Commonsense knowledge in cognitive robotics: a systematic literature review.

One of the big challenges in robotics is the generalization necessary for performing unknown tasks in unknown environments on unknown objects. For us humans, this challenge is simplified by the commonsense knowledge we can access. For cognitive robotics, representing and acquiring commonsense knowledge is a relevant problem, so we perform a systematic literature review to investigate the current state of commonsense knowledge exploitation in cognitive robotics.

INGRIDKG: A FAIR Knowledge Graph of Graffiti.

Graffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project (INGRID) closes this gap by dealing with graffiti image collections that have been made available to the project for public use.

COVIDPUBGRAPH: A FAIR Knowledge Graph of COVID-19 Publications.

The rapid generation of large amounts of information about the coronavirus SARS-CoV-2 and the disease COVID-19 makes it increasingly difficult to gain a comprehensive overview of current insights related to the disease. With this work, we aim to support the rapid access to a comprehensive data source on COVID-19 targeted especially at researchers. Our knowledge graph, COVIDPUBGRAPH, an RDF knowledge graph of scientific publications, abides by the Linked Data and FAIR principles.

An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition.

Background: This article provides an overview of the first BIOASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BIOASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.

Results: The 2013 BIOASQ competition comprised two tasks, Task 1a and Task 1b.

TopFed: TCGA tailored federated query processing and linking to LOD.

Backgroud: The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional effort to catalogue genetic mutations responsible for cancer using genome analysis techniques. One of the aims of this project is to create a comprehensive and open repository of cancer related molecular analysis, to be exploited by bioinformaticians towards advancing cancer knowledge. However, devising bioinformatics applications to analyse such large dataset is still challenging, as it often requires downloading large archives and parsing the relevant text files.

Ontology design patterns to disambiguate relations between genes and gene products in GENIA.

Motivation: Annotated reference corpora play an important role in biomedical information extraction. A semantic annotation of the natural language texts in these reference corpora using formal ontologies is challenging due to the inherent ambiguity of natural language. The provision of formal definitions and axioms for semantic annotations offers the means for ensuring consistency as well as enables the development of verifiable annotation guidelines.

Applying the functional abnormality ontology pattern to anatomical functions.

Background: Several biomedical ontologies cover the domain of biological functions, including molecular and cellular functions. However, there is currently no publicly available ontology of anatomical functions.Consequently, no explicit relation between anatomical structures and their functions is expressed in the anatomy ontologies that are available for various species.

Statistical tests for associations between two directed acyclic graphs.

Biological data, and particularly annotation data, are increasingly being represented in directed acyclic graphs (DAGs). However, while relevant biological information is implicit in the links between multiple domains, annotations from these different domains are usually represented in distinct, unconnected DAGs, making links between the domains represented difficult to determine. We develop a novel family of general statistical tests for the discovery of strong associations between two directed acyclic graphs.