11,772 research outputs found
Automatic annotation of bioinformatics workflows with biomedical ontologies
Legacy scientific workflows, and the services within them, often present
scarce and unstructured (i.e. textual) descriptions. This makes it difficult to
find, share and reuse them, thus dramatically reducing their value to the
community. This paper presents an approach to annotating workflows and their
subcomponents with ontology terms, in an attempt to describe these artifacts in
a structured way. Despite a dearth of even textual descriptions, we
automatically annotated 530 myExperiment bioinformatics-related workflows,
including more than 2600 workflow-associated services, with relevant
ontological terms. Quantitative evaluation of the Information Content of these
terms suggests that, in cases where annotation was possible at all, the
annotation quality was comparable to manually curated bioinformatics resources.Comment: 6th International Symposium on Leveraging Applications (ISoLA 2014
conference), 15 pages, 4 figure
Ontology-Based Recommendation of Editorial Products
Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have addressed this issue by creating Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University (OU) in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues. SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products. This is based on the Computer Science Ontology, a very large-scale, automatically generated taxonomy of research areas. SBR also allows users to investigate why a certain publication was suggested by the system. It does so by means of an interactive graph view that displays the topic taxonomy of the recommended editorial product and compares it with the topic-centric characterization of the input conference. An evaluation carried out with seven Springer Nature editors and seven OU researchers has confirmed the effectiveness of the solution
Semantic web service automation with lightweight annotations
Web services, both RESTful and WSDL-based, are an increasingly important part of the Web. With the application of semantic technologies, we can achieve automation of the use of those services. In this paper, we present WSMO-Lite and MicroWSMO, two related lightweight approaches to semantic Web service description, evolved from the WSMO framework. WSMO-Lite uses SAWSDL to annotate WSDL-based services, whereas MicroWSMO uses the hRESTS microformat to annotate RESTful APIs and services. Both frameworks share an ontology for service semantics together with most of automation algorithms
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Materials science literature contains millions of materials synthesis
procedures described in unstructured natural language text. Large-scale
analysis of these synthesis procedures would facilitate deeper scientific
understanding of materials synthesis and enable automated synthesis planning.
Such analysis requires extracting structured representations of synthesis
procedures from the raw text as a first step. To facilitate the training and
evaluation of synthesis extraction models, we introduce a dataset of 230
synthesis procedures annotated by domain experts with labeled graphs that
express the semantics of the synthesis sentences. The nodes in this graph are
synthesis operations and their typed arguments, and labeled edges specify
relations between the nodes. We describe this new resource in detail and
highlight some specific challenges to annotating scientific text with shallow
semantic structure. We make the corpus available to the community to promote
further research and development of scientific information extraction systems.Comment: Accepted as a long paper at the Linguistic Annotation Workshop (LAW)
at ACL 201
A pattern-based approach to a cell tracking ontology
Time-lapse microscopy has thoroughly transformed our understanding of biological motion and developmental dynamics from single cells to entire organisms. The increasing amount of cell tracking data demands the creation of tools to make extracted data searchable and interoperable between experiment and data types. In order to address that problem, the current paper reports on the progress in building the Cell Tracking Ontology (CTO): An ontology framework for describing, querying and integrating data from complementary experimental techniques in the domain of cell tracking experiments. CTO is based on a basic knowledge structure: the cellular genealogy serving as a backbone model to integrate specific biological ontologies into tracking data. As a first step we integrate the Phenotype and Trait Ontology (PATO) as one of the most relevant ontologies to annotate cell tracking experiments. The CTO requires both the integration of data on various levels of generality as well as the proper structuring of collected information. Therefore, in order to provide a sound foundation of the ontology, we have built on the rich body of work on top-level ontologies and established three generic ontology design patterns addressing three modeling challenges for properly representing cellular genealogies, i.e. representing entities existing in time, undergoing changes over time and their organization into more complex structures such as situations
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