dataesr/scientific-paragraphs-categorization huggingface dataset:
Crick-Chan query parsing. While LOD and the Semantic Web are rapidly adopted in the biology domain, the majority of biological knowledge is still only available in the form of natural language text, for example in manuscripts on PubMed or in textbooks on the NCBI Bookshelf. The ability to make use of this ocean of data would facilitate knowledge discovery and help bridge the current data retrieval process and the Semantic Web. The success of IBM Watson in the quiz show Jeopardy highlighted the potential of state-of-the-art cognitive computing in answering natural language questions. IBM Watson, however, does not rely so much on semantics or machine learning, but is rather based on queries on unstructured data, with statistical identification of answer domains (Lexical Answer Type). The software for IBM Watson (DeepQA) uses a system to answer a "word" that matches the natural language query by searching through millions of pages of documents, including the entire text of Wikipedia.
A scientific fact, or indeed any knowledge, is almost always written in natural language in the form of a manuscript, use of which is relatively less explored in the Semantic Web context. Therefore, at BH15 the G-language Project team aimed to develop a software system, designated "Crick-chan", that mimics DeepQA to find the most relevant "sentence" (as opposed to a "word" in Watson) from millions of scientific documents. Crick-chan mimics the architecture, and works as follows: 1. The question text first undergoes morphological analysis using Enju 156 to extract objective nouns and key verbs. Using a dictionary search, proper nouns are identified. 2. Queries are extended using the Bing search engine (which allows for the largest number of free queries among search engines). At the same time, the question is checked to see whether it belongs to the biology domain.
Natural language processing Clinical phenotype text mining. Clinical phenotypes, i. e. symptoms and signs, are key for diagnosis and treatment decision-making, particularly for rare or complex disorders  158. Delayed or inaccurate diagnosis incurs high economic costs in addition to heavy psychological burden on patients and their families. Deep clinical phenotyping in combination with genotyping are increasingly seen as important components of a vision for precision medicine  159. However, vast amounts of phenotypic data available from social media, EHR, biomedical databases, and the scientific literature, are largely inaccessible to direct computation because they are solely available in a narrative form. Natural Language Processing (NLP) involves the automatic extraction of relevant information from unstructured text and represents it in the form of structured concepts and relationships amenable to further computational analysis.
In fact, a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes found that 46 out of 97 papers on this topic used techniques based on natural language processing  164. In addition, several phenotype-annotated datasets have been recently extracted from journal articles and EHR by using BioNLP and text mining methodologies  158, [165] [166] [167] [168] [169]. The large-scale acquisition of phenotypic relationships from the literature enable a more complete view on the current knowledge, and thus, more efficient science. The use of text-mined data, i. e. information that is programmatically processed, aggregated and mined, shows much promise for some current challenges such as phenotype definition, hypothesis generation for research, understanding disease and pharmacovigilance.
The integration of curated and text mined data in the LOD opens new challenges for evidence and provenance tracking. Recent use of the nanopublication approach gives a mechanism for evidence, provenance and attribution tracking  171, 172. BH15 offered an opportunity to address different challenges related to the capture and analysis of human phenotype data. The text mining group focused its effort in the primary domains for deep phenotyping: acquisition of phenotype associations from journal articles, integration and alignment of annotation BioNLP tools, evaluation of secondary use of text mining corpora for knowledge discovery, semantic integration of text mined and curated data in the LOD, and curation of text mined data. All these tasks were pursued with a clear emphasis on standardization and interoperability between life sciences databases, text mined datasets and BioNLP tools, with the further aim to linking to the LOD.
As vocabularies, we use the phenotype ontologies for mammals, in particular the the Human Phenotype Ontology (HPO,  26, 166  ), MP, and the DO. Furthermore, we also use the phenotypic quality ontology (PATO,  180  ), and the Foundational Model of Anatomy (FMA,  181  ), an ontology of human anatomy, as additional vocabularies. Text processing is performed using Lucene, which includes basic text normalization such as stop-word removal and normalization to singular forms. The resulting text-mined annotations of the structured drug labels are freely available 182. In the future, these annotations of drugs need to be further evaluated and integrated in linked datasets.
Data analysis of text-mined corpora. The combination of highthroughput sequencing and deep clinical phenotyping offers improved capability in pinpointing the underlying genetic etiology of rare disorders. The accuracy of hybrid diagnosis systems is challenged by the vast number of associated variants, many of which lack phenotypic descriptions. At BH15, we sought to learn possible genotype-phenotype relationships from text mining. Specifically, we aimed to use text-mined corpora to learn associations between biological processes disrupted by gene mutations with externalized phenotypes. To do so, we combined two PubMed datasets: i) a dataset generated by the Biomedical Text Mining Group (BTMG) at NIH, comprised of automatically extracted named entities (MeSH terms, genes and mutations); and ii) a second one, generated by the Phenomics team at the Kinghorn Centre for Clinical Genomics (KCCG).
The latter covered structured PubMed metadata, i. e. , MeSH terms, keywords, etc. , as well as HPO annotations. The consolidation of the two datasets, via common MeSH terms, resulted in a final corpus of 6. 5M abstracts. To learn biological process -phenotype associations, we added biological process annotations from the Gene Ontology (GO,  183  ). Using the underlying diseases as latent variables (via MeSH terms) and summation as aggregation function, we produced an association matrix between 7,666 HPO terms and 10,438 GO Biological Process terms. The actual use of the matrix has been left for future experiments. Such experiments may cover various aggregation functions (e. g. , instead of summation, to use a linear interpolation of the term frequency inverse document frequency (TF-IDF) values of the HPO terms) as well as its application to discovering dense networks of phenotypesbiological processes.
Integration of text-mined and curated disease-phenotype data. DisGeNET-RDF contributes to LOD with Gene-Disease Associations (GDAs) obtained from Medline by text mining and integration with associations from different authoritative sources in human genetics  184. From release 3. 0. 0, DisGeNET-RDF also integrates curated Disease-Phenotype Associations (DPAs) to HPO terms for diseases in OMIM, Orphanet, and DECIPHER  185  from the HPO project  26. In order to examine what are the challenges to integrate text mining with curated DPAs in LOD, we analyzed the DPAs in DisGeNET-RDF (v3. 0. 0) and the DPAs text-mined from the scientific literature by Hoehndorf et al. 167. • Hoehndorf2015: This text-mining DPAs dataset contains 6,220 diseases identified by DO identifiers (DOIDs), 9,646 phenotypes identified using the HPO and the MP, and 124,213 DPAs.
All these issues affect ontology interoperability, and thus, the quality of their applications. The systematic ontological coding of phenotypic and molecular information in databases and their linking facilitates computational integrative approaches for identifying novel disease-related molecular information  187  , prioritizing candidate genes for diseases  [188] [189] [190] [191]  , as well as predicting novel drug-target interactions, drug targets, and indications  192, 193. The quality of the phenotypic descriptions of a resource will have implications for the quality of their interoperability, and thus, the quality of computational data analyses performed for translational research and knowledge discovery. In DisGeNET-RDF (v3. 0. 0), diseases are normalized with the UMLS CUIs, and are mapped to several disease vocabularies/ontologies with different coverage (see 194 to see disease mapping coverage statistics).
Some disease terms do not have cross-references to UMLS concepts. For instance, cancer (EFO_0000311) does not have UMLS CUIs associated, even though it is a general disease term. Nevertheless, the EFO contains over 2000 UMLS mappings from other ontologies, most of them from ORDO, which are manually curated. We suggest that an increase in the mapping between EFO and the UMLS terminologies will benefit data integration and interoperability between RDF datasets such as DisGeNET and other databases that are part of EBI RDF platform.
Here we extend the task definition to the use of LOD, and to the haiku structure, which has not previously been treated in the language generation literature  42, [206] [207] [208] [209] [210]. A haiku is a type of poem traditional to Japan; it consists of three verses with five, seven, and five syllables. In light of the work on semantic resources, in particular RDF datasets available through SPARQL, the idea arose to generate a haiku from a SPARQL query by identifying a connected subgraph in which the labels of the resources, or the properties linking them, follow the 5-7-5 syllable pattern of a haiku. Using the CELEX2 dictionary 211 , which maps English words to their syllables, we wrote a small haiku generator that can be initialized with a SPARQL endpoint and a start node (a resource) from which a search is started to identify a subgraph with the haiku pattern.
Reproducibility Extending the Common Workflow Language. Computational genomics faces challenges of scalability, reproducibility, and provenance tracking. Larger datasets, such as those produced by The Cancer Genome Atlas 214 , are now petabyte-sized, while procedures for read mapping, variant calling, genome assembly, and downstream imputation have grown impressively sophisticated, involving numerous steps by various programs. In addition to the need for reproducible, reusable, and trustworthy data, there is also the question of capturing reproducible data analysis, i. e. the steps that happen after raw data retrieval. Genomics analyses involving DNA or RNA sequencing are being used not just for primary research, but now also within the clinic, adding a legal component that makes it essential that analyses can be precisely reproduced.
The original idea of CWL is that a workflow can be described in a 'document' and this workflow, once described, can be rerun in different environments. CWL has roots in "make" and similar tools that determine order of execution based on dependency graphs of tasks. Unlike "make", CWL tasks are isolated and the user must be explicit about its inputs and outputs thereby creating a (hopefully reproducible) document of the workflow. To facilitate access to triple stores, we developed a package called Bio-Virtuoso based on Docker. The virtuoso-goloso container runs an instance of the Virtuoso triple store 221. This container also receives Turtle, RDF/XML, and OWL format files via the HTTP Post method and internally put them into Virtuoso speedy using the isql command. Graph-feeding containers download data from sources, convert them into RDF if necessary, and send them to virtuoso-goloso.
Multiple graphfeeding containers can be combined on demand. At BH15, we added more bioinformatics packages and documentation  229  to GNU Guix and created a deployment of Guix inside a Docker container 230. We also packaged CWL in Guix and added support for Ruby gems to Guix which means that existing Ruby packages can easily be deployed in Guix, similar to support for Python packages and R packages. Guix comes with a continuous integration system on a build farm. We want to harvest that information to see when packages are building or failing. See, for example, the Ruby builds 231 , which contain the SHA values of the package as well as the checkout of the Guix git repository reflecting the exact dependency graph. We are collaborating with Nix and Guix communities to get this information as JSON output so it can be used in a web service.
Much effort has been devoted to the problem of data interoperability, whether through data warehousing  245  , ontology-based query answering  246  , or shared application programming interfaces (APIs,  247  ). At BH15, a group of participants further developed a novel idea that was first proposed at a Data FAIRport meeting in 2014, called FAIR Projectors. FAIR Projectors are simple software applications that implement the FAIR principles by "projecting" data in any format (FAIR or non-FAIR) into a FAIR format. A projector will make use of a template-like document called a FAIR Profile, which acts as a meta-schema for the underlying data source. These meta-schemas may be indexed as a means to discover the projection of a dataset that matches the integrative requirements, i. e. the structure and semantics, of a particular workflow.
Ontology metadata mapping. Identification of equivalent or similar concepts between vocabularies is key to the analysis of aggregated datasets that use different terminologies. Efforts such as UMLS build and maintain a system for mapping biomedical ontologies to one another. However, such mappings depend on specific versions of the ontologies, and any one version can impact scientific analyses  250. Therefore, having access to ontology and mapping metadata is critical to the interpretation and reproducibility of results for bioinformatics research. Initiatives such as the Open Biomedical Ontologies (OBO) Foundry  251  , Linked Open Vocabularies (LOV, 252 ) and the National Center for Biomedical Ontology's (NCBO) BioPortal  253  have put forward schemas for ontology metadata. The Ontology Metadata Vocabulary (OMV,  254  ) was first published in 2005, but does not reuse current standard vocabularies.
In contrast, the Metadata for Ontology Description (MOD,  255  ) does reuse existing properties from SKOS, Friend Of A Friend (FOAF,  256  ) and Dublin Core and Dublin Core Terms (DC, DCT,  257  ). Recently, the W3C Semantic Web for Health Care and Life Sciences Interest Group 258 published a computable specification for the description of datasets, which could also be applied to the description of ontologies  259. With respect to mappings and their metadata, SKOS offers a lightweight system for terminology mappings, while Open Pharmacological Concept Triple Store (Open PHACTS,  260  ) put forward a more detailed proposal  261  for mappings between RDF datasets, or LinkSets. Yet, in our experience, additional attributes are needed for both ontology and mapping metadata. Therefore, we propose an enhanced metadata scheme as a best practice for ontologies and mappings so as to improve their discovery, analyses, and reporting of results.
Our work revealed a lack of common annotation in the description of mappings in both the attributes and vocabularies used. The inclusion of justification, provenance, evidence, directionality and versioning of mapping metadata has the potential to increase trust in the interpretation, reliability and reusability of mappings. Other provenance maintaining approaches such as Nanopublications 269 , singleton properties, or the Ontology of Biomedical AssociatioNs (OBAN,  270  ) that could be used to model this metadata description at individual mapping level to enable a more well detailed and fine-grained semantics description. Having good quality descriptions of ontology and mapping metadata is also relevant for ontology repositories such as BioPortal and Aber-OWL, ontology and data mapping services, and for methods geared towards scientific discovery.
The SMART Protocol framework proposes a minimal information unit for experimental protocols; the Sample, Instrument, Reagent, Objective model (SIRO, see 279), has been conceived in a way similar to that of the Patient Intervention Comparison Outcome (PICO,  280  ) model. It reuses a number of existing ontologies including the Information Artifact Ontology (IAO, 281 ), the OBI, the BioAssay Ontology (BAO,  282  ), the Chemical Entities of Biological Interest (ChEBI, 283 ), the EFO, the Eagle-i Resource Ontology (ERO, 284 ), EXACT, and the NCBI taxonomy  285. Semantic Web technologies including ontologies and Linked Data enable semantic publication of experimental protocols, their classification, and the mining of textual descriptions of experimental protocols. Limitations of current approaches to experimental metadata include an inability to cover the "digital continuum"-from the highly diverse set of complex processes in laboratories to the needs expressed by regulatory affairs.
Conclusions The BioHackathon series offers an unparalleled opportunity for scientists and software developers to work together to tackle challenging problems in the life sciences. BH15, the 2015 edition, was no exception, and featured contributions from a wide range of subdisciplines. On the topic of semantic metadata, we observed the FAIR principles gaining further traction with the development of additional tooling in the form of FAIR Projectors that represent data in FAIR ways using a template-like system. Likewise pertaining to semantic metadata, work was done at BH15 to assess the state of the art in recording the justification, provenance, evidence, directionality and versioning of ontology mappings. Additionally in this track, participants initiated work on a General Process Model to capture lab experimental metadata as networked sequences of activities, objects, transformations, and events.
Lastly in semantic metadata, participants worked on the ORKA system for annotating knowledge graphs by human curators. To contribute to the improvement of reproducibility in bioinformatics, participants in that track worked on three technologies that formally represent the steps of in silico experiments and the computational environment in which such experiments take place. The Common Workflow Language (CWL) is a system to describe command line tools and chain them together. At BH15, participants added CWL support to the Toil workflow engine and worked on CWL components that consume JSON(-LD). In addition, the Docker lightweight system for virtualization ('containerization') was targeted at BH15 to enable discovery of bioinformatics containers and simplify deployment of the Virtuoso triple store loaded with bioinformatic data sets.
In efforts to contribute to the representation of comparative data within frameworks of shared evolutionary ancestry, participants in the orthology and phylogeny track focused on two challenges. Firstly, work was done on the development of the Orthology Ontology to capture essential concepts pertaining to sequence orthology, including evolutionary events such as sequence duplication and speciation. Secondly, to attempt to place such evolutionary events on absolute time scales, an evaluation was made of the amenability of the Fossil-Calibrations database on the semantic web by implementing a prototype pipeline that calculates substitution rates for branches between speciation events as a function of time since gene duplication. Contributing In metabolomics, participants worked on improving the availability on the semantic web of data pertaining to the biochemical analysis of low-molecular-weight metabolites in biological systems.
This included a focus on the visualization of plant metabolome profiles and the identification and annotation of metabolites. Participants in this track further worked on the development of visual web applications to expose the metabolome database AtMetExpress. In the natural language processing track, participants worked on the capture and analysis of human phenotype data from free form text, i. e. the biomedical literature. Other participants in the same track worked on mining the structured text from drug labels to collect rich representations of drug (side) effects and indications. Also in this track, work was done on data analytics on text-mined corpora, specifically to attempt to learn associations between biological processes disrupted by gene mutations with externalized phenotypes.
Large assessments were made of the integration of text mined and curated data and of the interoperability of disease terminology. As a demonstration of the state of the art in generating natural language, a demo was developed that generates a haiku from data on the semantic web. The data retrieval and query answering track was concerned with new technologies for interrogating data on the semantic web. This included exposing semantic web services for Open-LifeData through re-implemented, more scalable interfaces. Participants in this track also worked on the SPARQL Builder, a tool for more easily constructing queries in the commonly used, but not very user-friendly, SPARQL language. Other ways to make queries easier included work on LODQA, a system that constructs queries from natural language.
A final demo of the state of the art in interrogating the semantic web in a playful way was Crick-Chan, which presents itself through cartoon animations and interacts with users through a chat bot interface. BH15 thus contributed to many challenges in bioinformatics, including the representation, publication, integration and application of biomedical data and metadata across multiple disciplines including chemistry, biology, genomics, proteomics, glycomics, metabolomics, phylogeny and physiology. A wealth of new semantics-aware applications have been developed through this hackathon that facilitate the reuse of complex biomedical data and build on global efforts to develop an ecosystem of interoperable data and services. As requirements for providing higher quality data and metadata continue to grow worldwide, BioHackathon participants will be well positioned to develop and apply semantic technologies to face the challenges of tomorrow.
This is a sprawling and fascinating report from a hackathon in 2015. The activities involve development of software and standards across a broad range of subdisciplines within the life sciences, but are united by largely basing their approaches on the application of semantic technologies. As such, the paper is an excellent overview of a wide variety of ways in which semantic technologies are being employed with great success in the biological sciences. Areas targeted include genotype/phenotype data, orthology and phylogeny, proteomics, metabolomics, data retrieval and querying, natural language processing, reproducibility, and metadata representation. Despite the several years that have elapsed since then, many of the tools built and insights gleaned still have relevance today, and so we are grateful that this work has been written and published.
179date, we have supported data sources such as the HPO, HPO-annotation, Online Mendelian Inheritance in Man (OMIM,222), OrphaNet223, the HUGO Gene Nomenclature Committee (HGNC,224), OMIM Japanese translation by Gendoo 225 , and MP179. Bio-Virtuoso is expected to lower barriers to learn SPARQL using real dataset and develop SPARQL-based applications. The project has a GitHub repository226. One problem of Docker-based deployment is that it requires special permissions from the Linux kernel, which are not given in many HPC environments. More importantly, Docker binary images are 'opaque', i. e. , it is not clear what is inside the container-and its state is affected by what time the container was created and what software is installed, i. e. , an intermediate apt-update may generate a different image.