Where do ontologies begin
For the same beginners, in ontologies, like me, as well as for those who want to start, for those who ask themselves what it is, what it is eaten with and where to start with, I suggest starting from where I started and I, namely:
And as an introductory course of copy-paste from a translation in Russian to raise interest in this topic.
For the translation, thanks to the unknown translator A.I. Filyaev.
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In the future, I hope, I will continue this topic as I master the material and obtain new knowledge.
In recent years, the development of ontologies — formal explicit descriptions of domain terms and the relations between them (Gruber 1993) — is being transferred from the world of artificial intelligence laboratories to the desks of subject matter experts. Ontology has become commonplace on the World Wide Web. Ontologies on the web range from large taxonomies categorizing websites (like on the Yahoo! site), to categorizing the products being sold and their characteristics (like on Amazon.com). The WWW Consortium (W3C) develops the RDF (Resource Description Framework) (Brickley and Guha 1999), a coding language for knowledge on web pages, in order to make it understandable for electronic agents who search for information. The Defense Advanced Research Projects Agency, DARPA, in collaboration with the W3C, is developing the Markup Language for DARPA Agents (DARPA Agent Markup Language, DAML), expanding RDF with more expressive designs designed to facilitate agent interaction in the network. (Hendler and McGuinness 2000). In many disciplines, standard ontologies are being developed that can be used by subject matter experts to share and annotate information in their field. For example, in the field of medicine, large standard, structured dictionaries have been created, such as snomed (Price and Spackman 2000) and the Unified Medical Language System (Humphreys and Lindberg 1993) semantic network. Extensive general-purpose ontologies also appear. For example, the United Nations Development Program and the Dun & Bradstreet company have joined forces to develop an ontology for the UNSPSC, which provides terminology for goods and services (http://www.unspsc.org/).
An ontology defines a common vocabulary for scientists who need to share information in the subject area. It includes machine-interpreted formulations of the basic concepts of the domain and the relationships between them.
Why the need to develop ontology? Here are some reasons:
For sharing by people or software agents a common understanding of the structure of information.
Sharing people or software agents with a common understanding of the structure of information is one of the most common goals for developing ontologies (Musen 1992; Gruber 1993). For example, suppose several different websites contain information on medicine or provide information on paid online medical services. If these websites share and publish the same basic ontology of the terms they all use, then computer agents can extract information from these various sites and accumulate it. Agents can use the accumulated information to respond to user requests or as input to other applications.
Ensuring that knowledge of the domain can be used has become one of the driving forces of the recent surge in ontology research. For example, for models of many different subject areas, it is necessary to formulate the concept of time. This representation includes the concept of time intervals, points in time, relative measures of time, etc. If one group of scientists develops such an ontology in detail, then others can simply reuse it in their subject areas. In addition, if we need to create a large ontology, we can integrate several existing ontologies describing parts of a large data domain. We can also reuse a basic ontology, such as UNSPSC, and extend it to describe the subject area of interest.
Creating explicit domain-specific assumptions underlying the implementation makes it easy to change these assumptions as our knowledge of the subject domain changes. The hard coding of assumptions about the world in a programming language leads to the fact that these assumptions are not only difficult to find and understand, but also difficult to change, especially to a non-programmer. In addition, explicit domain knowledge specifications are useful for new users who need to know the meaning of subject terms.
The separation of domain knowledge from operational knowledge is another version of the general use of ontologies. We can describe the task of configuring a product from its components in accordance with the required specification and implement a program that makes this configuration independent of the product and the components themselves (McGuinness and Wright 1998). After this, we can develop an ontology of the components and characteristics of a computer and apply this algorithm to configure non-standard computers. We can also use the same algorithm to configure elevators if we provide it with an ontology of elevator components (Rothenfluh et al. 1996).
Analysis of knowledge in the subject area is possible when there is a declarative specification of terms. Formal analysis of terms is extremely valuable both when attempting to reuse existing ontologies and when expanding them (McGuinness et al. 2000).
Often, the ontology of the domain is not in itself a goal. Developing an ontology is akin to defining a dataset and its structure for use by other programs. Problem-solving methods, domain-independent applications and software agents use ontologies and knowledge bases based on these ontologies as data. For example, in this article we develop an ontology of wines and food, as well as suitable combinations of wines and dishes. Then this ontology can be used as a basis for applications in the restaurant management toolkit: One application could compile a list of wines for the menu for the current day or respond to requests from waiters and visitors. Another application could analyze the wine cellar inventory and suggest categories of wines for replenishment and specific wines for purchasing to the next menu or for cookbooks.
- The terminology of ontologies, which will allow you to understand the meaning of the terms used in the article and lecture
- Article Ontology Development 101: A Guide to Creating Your First Ontology for 2001, which is a basic article for everyone who begins to engage in ontologies, as well as its translation into Russian from the site International Forum “Educational Technologists and Society . The disadvantage of the article is not a very good example of ontology with wine.
- Lecture Ontology and knowledge representation from lektorium.tv mentioned here . In my opinion, this is exactly what is needed for the presentation of what ontologies are and what it is for. In addition, the lecture provides a lot of useful information on existing projects, tools, applications, and more.
And as an introductory course of copy-paste from a translation in Russian to raise interest in this topic.
For the translation, thanks to the unknown translator A.I. Filyaev.
')
In the future, I hope, I will continue this topic as I master the material and obtain new knowledge.
Why create an ontology?
In recent years, the development of ontologies — formal explicit descriptions of domain terms and the relations between them (Gruber 1993) — is being transferred from the world of artificial intelligence laboratories to the desks of subject matter experts. Ontology has become commonplace on the World Wide Web. Ontologies on the web range from large taxonomies categorizing websites (like on the Yahoo! site), to categorizing the products being sold and their characteristics (like on Amazon.com). The WWW Consortium (W3C) develops the RDF (Resource Description Framework) (Brickley and Guha 1999), a coding language for knowledge on web pages, in order to make it understandable for electronic agents who search for information. The Defense Advanced Research Projects Agency, DARPA, in collaboration with the W3C, is developing the Markup Language for DARPA Agents (DARPA Agent Markup Language, DAML), expanding RDF with more expressive designs designed to facilitate agent interaction in the network. (Hendler and McGuinness 2000). In many disciplines, standard ontologies are being developed that can be used by subject matter experts to share and annotate information in their field. For example, in the field of medicine, large standard, structured dictionaries have been created, such as snomed (Price and Spackman 2000) and the Unified Medical Language System (Humphreys and Lindberg 1993) semantic network. Extensive general-purpose ontologies also appear. For example, the United Nations Development Program and the Dun & Bradstreet company have joined forces to develop an ontology for the UNSPSC, which provides terminology for goods and services (http://www.unspsc.org/).
An ontology defines a common vocabulary for scientists who need to share information in the subject area. It includes machine-interpreted formulations of the basic concepts of the domain and the relationships between them.
Why the need to develop ontology? Here are some reasons:
For sharing by people or software agents a common understanding of the structure of information.
- To be able to reuse knowledge in the subject area.
- In order to make the assumptions in the subject area explicit.
- To separate knowledge in the subject area from operational knowledge.
- To analyze knowledge in the subject area.
Sharing people or software agents with a common understanding of the structure of information is one of the most common goals for developing ontologies (Musen 1992; Gruber 1993). For example, suppose several different websites contain information on medicine or provide information on paid online medical services. If these websites share and publish the same basic ontology of the terms they all use, then computer agents can extract information from these various sites and accumulate it. Agents can use the accumulated information to respond to user requests or as input to other applications.
Ensuring that knowledge of the domain can be used has become one of the driving forces of the recent surge in ontology research. For example, for models of many different subject areas, it is necessary to formulate the concept of time. This representation includes the concept of time intervals, points in time, relative measures of time, etc. If one group of scientists develops such an ontology in detail, then others can simply reuse it in their subject areas. In addition, if we need to create a large ontology, we can integrate several existing ontologies describing parts of a large data domain. We can also reuse a basic ontology, such as UNSPSC, and extend it to describe the subject area of interest.
Creating explicit domain-specific assumptions underlying the implementation makes it easy to change these assumptions as our knowledge of the subject domain changes. The hard coding of assumptions about the world in a programming language leads to the fact that these assumptions are not only difficult to find and understand, but also difficult to change, especially to a non-programmer. In addition, explicit domain knowledge specifications are useful for new users who need to know the meaning of subject terms.
The separation of domain knowledge from operational knowledge is another version of the general use of ontologies. We can describe the task of configuring a product from its components in accordance with the required specification and implement a program that makes this configuration independent of the product and the components themselves (McGuinness and Wright 1998). After this, we can develop an ontology of the components and characteristics of a computer and apply this algorithm to configure non-standard computers. We can also use the same algorithm to configure elevators if we provide it with an ontology of elevator components (Rothenfluh et al. 1996).
Analysis of knowledge in the subject area is possible when there is a declarative specification of terms. Formal analysis of terms is extremely valuable both when attempting to reuse existing ontologies and when expanding them (McGuinness et al. 2000).
Often, the ontology of the domain is not in itself a goal. Developing an ontology is akin to defining a dataset and its structure for use by other programs. Problem-solving methods, domain-independent applications and software agents use ontologies and knowledge bases based on these ontologies as data. For example, in this article we develop an ontology of wines and food, as well as suitable combinations of wines and dishes. Then this ontology can be used as a basis for applications in the restaurant management toolkit: One application could compile a list of wines for the menu for the current day or respond to requests from waiters and visitors. Another application could analyze the wine cellar inventory and suggest categories of wines for replenishment and specific wines for purchasing to the next menu or for cookbooks.
Source: https://habr.com/ru/post/140696/
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