In information science , an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or all domains of discourse . More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to as applied ontology .
44-426: (Redirected from SPARQ ) Sparq may refer to: SPARQL , programming language SPARQCode , standard encoding for the contents of a QR barcode SPARQ Training , creators of a standardized test for athleticism SyQuest SparQ drive , a short-lived (1998–1999) removable-disk hard drive See also [ edit ] Spark (disambiguation) Topics referred to by
88-568: A SPARQL query for a SPARQL endpoint, for example ViziQuer. In addition, tools exist to translate SPARQL queries to other query languages, for example to SQL and to XQuery . SPARQL allows users to write queries that follow the RDF specification of the W3C . Thus, the entire dataset is "subject-predicate-object" triples. Subjects and predicates are always URI identifiers, but objects can be URIs or literal values. This single physical schema of 3 "columns"
132-402: A common upper ontology is a largely manual process and therefore time-consuming and expensive. Domain ontologies that use the same upper ontology to provide a set of basic elements with which to specify the meanings of the domain ontology entities can be merged with less effort. There are studies on generalized techniques for merging ontologies, but this area of research is still ongoing, and it
176-510: A language for updating the database with several new query forms. Another SPARQL query example that models the question "What are all the country capitals in Africa?": Variables are indicated by a ? or $ prefix. Bindings for ?capital and the ?country will be returned. When a triple ends with a semicolon, the subject from this triple will implicitly complete the following pair to an entire triple. So for example ex:isCapitalOf ?y
220-539: A linguistic tool for learning domain ontologies. The Gellish ontology is an example of a combination of an upper and a domain ontology. A survey of ontology visualization methods is presented by Katifori et al. An updated survey of ontology visualization methods and tools was published by Dudás et al. The most established ontology visualization methods, namely indented tree and graph visualization are evaluated by Fu et al. A visual language for ontologies represented in OWL
264-562: A list of names and emails for people who drive automobiles with a high fuel efficiency. In the case of queries that read data from the database, the SPARQL language specifies four different query variations for different purposes. Each of these query forms takes a WHERE block to restrict the query, although, in the case of the DESCRIBE query, the WHERE is optional. SPARQL 1.1 specifies
308-506: A preface to the proceedings. Some researchers, drawing inspiration from philosophical ontologies, viewed computational ontology as a kind of applied philosophy. In 1993, the widely cited web page and paper "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" by Tom Gruber used ontology as a technical term in computer science closely related to earlier idea of semantic networks and taxonomies . Gruber introduced
352-415: A range of fields, including biomedical informatics, industry. Such efforts often use ontology editing tools such as Protégé . Ontology is a branch of philosophy and intersects areas such as metaphysics , epistemology , and philosophy of language , as it considers how knowledge, language, and perception relate to the nature of reality. Metaphysics deals with questions like "what exists?" and "what
396-551: A realm of the world, such as biology or politics. Each domain ontology typically models domain-specific definitions of terms. For example, the word card has many different meanings. An ontology about the domain of poker would model the " playing card " meaning of the word, while an ontology about the domain of computer hardware would model the " punched card " and " video card " meanings. Since domain ontologies are written by different people, they represent concepts in very specific and unique ways, and are often incompatible within
440-508: A theory of a modeled world and a component of knowledge-based systems . In particular, David Powers introduced the word ontology to AI to refer to real world or robotic grounding, publishing in 1990 literature reviews emphasizing grounded ontology in association with the call for papers for a AAAI Summer Symposium Machine Learning of Natural Language and Ontology, with an expanded version published in SIGART Bulletin and included as
484-456: Is a formal, explicit specification of a shared conceptualization that is characterized by high semantic expressiveness required for increased complexity." Contemporary ontologies share many structural similarities, regardless of the language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes and relations. A domain ontology (or domain-specific ontology) represents concepts which belong to
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#1732791172710528-792: Is a recent event to see the issue sidestepped by having multiple domain ontologies using the same upper ontology like the OBO Foundry . An upper ontology (or foundation ontology) is a model of the commonly shared relations and objects that are generally applicable across a wide range of domain ontologies. It usually employs a core glossary that overarches the terms and associated object descriptions as they are used in various relevant domain ontologies. Standardized upper ontologies available for use include BFO , BORO method , Dublin Core , GFO , Cyc , SUMO , UMBEL , and DOLCE . WordNet has been considered an upper ontology by some and has been used as
572-448: Is an integrated query language combining XQuery with SPARQL to query both XML and RDF data sources at once. Open source, reference SPARQL implementations See List of SPARQL implementations for more comprehensive coverage, including triplestore , APIs , and other storages that have implemented the SPARQL standard. Ontology (information science) Every academic discipline or field, in creating its terminology, thereby lays
616-400: Is chosen, it must be the same on each line of the query to signify that the query engine is to join triples with the same subject. The result of the join is a set of rows – ?person , ?name , ?email . This query returns the ?name and ?email because ?person is often a complex URI rather than a human-friendly string. Note that any ?person may have multiple mailboxes, so in
660-431: Is common for ontology editors to use one or more ontology languages . Aspects of ontology editors include: visual navigation possibilities within the knowledge model , inference engines and information extraction ; support for modules; the import and export of foreign knowledge representation languages for ontology matching ; and the support of meta-ontologies such as OWL-S , Dublin Core , etc. Ontology learning
704-653: Is considered by some as a successor to prior work in philosophy. However many current efforts are more concerned with establishing controlled vocabularies of narrow domains than with philosophical first principles , or with questions such as the mode of existence of fixed essences or whether enduring objects (e.g., perdurantism and endurantism ) may be ontologically more primary than processes . Artificial intelligence has retained considerable attention regarding applied ontology in subfields like natural language processing within machine translation and knowledge representation , but ontology editors are being used often in
748-485: Is different from Wikidata All article disambiguation pages All disambiguation pages SPARQL SPARQL (pronounced " sparkle ", a recursive acronym for SPARQL Protocol and RDF Query Language ) is an RDF query language —that is, a semantic query language for databases —able to retrieve and manipulate data stored in Resource Description Framework (RDF) format. It
792-522: Is hyperdenormalized in that what would be 1 relational record with 4 fields is now 4 triples with the subject being repeated over and over, the predicate essentially being the column name, and the object being the field value. Although this seems unwieldy, the SPARQL syntax offers these features: 1. Subjects and Objects can be used to find the other including recursively. Below is a set of triples. It should be clear that ex:sw001 and ex:sw002 link to ex:sw003 , which itself has links: In SPARQL,
836-428: Is much more like MongoDB or SPARK. Expressions are evaluated exactly in the order they are declared including filtering and joining of data. The programming model becomes what a SQL statement would be like with multiple WHERE clauses. The combination of list-aware subjects and objects plus a pipeline approach can yield extremely expressive queries spanning many different domains of data. Unlike relational databases,
880-437: Is often provided externally, to allow joining of different datasets unambiguously. In addition, SPARQL provides specific graph traversal syntax for data that can be thought of as a graph. The example below demonstrates a simple query that leverages the ontology definition foaf ("friend of a friend"). Specifically, the following query returns names and emails of every person in the dataset : This query joins all of
924-410: Is short for ?x ex:isCapitalOf ?y . The SPARQL query processor will search for sets of triples that match these four triple patterns, binding the variables in the query to the corresponding parts of each triple. Important to note here is the "property orientation" (class matches can be conducted solely through class-attributes or properties – see Duck typing ). To make queries concise, SPARQL allows
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#1732791172710968-535: Is specified by the Visual Notation for OWL Ontologies (VOWL) . Ontology engineering (also called ontology building) is a set of tasks related to the development of ontologies for a particular domain. It is a subfield of knowledge engineering that studies the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies, and the tools and languages that support them. Ontology engineering aims to make explicit
1012-530: Is the attempt to represent entities, including both objects and events, with all their interdependent properties and relations, according to a system of categories. In both fields, there is considerable work on problems of ontology engineering (e.g., Quine and Kripke in philosophy, Sowa and Guarino in information science), and debates concerning to what extent normative ontology is possible (e.g., foundationalism and coherentism in philosophy, BFO and Cyc in artificial intelligence). Applied ontology
1056-590: Is the automatic or semi-automatic creation of ontologies, including extracting a domain's terms from natural language text. As building ontologies manually is extremely labor-intensive and time-consuming, there is great motivation to automate the process. Information extraction and text mining have been explored to automatically link ontologies to documents, for example in the context of the BioCreative challenges. Epistemological assumptions, which in research asks "What do you know? or "How do you know it?", creates
1100-424: Is the nature of reality?". One of five traditional branches of philosophy, metaphysics is concerned with exploring existence through properties, entities and relations such as those between particulars and universals , intrinsic and extrinsic properties , or essence and existence . Metaphysics has been an ongoing topic of discussion since recorded history. The compound word ontology combines onto - , from
1144-515: The Greek ὄν , on ( gen. ὄντος, ontos ), i.e. "being; that which is", which is the present participle of the verb εἰμί , eimí , i.e. "to be, I am", and -λογία , -logia , i.e. "logical discourse", see classical compounds for this type of word formation. While the etymology is Greek, the oldest extant record of the word itself, the Neo-Latin form ontologia , appeared in 1606 in
1188-503: The definition and ontology of economics is a primary concern in Marxist economics , but also in other subfields of economics . An example of economics relying on information science occurs in cases where a simulation or model is intended to enable economic decisions, such as determining what capital assets are at risk and by how much (see risk management ). What ontologies in both information science and philosophy have in common
1232-498: The subsumption relation , but ontologies need not be limited to these forms. Ontologies are also not limited to conservative definitions – that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world. To specify a conceptualization, one needs to state axioms that do constrain the possible interpretations for the defined terms. As refinement of Gruber's definition Feilmayr and Wöß (2016) stated: "An ontology
1276-454: The behavior is reversed. This will produce all the things upon which ex:sw003 depends (downstream dependency): Even more attractive is that we can easily instruct SPARQL to recursively follow the path: Bound variables can therefore also be lists and will be operated upon without complicated syntax. The effect of this is similar to the following: 2. SPARQL expressions are a pipeline Unlike SQL which has subqueries and CTEs, SPARQL
1320-537: The definition of prefixes and base URIs in a fashion similar to Turtle . In this query, the prefix " ex " stands for “ http://example.com/exampleOntology# ”. GeoSPARQL defines filter functions for geographic information system (GIS) queries using well-understood OGC standards ( GML , WKT , etc.). SPARUL is another extension to SPARQL. It enables the RDF store to be updated with this declarative query language, by adding INSERT and DELETE methods. XSPARQL
1364-450: The field of artificial intelligence (AI) have recognized that knowledge engineering is the key to building large and powerful AI systems . AI researchers argued that they could create new ontologies as computational models that enable certain kinds of automated reasoning , which was only marginally successful . In the 1980s, the AI community began to use the term ontology to refer to both
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1408-460: The first time a variable is encountered in the expression pipeline, it is populated with result. The second and subsequent times it is seen, it is used as an input. If we assign ("bind") the URI ex:sw003 to the ?targets variable, then it drives a result into ?src ; this tells us all the things that link to ex:sw003 (upstream dependency): But with a simple switch of the binding variable,
1452-426: The foundation researchers use when approaching a certain topic or area for potential research. As epistemology is directly linked to knowledge and how we come about accepting certain truths, individuals conducting academic research must understand what allows them to begin theory building. Simply, epistemological assumptions force researchers to question how they arrive at the knowledge they have. An ontology language
1496-458: The groundwork for an ontology. Each uses ontological assumptions to frame explicit theories, research and applications. Improved ontologies may improve problem solving within that domain, interoperability of data systems, and discoverability of data. Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages. For instance,
1540-419: The knowledge contained in software applications, and organizational procedures for a particular domain. Ontology engineering offers a direction for overcoming semantic obstacles, such as those related to the definitions of business terms and software classes. Known challenges with ontology engineering include: Ontology editors are applications designed to assist in the creation or manipulation of ontologies. It
1584-465: The object column is heterogeneous: the object data type, if not an URI, is usually implied (or specified in the ontology ) by the predicate value. Literal nodes carry type information consistent with the underlying XSD namespace including signed and unsigned short and long integers, single and double precision floats, datetime, penny-precise decimal, Boolean, and string. Triple store implementations on traditional relational databases will typically store
1628-476: The returned set, a ?name row may appear multiple times, once for each mailbox. This query can be distributed to multiple SPARQL endpoints (services that accept SPARQL queries and return results), computed, and results gathered, a procedure known as federated query . Whether in a federated manner or locally, additional triple definitions in the query could allow joins to different subject types, such as automobiles, to allow simple queries, for example, to return
1672-528: The same project. As systems that rely on domain ontologies expand, they often need to merge domain ontologies by hand-tuning each entity or using a combination of software merging and hand-tuning. This presents a challenge to the ontology designer. Different ontologies in the same domain arise due to different languages, different intended usage of the ontologies, and different perceptions of the domain (based on cultural background, education, ideology, etc.) . At present, merging ontologies that are not developed from
1716-406: The same term [REDACTED] This disambiguation page lists articles associated with the title Sparq . If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=Sparq&oldid=995860142 " Category : Disambiguation pages Hidden categories: Short description
1760-652: The term as a specification of a conceptualization : An ontology is a description (like a formal specification of a program) of the concepts and relationships that can formally exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set of concept definitions, but more general. And it is a different sense of the word than its use in philosophy. Attempting to distance ontologies from taxonomies and similar efforts in knowledge modeling that rely on classes and inheritance , Gruber stated (1993): Ontologies are often equated with taxonomic hierarchies of classes, class definitions, and
1804-481: The triples with a matching subject, where the type predicate, " a ", is a person ( foaf:Person ), and the person has one or more names ( foaf:name ) and mailboxes ( foaf:mbox ). For the sake of readability, the author of this query chose to reference the subject using the variable name " ?person ". Since the first element of the triple is always the subject, the author could have just as easily used any variable name, such as " ?subj " or " ?x ". Whatever name
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1848-438: The value as a string and a fourth column will identify the real type. Polymorphic databases such as MongoDB and SQLite can store the native value directly into the object field. Thus, SPARQL provides a full set of analytic query operations such as JOIN , SORT , AGGREGATE for data whose schema is intrinsically part of the data rather than requiring a separate schema definition. However, schema information (the ontology)
1892-643: The work Ogdoas Scholastica by Jacob Lorhard ( Lorhardus ) and in 1613 in the Lexicon philosophicum by Rudolf Göckel ( Goclenius ). The first occurrence in English of ontology as recorded by the OED ( Oxford English Dictionary , online edition, 2008) came in Archeologia Philosophica Nova or New Principles of Philosophy by Gideon Harvey . Since the mid-1970s, researchers in
1936-767: Was made a standard by the RDF Data Access Working Group (DAWG) of the World Wide Web Consortium , and is recognized as one of the key technologies of the semantic web . On 15 January 2008, SPARQL 1.0 was acknowledged by W3C as an official recommendation, and SPARQL 1.1 in March, 2013. SPARQL allows for a query to consist of triple patterns , conjunctions , disjunctions , and optional patterns . Implementations for multiple programming languages exist. There exist tools that allow one to connect and semi-automatically construct
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