based on actual data of real professional soccer players. The underlying technology is a hierarchical three-tier multiagent system that consists of autonomous BDI agents and allows dynamic group structures (e.g. an emergent situation for a wing attack). The German Bundesliga, one of the most prestigious soccer leagues in the world, adopted this AI system for their official web site. The online games run seamlessly in a web browser with a state-of-the-art 3D visualization engine. Fundamental research within the domain of the RoboCup simulation league is the basis for this technology. We will describe the architecture of the multiagent system (MAS) in this paper, discuss motion capture techniques for graphical animation, and reveal details about user acceptance of the games