Semantic integration

From Wikipedia, the free encyclopedia

Semantic integration is the process of interrelating information from diverse sources, for example calendars and to do lists, email archives, presence information (physical, psychological, and social), documents of all sorts, contacts (including social graphs), search results, and advertising and marketing relevance derived from them. In this regard, semantics focuses on the organization of and action upon information by acting as an intermediary between heterogeneous data sources, which may conflict not only by structure but also context or value.

Applications and methods

In enterprise application integration (EAI), semantic integration can facilitate or even automate the communication between computer systems using metadata publishing. Metadata publishing potentially offers the ability to automatically link ontologies. One approach to (semi-)automated ontology mapping requires the definition of a semantic distance or its inverse, semantic similarity and appropriate rules. Other approaches include so-called lexical methods, as well as methodologies that rely on exploiting the structures of the ontologies. For explicitly stating similarity/equality, there exist special properties or relationships in most ontology languages. OWL, for example has "owl:equivalentClass", "owl:equivalentProperty" and "owl:sameAs".

Eventually system designs may see the advent of composable architectures where published semantic-based interfaces are joined together to enable new and meaningful capabilities. These could predominately be described by means of design-time declarative specifications, that could ultimately be rendered and executed at run-time.

Semantic integration can also be used to facilitate design-time activities of interface design and mapping. In this model, semantics are only explicitly applied to design and the run-time systems work at the syntax level. This "early semantic binding" approach can improve overall system performance while retaining the benefits of semantic driven design.

Semantic integration situations

From the industry use case, it has been observed that the semantic mappings were performed only within the scope of the ontology class or the datatype property. These identified semantic integrations are (1) integration of ontology class instances into another ontology class without any constraint, (2) integration of selected instances in one ontology class into another ontology class by the range constraint of the property value and (3) integration of ontology class instances into another ontology class with the value transformation of the instance property. Each of them requires a particular mapping relationship, which is respectively: (1) equivalent or subsumption mapping relationship, (2) conditional mapping relationship that constraints the value of property (data range) and (3) transformation mapping relationship that transforms the value of property (unit transformation). Each identified mapping relationship can be defined as either (1) direct mapping type, (2) data range mapping type or (3) unit transformation mapping type.

KG vs. RDB approaches

In the case of integrating supplemental data source,

• KG(Knowledge graph) formally represents the meaning involved in information by describing concepts, relationships between things, and categories of things. These embedded semantics with the data offer significant advantages such as reasoning over data and dealing with heterogeneous data sources. The rules can be applied on KG more efficiently using graph query. For example, the graph query does the data inference through the connected relations, instead of repeated full search of the tables in relational database. KG facilitates the integration of new heterogeneous data by just adding new relationships between existing information and new entities. This facilitation is emphasized for the integration with existing popular linked open data source such as Wikidata.org.
• SQL query is tightly coupled and rigidly constrained by datatype within the specific database and can join tables and extract data from tables, and the result is generally a table, and a query can join tables by any columns which match by datatype. SPARQL query is the standard query language and protocol for Linked Open Data on the web and loosely coupled with the database so that it facilitates the reusability and can extract data through the relations free from the datatype, and not only extract but also generate additional knowledge graph with more sophisticated operations(logic: transitive/symmetric/inverseOf/functional). The inference based query (query on the existing asserted facts without the generation of new facts by logic) can be fast comparing to the reasoning based query (query on the existing plus the generated/discovered facts based on logic).
• The information integration of heterogeneous data sources in traditional database is intricate, which requires the redesign of the database table such as changing the structure and/or addition of new data. In the case of semantic query, SPARQL query reflects the relationships between entities in a way that aligned with human's understanding of the domain, so the semantic intention of the query can be seen on the query itself. Unlike SPARQL, SQL query, which reflects the specific structure of the database and derived from matching the relevant primary and foreign keys of tables, loses the semantics of the query by missing the relationships between entities. Below is the example that compares SPARQL and SQL queries for medications that treats "TB of vertebra".

SELECT ?medication
WHERE {
?diagnosis a example:Diagnosis .
?diagnosis example:name “TB of vertebra” .
?medication example:canTreat ?diagnosis .
}

SELECT DRUG.medID
FROM DIAGNOSIS, DRUG, DRUG_DIAGNOSIS
WHERE DIAGNOSIS.diagnosisID=DRUG_DIAGNOSIS.diagnosisID
AND DRUG.medID=DRUG_DIAGNOSIS.medID
AND DIAGNOSIS.name=”TB of vertebra”

Examples

The Pacific Symposium on Biocomputing has been a venue for the popularization of the ontology mapping task in the biomedical domain, and a number of papers on the subject can be found in its proceedings.

Open Semantic Framework

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Open_Semantic_Framework 

 

Open Semantic Framework

Developer(s)	Structured Dynamics
Initial release	June 2009
Stable release	OSF v 3.4 / March 2016
Repository	github.com/structureddynamics/OSF-Web-Services
Written in	PHP Java Clojure
Operating system	platform independent
Type	Semantic Web Linked data Knowledge management
License	Apache 2
Website	opensemanticframework.org

The Open Semantic Framework (OSF) is an integrated software stack using semantic technologies for knowledge management. It has a layered architecture that combines existing open source software with additional open source components developed specifically to provide a complete Web application framework. OSF is made available under the Apache 2 license.

OSF is a platform-independent Web services framework for accessing and exposing structured data, semi-structured data, and unstructured data using ontologies to reconcile semantic heterogeneities within the contributing data and schema. Internal to OSF, all data is converted to RDF to provide a common data model. The OWL 2 ontology language is used to describe the data schema overlaying all of the constituent data sources.

The architecture of OSF is built around a central layer of RESTful web services, designed to enable most constituent modules within the software stack to be substituted without major adverse impacts on the entire stack. A central organizing perspective of OSF is that of the dataset. These datasets contain the records in any given OSF instance. One or more domain ontologies is used by a given OSF instance to define the structural relationships amongst the data and their attributes and concepts.

Some of the use applications for OSF include local government, health information systems, community indicator systems, eLearning, citizen engagement, or any domain that may be modeled by ontologies.

Documentation and training videos are provided with the open-source OSF application.

History

Early components of OSF were provided under the names of structWSF and conStruct starting in June 2009. The first version 1.x of OSF was announced in August 2010. The first automated OSF installeer was released in March 2012. OSF was expanded with an ontology manager, structOntology in August 2012. The version 2.x developments of OSF occurred for enterprise sponsors in the period of early 2012 until the end of 2013. None of these interim 2.x versions were released to the public. Then, at the conclusion of this period, Structured Dynamics, the main developer of OSF, refactored these specific enterprise developments to leapfrog to a new version 3.0 of OSF, announced in early 2014. These public releases were last updated to OSF version 3.4.0 in August 2016.

Architecture and technologies

OSF simple stack architecture

 

The Open Semantic Framework has a basic three-layer architecture. User interactions and content management are provided by an external content management system, which is currently Drupal (but does not depend on it). This layer accesses the pivotal OSF Web Services; there are now more than 20 providing OSF's distributed computing functionality. Full CRUD access and user permissions and security is provided to all digital objects in the stack. This middleware layer then provides a means to access the third layer, the engines and indexers that drive the entire stack. Both the top CMS layer and the engines layer are provided by existing off-the-shelf software. What makes OSF a complete stack are the connecting scripts and the intermediate Web services layer.

The premise of the OSF stack is based on the RDF data model. RDF provides the means for integrating existing structured data assets in any format, with semi-structured data like XML and HTML, and unstructured documents or text. The OSF framework is made operational via ontologies that capture the domain or knowledge space, matched with internal ontologies that guide OSF operations and data display. This design approach is known as ODapps, for ontology-driven applications.

Content management layer

OSF delegates all direct user interactions and standard content management to an external CMS. In the case of Drupal, this integration is tighter, and supports connectors and modules that can replace standard Drupal storage and databases with OSF triplestores.

Web services layer

This intermediate OSF Web Services layer may also be accessed directly via API or command line or utilities like cURL, suitable for interfacing with standard content management systems (CMSs), or via a dedicated suite of connectors and modules that leverage the open source Drupal CMS. These connectors and modules, also part of the standard OSF stack and called OSF for Drupal, natively enable Drupal's existing thousands of modules and ecosystem of developers and capabilities to access OSF using familiar Drupal methods.

The OSF middleware framework is generally RESTful in design and is based on HTTP and Web protocols and W3C open standards. The initial OSF framework comes packaged with a baseline set of more than 20 Web services in CRUD, browse, search, tagging, ontology management, and export and import. All Web services are exposed via APIs and SPARQL endpoints. Each request to an individual Web service returns an HTTP status and optionally a document of resultsets. Each results document can be serialized in many ways, and may be expressed as either RDF, pure XML, JSON, or other formats.

Engines layer

The engines layer represents the major workflow requirements and data management and indexing of the system. The premise of the Open Semantic Framework is based on the RDF data model. Using a common data model means that all Web services and actions against the data only need to be programmed via a single, canonical form. Simple converters convert external, native data formats to the RDF form at time of ingest; similar converters can translate the internal RDF form back into native forms for export (or use by external applications). This use of a canonical form leads to a simpler design at the core of the stack and a uniform basis to which tools or other work activities can be written.

The OSF engines are all open source and work to support this premise. The OSF engines layer governs the index and management of all OSF content. Documents are indexed by the Solr engine for full-text search, while information about their structural characteristics and metadata are stored in an RDF triplestore database provided by OpenLink's Virtuoso software. The schema aspects of the information (the "ontologies") are separately managed and manipulated with their own W3C standard application, the OWL API. At ingest time, the system automatically routes and indexes the content into its appropriate stores. Another engine, GATE (General Architecture for Text Engineering), provides semi-automatic assistance in tagging input information and other natural language processing (NLP) tasks.

Alternatives

OSF is sometimes referred to as a linked data application. Alternative applications in this space include:

• Callimachus
• CubicWeb
• LOD2 Stack
• Apache Marmotta

The Open Semantic Framework also has alternatives in the semantic publishing and semantic computing arenas.

Ontology (information science)

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Ontology_(information_science) 

In computer science and information science, an ontology encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities that substantiate 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 concepts and categories that represent the subject.

Every academic discipline or field creates ontologies to limit complexity and organize data into information and knowledge. New ontologies improve problem solving within that domain. 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.

Etymology

The compound word ontology combines onto-, from 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 New Latin form ontologia, appeared in 1606 in 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.

Overview

What ontologies in both information science and philosophy have in common is the attempt to represent entities, ideas 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 computer 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 is considered a spiritual successor to prior work in philosophy, however many current efforts are more concerned with establishing controlled vocabularies of narrow domains than first principles, the existence of fixed essences or whether enduring objects (e.g., perdurantism and endurantism) may be ontologically more primary than processes.

Every field uses ontological assumptions to frame explicit theories, research and applications. For instance, the definition and ontology of economics is a primacy 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). 

Artificial intelligence has retained the most attention regarding applied ontology in subfields like natural language processing within machine translation and knowledge representation, but ontology editors are being used often in a range of fields like education without the intent to contribute to AI.

History

Ontologies arise out of the branch of philosophy known as metaphysics, which deals with questions like "what exists?" and "what 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.

Since the mid-1970s, researchers in 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 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 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 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 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 is a formal, explicit specification of a shared conceptualization that is characterized by high semantic expressiveness required for increased complexity."

Components

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. In this section each of these components is discussed in turn.

Common components of ontologies include:

Individuals

Instances or objects (the basic or "ground level" objects)

Classes

Sets, collections, concepts, classes in programming, types of objects or kinds of things

Attributes

Aspects, properties, features, characteristics or parameters that objects (and classes) can have

Relations

Ways in which classes and individuals can be related to one another

Function terms

Complex structures formed from certain relations that can be used in place of an individual term in a statement

Restrictions

Formally stated descriptions of what must be true in order for some assertion to be accepted as input

Rules

Statements in the form of an if-then (antecedent-consequent) sentence that describe the logical inferences that can be drawn from an assertion in a particular form

Axioms

Assertions (including rules) in a logical form that together comprise the overall theory that the ontology describes in its domain of application. This definition differs from that of "axioms" in generative grammar and formal logic. In those disciplines, axioms include only statements asserted as a priori knowledge. As used here, "axioms" also include the theory derived from axiomatic statements

Events

The changing of attributes or relations

Ontologies are commonly encoded using ontology languages.

Types

Domain ontology

A domain ontology (or domain-specific ontology) represents concepts which belong to 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 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 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's a recent event to see the issue sidestepped by having multiple domain ontologies using the same upper ontology like the OBO Foundry.

Upper ontology

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, the Unified Foundational Ontology (UFO), and DOLCE. WordNet has been considered an upper ontology by some and has been used as a linguistic tool for learning domain ontologies.

Hybrid ontology

The Gellish ontology is an example of a combination of an upper and a domain ontology.

Visualization

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 is specified by the Visual Notation for OWL Ontologies (VOWL).

Engineering

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 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:

1. Ensuring the ontology is current with domain knowledge and term use
2. Providing sufficient specificity and concept coverage for the domain of interest, thus minimizing the content completeness problem
3. Ensuring the ontology can support its use cases

Editors

Ontology editors are applications designed to assist in the creation or manipulation of ontologies. It 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.

Name	Written in	License	Features	Publisher/creator
a.k.a. software			Ontology, taxonomy and thesaurus management software	The Synercon Group
Anzo for Excel			Includes an RDFS and OWL ontology editor within Excel; generates ontologies from Excel spreadsheets	Cambridge Semantics
Be Informed Suite		Commercial	tool for building large ontology based applications. Includes visual editors, inference engines, export to standard formats
CENtree	Java	Commercial	Web based client-server ontology management tool for life sciences, supports OWL, RDFS, OBO	SciBite
Chimaera			Other web service	Stanford University
CmapTools	Java based		Ontology Editor (COE) ontology editor Supports numerous formats	Florida Institute for Human and Machine Cognition
dot15926 Editor	Python?	Open source	ontology editor for data compliant to engineering ontology standard ISO 15926. Allows Python scripting and pattern-based data analysis. Supports extensions.
EMFText OWL2 Manchester Editor	Eclipse-based	open-source	Pellet integration
Enterprise Architect			along with UML modeling, supports OMG's Ontology Definition MetaModel which includes OWL and RDF	Sparx Systems
Fluent Editor			ontology editor for OWL and SWRL with Controlled Natural Language (Controlled English). Supports OWL, RDF, DL and Functional rendering, unlimited imports and built-in reasoning services.
Gra.fo		Free and Commercial	A visual, collaborative and real time ontology and knowledge graph schema editor. Features include sharing documents, commenting, search and tracking history. Support W3C Semantic Web standards: RDF, RDFS, OWL and also Property Graph schemas.	Capsenta
HOZO	Java		graphical editor especially created to produce heavy-weight and well thought out ontologies	Osaka University and Enegate Co, ltd.
Java Ontology Editor (JOE)[35]	Java		Can be used to create and browse ontologies, and construct ontology based queries. Incorporates abstraction mechanisms that enable users to manage large ontologies	Center for Information Technology, Department of Electrical and Computer Engineering, University of South Carolina
KAON		open source	single user and server based solutions possible	FZI/AIFB Karlsruhe
KMgen			Ontology editor for the KM language. km: The Knowledge Machine
Knoodl		Free	web application/service that is an ontology editor, wiki, and ontology registry. Supports creation of communities where members can collaboratively import, create, discuss, document and publish ontologies. Supports OWL, RDF, RDFS, and SPARQL queries.	Revelytix, Inc..
Menthor Editor			An ontology engineering tool for dealing with OntoUML. It also includes OntoUML syntax validation, Alloy simulation, Anti-Pattern verification, and transformations from OntoUML to OWL, SBVR and Natural Language (Brazilian Portuguese)
Model Futures IDEAS AddIn		free	A plug-in for Enterprise Architect] that allows IDEAS Group 4D ontologies to be developed using a UML profile
Model Futures OWL Editor		Free	Able to work with very large OWL files (e.g. Cyc) and has extensive import and export capabilities (inc. UML, Thesaurus Descriptor, MS Word, CA ERwin Data Modeler, CSV, etc.)
myWeb	Java		mySQL connection, bundled with applet that allows online browsing of ontologies (including OBO)
Neologism	built on Drupal	open source	Web-based, supports RDFS and a subset of OWL
NeOn Toolkit	Eclipse-based	open source	OWL support, several import mechanisms, support for reuse and management of networked ontologies, visualization, etc.	NeOn Project
OBIS			Web based user interface that allows users to input ontology instances that can be accessed via SPARQL endpoint
OBO-Edit	Java	open source	downloadable, developed by the Gene Ontology Consortium for editing biological ontologies. OBO-Edit is no longer actively developed	Gene Ontology Consortium
Ontosight		Free and Commercial	Machine learning-based auto-scaling biomedical ontology combining all public biomedical ontologies	Innoplexus
OntoStudio	Eclipse	downloadable, support for RDF(S), OWL and ObjectLogic (derived from F-Logic), graphical rule editor, visualizations	semafora systems
Ontolingua			Web service	Stanford University
ONTOLIS		Commercial	Collaborative web application for managing ontologies and knowledge engineering, web-browser-based graphical rules editor, sophisticated search and export interface. Web service available to link ontology information to existing data	ONTOLIS
Open Semantic Framework (OSF)			an integrated software stack using semantic technologies for knowledge management, which includes an ontology editor
OWLGrEd			A graphical ontology editor, easy-to-use
PoolParty Thesaurus Server		Commercial	ontology, taxonomy and thesaurus management software, fully based on standards like RDFS, SKOS and SPARQL, integrated with Virtuoso Universal Server	Semantic Web Company
Protégé	Java	open source	downloadable, supports OWL, many sample ontologies	Stanford University
ScholOnto			net-centric representations of research
Semantic Turkey	Firefox extension - based on Java		for managing ontologies and acquiring new knowledge from the Web	developed at University of Rome, Tor Vergata
Sigma knowledge engineering environment			is a system primarily for development of the Suggested Upper Merged Ontology
Swoop	Java	open source	downloadable, OWL Ontology browser and editor	University of Maryland
Semaphore Ontology Manager		Commercial	ontology, taxonomy and thesaurus management software. Tool to manage the entire "build - enhance - review - maintain" ontology lifecycle.	Smartlogic Semaphore Limited
Synaptica			Ontology, taxonomy and thesaurus management software. Web based, supports OWL and SKOS.	Synaptica, LLC.
TopBraid Composer	Eclipse-based		downloadable, full support for RDFS and OWL, built-in inference engine, SWRL editor and SPARQL queries, visualization, import of XML and UML	TopQuadrant
Transinsight			Editor especially designed for creating text mining ontologies and part of GoPubMed.org
WebODE			Web service	Technical University of Madrid
TwoUse Toolkit	Eclipse-based	open source	model-driven ontology editing environment especially designed for software engineers
Thesaurus Master			Manages creation and use of ontologies for use in data management and semantic enrichment by enterprise, government, and scholarly publishers.
TODE	.Net		Tool for Ontology Development and Editing
VocBench			Collaborative Web Platform for Management of SKOS thesauri, OWL ontologies and OntoLex lexicons, now in its third incarnation supported by the ISA2 program of the EU	originally developed on a joint effort between University of Rome Tor Vergata and the Food and the Agriculture Organization of the United Nations: FAO

Learning

Ontology learning 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.

Languages

An ontology language is a formal language used to encode an ontology. There are a number of such languages for ontologies, both proprietary and standards-based:

• Common Algebraic Specification Language is a general logic-based specification language developed within the IFIP working group 1.3 "Foundations of System Specifications" and is a de facto standard language for software specifications. It is now being applied to ontology specifications in order to provide modularity and structuring mechanisms.
• Common logic is ISO standard 24707, a specification of a family of ontology languages that can be accurately translated into each other.
• The Cyc project has its own ontology language called CycL, based on first-order predicate calculus with some higher-order extensions.
• DOGMA (Developing Ontology-Grounded Methods and Applications) adopts the fact-oriented modeling approach to provide a higher level of semantic stability.
• The Gellish language includes rules for its own extension and thus integrates an ontology with an ontology language.
• IDEF5 is a software engineering method to develop and maintain usable, accurate, domain ontologies.
• KIF is a syntax for first-order logic that is based on S-expressions. SUO-KIF is a derivative version supporting the Suggested Upper Merged Ontology.
• MOF and UML are standards of the OMG
• Olog is a category theoretic approach to ontologies, emphasizing translations between ontologies using functors.
• OBO, a language used for biological and biomedical ontologies.
• OntoUML is an ontologically well-founded profile of UML for conceptual modeling of domain ontologies.
• OWL is a language for making ontological statements, developed as a follow-on from RDF and RDFS, as well as earlier ontology language projects including OIL, DAML, and DAML+OIL. OWL is intended to be used over the World Wide Web, and all its elements (classes, properties and individuals) are defined as RDF resources, and identified by URIs.
• Rule Interchange Format (RIF) and F-Logic combine ontologies and rules.
• Semantic Application Design Language (SADL) captures a subset of the expressiveness of OWL, using an English-like language entered via an Eclipse Plug-in.
• SBVR (Semantics of Business Vocabularies and Rules) is an OMG standard adopted in industry to build ontologies.
• TOVE Project, TOronto Virtual Enterprise project

Published examples

• Arabic Ontology, a linguistic ontology for Arabic, which can be used as an Arabic Wordnet but with ontologically-clean content.
• AURUM - Information Security Ontology, An ontology for information security knowledge sharing, enabling users to collaboratively understand and extend the domain knowledge body. It may serve as a basis for automated information security risk and compliance management.
• BabelNet, a very large multilingual semantic network and ontology, lexicalized in many languages
• Basic Formal Ontology, a formal upper ontology designed to support scientific research
• BioPAX, an ontology for the exchange and interoperability of biological pathway (cellular processes) data
• BMO, an e-Business Model Ontology based on a review of enterprise ontologies and business model literature
• SSBMO, a Strongly Sustainable Business Model Ontology based on a review of the systems based natural and social science literature (including business). Includes critique of and significant extensions to the Business Model Ontology (BMO).
• CCO and GexKB, Application Ontologies (APO) that integrate diverse types of knowledge with the Cell Cycle Ontology (CCO) and the Gene Expression Knowledge Base (GexKB)
• CContology (Customer Complaint Ontology), an e-business ontology to support online customer complaint management
• CIDOC Conceptual Reference Model, an ontology for cultural heritage
• COSMO, a Foundation Ontology (current version in OWL) that is designed to contain representations of all of the primitive concepts needed to logically specify the meanings of any domain entity. It is intended to serve as a basic ontology that can be used to translate among the representations in other ontologies or databases. It started as a merger of the basic elements of the OpenCyc and SUMO ontologie5s, and has been supplemented with other ontology elements (types, relations) so as to include representations of all of the words in the Longman dictionary defining vocabulary.
• Cyc, a large Foundation Ontology for formal representation of the universe of discourse
• Disease Ontology, designed to facilitate the mapping of diseases and associated conditions to particular medical codes
• DOLCE, a Descriptive Ontology for Linguistic and Cognitive Engineering
• Drammar, ontology of drama
• Dublin Core, a simple ontology for documents and publishing
• Financial Industry Business Ontology (FIBO), a business conceptual ontology for the financial industry
• Foundational, Core and Linguistic Ontologies
• Foundational Model of Anatomy, an ontology for human anatomy
• Friend of a Friend, an ontology for describing persons, their activities and their relations to other people and objects
• Gene Ontology for genomics
• Gellish English dictionary, an ontology that includes a dictionary and taxonomy that includes an upper ontology and a lower ontology that focusses on industrial and business applications in engineering, technology and procurement.
• Geopolitical ontology, an ontology describing geopolitical information created by Food and Agriculture Organization(FAO). The geopolitical ontology includes names in multiple languages (English, French, Spanish, Arabic, Chinese, Russian and Italian); maps standard coding systems (UN, ISO, FAOSTAT, AGROVOC, etc.); provides relations among territories (land borders, group membership, etc.); and tracks historical changes. In addition, FAO provides web services of geopolitical ontology and a module maker to download modules of the geopolitical ontology into different formats (RDF, XML, and EXCEL). See more information at FAO Country Profiles.
• GAO (General Automotive Ontology) - an ontology for the automotive industry that includes 'car' extensions
• GOLD, General Ontology for Linguistic Description
• GUM (Generalized Upper Model), a linguistically motivated ontology for mediating between clients systems and natural language technology
• IDEAS Group, a formal ontology for enterprise architecture being developed by the Australian, Canadian, UK and U.S. Defence Depts.
• Linkbase, a formal representation of the biomedical domain, founded upon Basic Formal Ontology.
• LPL, Landmark Pattern Language
• NCBO Bioportal, biological and biomedical ontologies and associated tools to search, browse and visualise
• NIFSTD Ontologies from the Neuroscience Information Framework: a modular set of ontologies for the neuroscience domain.
• OBO-Edit, an ontology browser for most of the Open Biological and Biomedical Ontologies
• OBO Foundry, a suite of interoperable reference ontologies in biology and biomedicine
• OMNIBUS Ontology, an ontology of learning, instruction, and instructional design
• Ontology for Biomedical Investigations, an open-access, integrated ontology of biological and clinical investigations
• ONSTR, Ontology for Newborn Screening Follow-up and Translational Research, Newborn Screening Follow-up Data Integration Collaborative, Emory University, Atlanta.
• Plant Ontology for plant structures and growth/development stages, etc.
• POPE, Purdue Ontology for Pharmaceutical Engineering
• PRO, the Protein Ontology of the Protein Information Resource, Georgetown University
• ProbOnto, knowledge base and ontology of probability distributions.
• Program abstraction taxonomy
• Protein Ontology for proteomics
• RXNO Ontology, for name reactions in chemistry
• Sequence Ontology, for representing genomic feature types found on biological sequences
• SNOMED CT (Systematized Nomenclature of Medicine—Clinical Terms)
• Suggested Upper Merged Ontology, a formal upper ontology
• Systems Biology Ontology (SBO), for computational models in biology
• SWEET, Semantic Web for Earth and Environmental Terminology
• ThoughtTreasure ontology
• TIME-ITEM, Topics for Indexing Medical Education
• Uberon, representing animal anatomical structures
• UMBEL, a lightweight reference structure of 20,000 subject concept classes and their relationships derived from OpenCyc
• WordNet, a lexical reference system
• YAMATO, Yet Another More Advanced Top-level Ontology

The W3C Linking Open Data community project coordinates attempts to converge different ontologies into worldwide Semantic Web.

Libraries

The development of ontologies has led to the emergence of services providing lists or directories of ontologies called ontology libraries. 

The following are libraries of human-selected ontologies.

• COLORE is an open repository of first-order ontologies in Common Logic with formal links between ontologies in the repository.
• DAML Ontology Library maintains a legacy of ontologies in DAML.
• Ontology Design Patterns portal is a wiki repository of reusable components and practices for ontology design, and also maintains a list of exemplary ontologies.
• Protégé Ontology Library contains a set of OWL, Frame-based and other format ontologies.
• SchemaWeb is a directory of RDF schemata expressed in RDFS, OWL and DAML+OIL.

The following are both directories and search engines.

• OBO Foundry is a suite of interoperable reference ontologies in biology and biomedicine.
• Bioportal (ontology repository of NCBO)
• OntoSelect Ontology Library offers similar services for RDF/S, DAML and OWL ontologies.
• Ontaria is a "searchable and browsable directory of semantic web data" with a focus on RDF vocabularies with OWL ontologies. (NB Project "on hold" since 2004).
• Swoogle is a directory and search engine for all RDF resources available on the Web, including ontologies.
• Open Ontology Repository initiative
• ROMULUS is a foundational ontology repository aimed at improving semantic interoperability. Currently there are three foundational ontologies in the repository: DOLCE, BFO and GFO.

Examples of applications

In general, ontologies can be used beneficially in several fields.

• Enterprise applications. A more concrete example is SAPPHIRE (Health care) or Situational Awareness and Preparedness for Public Health Incidences and Reasoning Engines which is a semantics-based health information system capable of tracking and evaluating situations and occurrences that may affect public health.
• Geographic information systems bring together data from different sources and benefit therefore from ontological metadata which helps to connect the semantics of the data.
• Domain-specific ontologies are extremely important in biomedical research, which requires named entity disambiguation of various biomedical terms and abbreviations that have the same string of characters but represent different biomedical concepts. For example, CSF can represent Colony Stimulating Factor or Cerebral Spinal Fluid, both of which are represented by the same term, CSF, in biomedical literature. This is why a large number of public ontologies are related to the life sciences. Life science data science tools that fail to implement these types of biomedical ontologies will not be able to accurately determine causal relationships between concepts.

Taxonomy (general)

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Taxonomy_(general) 

 

Taxonomy is the practice and science of categorization based on discrete sets. The word is also used as a count noun: a taxonomy, or taxonomic scheme, is a particular categorisation. The word finds its roots in the Greek language τάξις, taxis (meaning 'order', 'arrangement') and νόμος, nomos ('law' or 'science'). Originally, taxonomy referred only to the categorisation of organisms or a particular categorisation of organisms. In a wider, more general sense, it may refer to a categorisation of things or concepts, as well as to the principles underlying such a categorisation. Taxonomy is different from meronomy, which is dealing with the categorisation of parts of a whole.

Many taxonomies have a hierarchical structure, but this is not a requirement. Taxonomy uses taxonomic units, known as "taxa" (singular "taxon").

Applications

Wikipedia categories illustrate a taxonomy, and a full taxonomy of Wikipedia categories can be extracted by automatic means. As of 2009, it has been shown that a manually-constructed taxonomy, such as that of computational lexicons like WordNet, can be used to improve and restructure the Wikipedia category taxonomy.

In a broader sense, taxonomy also applies to relationship schemes other than parent-child hierarchies, such as network structures. Taxonomies may then include a single child with multi-parents, for example, "Car" might appear with both parents "Vehicle" and "Steel Mechanisms"; to some however, this merely means that 'car' is a part of several different taxonomies. A taxonomy might also simply be organization of kinds of things into groups, or an alphabetical list; here, however, the term vocabulary is more appropriate. In current usage within knowledge management, taxonomies are considered narrower than ontologies since ontologies apply a larger variety of relation types.

Mathematically, a hierarchical taxonomy is a tree structure of classifications for a given set of objects. It is also named containment hierarchy. At the top of this structure is a single classification, the root node, that applies to all objects. Nodes below this root are more specific classifications that apply to subsets of the total set of classified objects. The progress of reasoning proceeds from the general to the more specific.

By contrast, in the context of legal terminology, an open-ended contextual taxonomy is employed—a taxonomy holding only with respect to a specific context. In scenarios taken from the legal domain, a formal account of the open-texture of legal terms is modeled, which suggests varying notions of the "core" and "penumbra" of the meanings of a concept. The progress of reasoning proceeds from the specific to the more general.

History

Anthropologists have observed that taxonomies are generally embedded in local cultural and social systems, and serve various social functions. Perhaps the most well-known and influential study of folk taxonomies is Émile Durkheim's The Elementary Forms of Religious Life. A more recent treatment of folk taxonomies (including the results of several decades of empirical research) and the discussion of their relation to the scientific taxonomy can be found in Scott Atran's Cognitive Foundations of Natural History. Folk taxonomies of organisms have been found in large part to agree with scientific classification, at least for the larger and more obvious species, which means that it is not the case that folk taxonomies are based purely on utilitarian characteristics.

In the seventeenth century the German mathematician and philosopher Gottfried Leibniz, following the work of the thirteenth-century Majorcan philosopher Ramon Llull on his Ars generalis ultima, a system for procedurally generating concepts by combining a fixed set of ideas, sought to develop an alphabet of human thought. Leibniz intended his characteristica universalis to be an "algebra" capable of expressing all conceptual thought. The concept of creating such a "universal language" was frequently examined in the 17th century, also notably by the English philosopher John Wilkins in his work An Essay towards a Real Character and a Philosophical Language (1668), from which the classification scheme in Roget's Thesaurus ultimately derives.

Use of taxonomies in various disciplines

Taxonomies in software engineering

Vegas et al. make a compelling case to advance the knowledge in the field of software engineering through the use of taxonomies. Similarly, Ore et al. provide a systematic methodology to approach taxonomy building in software engineering related topics.

Software testing taxonomies

Several taxonomies have been proposed in software testing research to classify techniques, tools, concepts and artifacts. The following are some example taxonomies:

1. A taxonomy of model-based testing techniques
2. A taxonomy of static-code analysis tools

Engström et al. suggest and evaluate the use of a taxonomy to bridge the communication between researchers and practitioners engaged in the area of software testing. They have also developed a web-based tool to facilitate and encourage the use of the taxonomy. The tool and its source code are available for public use.

Taxonomies in research publishing

Citing inadequacies with current practices in listing authors of papers in medical research journals, Drummond Rennie and co-authors called in a 1997 article in JAMA, the Journal of the American Medical Association for

a radical conceptual and systematic change, to reflect the realities of multiple authorship and to buttress accountability. We propose dropping the outmoded notion of author in favor of the more useful and realistic one of contributor.

Since 2012, several major academic and scientific publishing bodies have mounted Project CRediT to develop a controlled vocabulary of contributor roles. Known as CRediT (Contributor Roles Taxonomy), this is an example of a flat, non-hierarchical taxonomy; however, it does include an optional, broad classification of the degree of contribution: lead, equal or supporting. Amy Brand and co-authors summarise their intended outcome as:

Identifying specific contributions to published research will lead to appropriate credit, fewer author disputes, and fewer disincentives to collaboration and the sharing of data and code.

As of mid-2018, this taxonomy apparently restricts its scope to research outputs, specifically journal articles; however, it does rather unusually "hope to … support identification of peer reviewers". (As such, it has not yet defined terms for such roles as editor or author of a chapter in a book of research results.) Version 1, established by the first Working Group in the (northern) autumn of 2014, identifies 14 specific contributor roles using the following defined terms:

• Conceptualization
• Methodology
• Software
• Validation
• Formal Analysis
• Investigation
• Resources
• Data curation
• Writing – Original Draft
• Writing – Review & Editing
• Visualization
• Supervision
• Project Administration
• Funding acquisition

Reception has been mixed, with several major publishers and journals planning to have implemented CRediT by the end of 2018, whilst almost as many aren't persuaded of the need or value of using it. For example,

The National Academy of Sciences has created a TACS (Transparency in Author Contributions in Science) webpage to list the journals that commit to setting authorship standards, defining responsibilities for corresponding authors, requiring ORCID iDs, and adopting the CRediT taxonomy.

The same webpage has a table listing 21 journals (or families of journals), of which:

• 5 have, or by end 2018 will have, implemented CRediT,
• 6 require an author contribution statement and suggest using CRediT,
• 8 don't use CRediT, of which 3 give reasons for not doing so, and
• 2 are uninformative.

The taxonomy is an open standard conforming to the OpenStand principles, and is published under a Creative Commons licence.

Taxonomy for the web

Websites with a well designed taxonomy or hierarchy are easily understood by users, due to the possibility of users developing a mental model of the site structure.

Guidelines for writing taxonomy for the web

• Mutually exclusive categories can be beneficial. If categories appear several places, it's called cross-listing or polyhierarchical. The hierarchy will lose its value if cross-listing appears too often. Cross-listing often appears when working with ambiguous categories that fits more than one place.
• Having a balance between breadth and depth in the taxonomy is beneficial. Too many options (breadth), will overload the users by giving them too many choices. At the same time having a too narrow structure, with more than two or three levels to click-through, will make users frustrated and might give up.

Is-a and has-a relationships, and hyponymy

Two of the predominant types of relationships in knowledge-representation systems are predication and the universally quantified conditional. Predication relationships express the notion that an individual entity is an example of a certain type (for example, John is a bachelor), while universally quantified conditionals express the notion that a type is a subtype of another type (for example, "A dog is a mammal", which means the same as "All dogs are mammals").

Taxonomies are often represented as is-a hierarchies where each level is more specific (in mathematical language "a subset of") the level above it. For example, a basic biology taxonomy would have concepts such as mammal, which is a subset of animal, and dogs and cats, which are subsets of mammal. This kind of taxonomy is called an is-a model because the specific objects are considered as instances of a concept. For example, Fido is-an instance of the concept dog and Fluffy is-a cat.

 

In linguistics, is-a relations are called hyponymy. Words that describe categories are called hypernyms and words that are examples of categories are hyponyms. In the simple biology example, dog is a hypernym and Fido is one of its hyponyms. A word can be both a hyponym and a hypernym. For example, dog is a hyponym of mammal and also a hypernym of Fido.