A rough translation of terms used to describe relational databases and ontologies:
Relational database | Ontology |
row | subject |
column | predicate |
table data | literal nodes |
SPARQL is a query language for ontologies
SELECT ?name WHERE { [ a my:person; my:suffers my:insomnia; my:name ?name]. }
This query returns the names of all people who suffer insomnia. It uses a syntax similar to SQL and contains a blank node inside a WHERE clause.
SPARQL supports keywords like (NOT) EXISTS, UNION, OPTIONAL, DISTINCT, MINUS, GROUP BY, HAVING. Available aggregates are: COUNT, SUM, MIN, MAX, AVG, GROUP_CONCAT, and SAMPLE. Moreover, all XPath functions (e.g., string manipulation utils like concat) can be used as long as they are preceded with the "xsd:" prefix.
SELECT (SUM(?lprice) AS ?totalPrice) WHERE { ?org :affiliates ?auth . ?auth :writesBook ?book . ?book :price ?lprice . } GROUP BY ?org HAVING (SUM(?lprice) > 10)
DELETE { ?x my:likes ?y . } WHERE { ?x a my:person . ?y a my:person; my:suffers my:xenophobia . }
Removes people that like xenophobia
INSERT { ?x my:loves ?y . } WHERE { ?x a my:person . MINUS {?x a my:girl} ?y a my:person, my:girl . }
Outputs that everybody loves girls -- excluding the girls themselves
N3 Notation:
@prefix my: <http://www.codeproject.com/KB/recipes/n3_notation#>. my:Peter a my:person, my:boy; my:suffers my:acrophobia, my:insomnia, my:xenophobia; my:name "Peter"; my:likes my:Kate. my:Mark a my:person, my:boy; my:suffers my:insomnia; my:name "Mark". my:Kate a my:person, my:girl; my:name "Kate".
SparQL statement:
PREFIX my: <http://www.codeproject.com/KB/recipes/n3_notation#> SELECT ?name WHERE { [ a my:person; my:suffers my:insomnia; my:name ?name] . }
SparQL Results:
Chemical Entities of Biological Interest, also known as ChEBI, is a database and ontology of molecular entities focused on 'small' chemical compounds, that is part of the Open Biomedical Ontologies effort.
ChEBI ontology files ChEBI schema2018-09-09
FOODON
FoodOn (http://foodon.org) is a consortium-driven project to build a comprehensive and easily accessible global farm-to-fork ontology about food, that accurately and consistently describes foods commonly known in cultures from around the world.
Food Ontology
EAN全名为European Article Number(欧洲商品条码),在1977年时由欧洲几个主要工业国家共同发展出来的,后来变成国际商品条码系统。台湾在1985年加入EAN会员,现在我们买东西时在柜台结帐,服务人员用扫瞄器所读的商品上的条码就是EAN条码。国际商品条码需经过申请,不可自行编码列印。
EAN-13现在称之为GTIN-13 (Global Trade Item Number)条码,属于GS1国际条码系统其中的一种。
备注:GS1国际条码系统包括GTIN-13、GTIN-12(原称UPC)、GTIN-8(原称EAN-8)、GLN、GTIN-14(ITF-14)、SSCC、GS1-128(原称EAN- 128)、GS1 Databar(原称RSS code)、GS1 Data Matrix 等。
KEGG MEDICUS is an integrated information resource of diseases, drugs, and health-related substances, aiming to bring the genomic revolution to society (see background information). Specifically, package insert information for all drugs marketed in Japan and the USA is integrated with KEGG DRUG and KEGG DISEASE.
The KEGG MEDICUS subset is freely available at the GenomeNet FTP site.
ftp://ftp.genome.jp/pub/kegg/medicus/
plant environment ontology (EO:0007359): A set of standardized controlled vocabularies to describe various types of treatments given to an individual plant / a population or a cultured tissue and/or cell type sample to evaluate the response on its exposure. It also includes the study types, where the terms can be used to identify the growth study facility. Each growth facility such as field study, growth chamber, green house etc is a environment on its own it may also involve instances of biotic and abiotic environments as supplemental treatments used in these studies.
plant environment (EO:0007359): A plant treatment, growing condition, and/or study type applied to a whole plant (PO:0000003), a plant structure (PO:00090119), and/or a plant population to evaluate the plant response.
Revised comment: The Plant Environment Ontology describes the treatments, growing conditions, and/or study types used in various types of plant biology experiments. The subjects of the studies may also include in vitro plant structures (PO:0000004). A plant treatment is the most specific, and may be a component of a plant growing condition. The broadest classes are the plant study types where the terms can be used to identify the growth study facility. Each growth facility such as field study, growth chamber, green house etc is a environment on its own it may also involve instances of biotic and abiotic treatments and different growing conditions.
taxonomy is usually only a hierarchy of concepts (i.e. the only relation between the concepts is parent/child, or subClass/superClass, or broader/narrower)
in an ontology, arbitrary complex relations between concepts can be expressed too (X marriedTo Y; or A worksFor B; or C locatedIn D, etc )
An Ontology captures the entities, their types(classes), class hierarchy,properties and relationships between the entities. An Ontology has instances/individuals. But, if you are dealing with data , then a Knowledge Graph is a better option. The data/triples are stored in the form of nodes and edges,i.e; each data point is a node and the relationship becomes an edge that connects those nodes.
So, you can say an Ontology records the structure/schema whereas a Knowledge Graph captures the data. Moreover, an ontology has the power to enable restrictions/rules and reasoning.
Assume "a knowledge graph" is a Semantic Network. Google Knowledge Graph is a Semantic Network.
An OWL Ontology provides classification - kind-of dynamic schema - for a Semantic Network. So, in your terms, an ontology would be metadata for a knowledge graph.
Header Tags Header tags should appear in the following order: format-version data-version date saved-by auto-generated-by import subsetdef synonymtypedef default-namespace namespace-id-rule idspace treat-xrefs-as-equivalent treat-xrefs-as-genus-differentia treat-xrefs-as-relationship treat-xrefs-as-is_a remark ontology Ordering Term and Typedef stanzas [Term], [Typdef], and [Instance] stanzas should be serialized in alphabetical order on the value of their id tag. Ordering Term and Typedef tags Term tags should appear in the following order: id is_anonymous name namespace alt_id def comment subset synonym xref builtin property_value is_a intersection_of union_of equivalent_to disjoint_from relationship created_by creation_date is_obsolete replaced_by consider Typedef tags should appear in the following order: id is_anonymous name namespace alt_id def comment subset synonym xref property_value domain range builtin holds_over_chain is_anti_symmetric is_cyclic is_reflexive is_symmetric is_transitive is_functional is_inverse_functional is_a intersection_of union_of equivalent_to disjoint_from inverse_of transitive_over equivalent_to_chain disjoint_over relationship is_obsolete created_by creation_date replaced_by consider expand_assertion_to expand_expression_to is_metadata_tag is_class_level Instance tags should appear in the following order: id is_anonymous name namespace alt_id def comment subset synonym xref instance_of property_value relationship created_by creation_date is_obsolete replaced_by consider
Reference: https://owlcollab.github.io/oboformat/doc/GO.format.obo-1_4.html
网络资源:Dublin Core、IAFA Template、CDF、Web Collections 文献资料:MARC(with 856 Field),Dublic Core 人文科学:TEI Header 社会科学数据集:ICPSR SGML Codebook 博物馆与艺术作品:CIMI、CDWA、RLG REACH Element Set、VRA Core 政府信息:GILS 地理空间信息:FGDC/CSDGM 数字图像:MOA2 metadata、CDL metadata、Open Archives Format、VRA Core、NISO/CLIR/RLG Technical Metadata for Images 档案库与资源集合:EAD 技术报告:RFC 1807 连续图像:MPEG-7
Vendor | Formats | Vendor Required Software |
---|---|---|
ABI | T2D | DataExplorer 4.0 |
Agilent | MassHunter .d | distributed with ProteoWizard |
Bruker | Compass .d, YEP, BAF, FID, TDF | distributed with ProteoWizard |
Sciex | WIFF / WIFF2 | distributed with ProteoWizard |
Shimadzu | LCD | distributed with ProteoWizard |
Thermo Scientific | RAW | distributed with ProteoWizard |
Waters | MassLynx .raw / UNIFI | distributed with ProteoWizard |
These words existed way before Computer Science came around.
Attribute is a quality or object that we attribute to someone or something. For example, the scepter is an attribute of power and statehood.
Property is a quality that exists without any attribution. For example, clay has adhesive qualities; or, one of the properties of metals is electrical conductivity. Properties demonstrate themselves though physical phenomena without the need attribute them to someone or something. By the same token, saying that someone has masculine attributes is self-evident. In effect, you could say that a property is owned by someone or something.
To be fair though, in Computer Science these two words, at least for the most part, can be used interchangeably - but then again programmers usually don't hold degrees in English Literature and do not write or care much about grammar books :).
cvList contains information about the controlled vocabularies referenced in the rest of the mzML document; fileDescription contains basic information on the type of spectra contained in the file; referenceableParamGroupList is an optional element that of groups of controlled vocabulary terms that can be referenced as a unit throughout defines a list the document; sampleList can contain information about samples that are referenced in the file; instrumentConfigurationList contains information about the instrument that generated the run; softwareList and dataProcessingList provide a history of data processing that occurred after the raw acquisition; acquisitionSettingsList is an optional element that stores special input parameters for the mass spectrometer, such as inclusion lists. These elements are followed by the acquired spectra and chromatograms. Both spectral and chromatographic data are represented by binary format data encoded into base 64 strings, rather than human-readable ASCII text for enhanced fidelity and efficiency when dealing with profile data. This design choice does not enjoy unanimous approval, but has been agreed upon by the majority of designers
Martens L, Chambers M C, Sturm M, et al. mzML—a Community Standard for Mass Spectrometry Data [J]. Molecular & Cellular Proteomics, 2011, 10(1): 1-7