01861nas a2200181   4500008004100000022001400041245006200055210006000117300000800177490001600185520134800201653000701549653001501556653001701571100001801588700002301606856005001629       2009                        eng d  a1471-210500aGoWeb: a semantic search engine for the life science web.0 aGoWeb a semantic search engine for the life science web  aS7+0 v10 Suppl 103 aBACKGROUND: Current search engines are keyword-based. Semantic technologies promise a next generation of semantic search engines, which will be able to answer questions. Current approaches either apply natural language processing to unstructured text or they assume the existence of structured statements over which they can reason. RESULTS: Here, we introduce a third approach, GoWeb, which combines classical keyword-based Web search with text-mining and ontologies to navigate large results sets and facilitate question answering. We evaluate GoWeb on three benchmarks of questions on genes and functions, on symptoms and diseases, and on proteins and diseases. The first benchmark is based on the BioCreAtivE 1 Task 2 and links 457 gene names with 1352 functions. GoWeb finds 58% of the functional GeneOntology annotations. The second benchmark is based on 26 case reports and links symptoms with diseases. GoWeb achieves 77% success rate improving an existing approach by nearly 20%. The third benchmark is based on 28 questions in the TREC genomics challenge and links proteins to diseases. GoWeb achieves a success rate of 79%. CONCLUSION: GoWeb's combination of classical Web search with text-mining and ontologies is a first step towards answering questions in the biomedical domain. GoWeb is online at: http://www.gopubmed.org/goweb.10ait10aontologies10asemantic-web1 aDietze, Heiko1 aSchroeder, Michael  uhttp://dx.doi.org/10.1186/1471-2105-10-S10-S702868nas a2200229   4500008004100000245005300041210005100094260001200145300001600157490000700173520225200180653001602432653000702448653001502455653001602470100002002486700002002506700002202526700001602548700002202564856005202586       2006                        eng d00aBio-Ontology and text: bridging the modeling gap0 aBioOntology and text bridging the modeling gap  cOctober  a2421–24290 v223 aMotivation: Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text. Results: A novel representational schema, PGschema, was developed that enables translation of phenotypic, genetic and their closely related information found in textual narratives to a well-defined data structure comprising phenotypic and genetic concepts from established ontologies along with modifiers and relationships. Evaluation for coverage of a selected set of entities showed that 90% of the information could be represented (95% confidence interval: 86-93%; n = 268). Moreover, PGschema can be expressed automatically in an XML format using natural language techniques to process the text. To our knowledge, we are providing the first evaluation of a translational schema for NLP that contains declarative knowledge about genes and their associated biomedical data (e.g. phenotypes). Availability: http://zellig.cpmc.columbia.edu/PGschema Contact: Friedman@dbmi.columbia.edu or Lussier@uchicago.edu 10.1093/bioinformatics/btl40510adata-mining10ait10aontologies10atext-mining1 aFriedman, Carol1 aBorlawsky, Tara1 aShagina, Lyudmila1 aXing, Rosie1 aLussier, Yves, A.  uhttp://dx.doi.org/10.1093/bioinformatics/btl40500623nas a2200229   4500008004100000022001300041245006200054210006200116260000900178300001100187490000600198653001700204653001000221653000700231653002000238653001500258653001700273100002200290700001700312700001800329856004600347       2008                        eng d  a1556467300aResearch between natural and cultural history information0 aResearch between natural and cultural history information  cJune  a1–220 v110abiodiversity10acidoc10ait10anatural-history10aontologies10asemantic-web1 aLampe, Karl-Heinz1 aRiede, Klaus1 aDoerr, Martin  uhttp://dx.doi.org/10.1145/1367080.136708401402nas a2200217   4500008004100000022001400041245008900055210006900144260000900213300001400222490000700236520077500243653001901018653001401037653001501051653001601066100002001082700001901102700002201121856004101143       2009                        eng d  a1477-405400aBiological knowledge management: the emerging role of the Semantic Web technologies.0 aBiological knowledge management the emerging role of the Semanti  cJuly  a392–4070 v103 aNew knowledge is produced at a continuously increasing speed, and the list of papers, databases and other knowledge sources that a researcher in the life sciences needs to cope with is actually turning into a problem rather than an asset. The adequate management of knowledge is therefore becoming fundamentally important for life scientists, especially if they work with approaches that thoroughly depend on knowledge integration, such as systems biology. Several initiatives to organize biological knowledge sources into a readily exploitable resourceome are presently being carried out. Ontologies and Semantic Web technologies revolutionize these efforts. Here, we review the benefits, trends, current possibilities, and the potential this holds for the biosciences.10abioinformatics10adatabases10aontologies10asemanticweb1 aAntezana, Erick1 aKuiper, Martin1 aMironov, Vladimir  uhttp://dx.doi.org/10.1093/bib/bbp02401233nas a2200181   4500008004100000022001400041245005500055210005400110300000900164490000600173520075100179653001700930653000900947653001500956653001500971100001900986856004601005       2009                        eng d  a1756-050000aData publication: towards a database of everything0 aData publication towards a database of everything  a113+0 v23 aThe fabric of science is changing, driven by a revolution in digital technologies that facilitate the acquisition and communication of massive amounts of data. This is changing the nature of collaboration and expanding opportunities to participate in science. If digital technologies are the engine of this revolution, digital data are its fuel. But for many scientific disciplines, this fuel is in short supply. The publication of primary data is not a universal or mandatory part of science, and despite policies and proclamations to the contrary, calls to make data publicly available have largely gone unheeded. In this short essay I consider why, and explore some of the challenges that lie ahead, as we work toward a database of everything.10abiodiversity10adata10adatadeluge10aontologies1 aSmith, Vincent  uhttp://dx.doi.org/10.1186/1756-0500-2-113