Talk:Ontology (information science)

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Conflating ontology with ontology technology[edit]

This article conflates the two notions of "applied ontologies" vs "specific ontology technology". There are dozens of implementations of ontologies, not least of which Wikipedia's topic structure, that emphasize and exercise principles of "applied" ontologies, (as opposed to the philosophy of the application of those models, which is what the main Ontology article covers)

But the unifying theme of an ontology is that is a "model of a domain of knowledge that itself facilities the creation of new knowledge". Which is what separates it from a mere taxonomy or glossary in a given domain of knowledge. The main benifit that this article puts forward for an ontology is its helpfulness on getting lots of experts on the same page

But that is the main benefit of a taxonomy and an ontology should be doing more than that. The most substantial example of an ontology in the history of science is without question the periodic table of the elements.

Creating the table layout and maturing it, allowed chemists to infer that new, currently unknown, chemicals must exist and to make interesting predictions about their properties based on their locations in the table and their inferred atomic weights.

The fact that this article does not mention the periodic table is a little like having an article on this history of basketball and not including Wilt Chamberlain or the article on baseball not mentioning Babe Ruth. The periodic table is far and away the most important information ontology that humans ever created and is a central part of the shift away from alchemy towards chemistry, which itself was central to the movement to create modern science. Yet it is not mentioned even among the dozens of examples listed below.

Another huge victory for an ontology was are the Feynman Quark Diagrams, which was an ontology worthy of winning the Feynman the Nobel Prize. It is critical to point out that the most profound ontologies in the history of science have been pen-and-paper exercises, and remain visual drawings to this day. This is reflexive of the ongoing debate in the information science community between the effectiveness of formal ontological methods (which are not well discussed here, but are discussed in isolation) have failed to "catch on" the way that hashtags, wikipedia page name spaces and other "folksonomy" light-weight digital taxonomies have largely won out on information organization over more formal organization schemes like the dewey decimal system. (which BTW is also missing from this article... madness).

Instead of mentioning the tensions between formal and informal ontologies, this article emphasizes Google Knowledge Graph, which it little more IMHO than an "ontology parasite as a product" that has contributed almost nothing to the science of ontologies, and instead merely found a way to commercialize the ontology inherently found in the structure of wikipedia itself. Facebook chooses to call its forum product "groups" but the article on "groups" does not mention this in the introduction of its article. This endorsement of Googles products represents an inappropriate product endorsement that is only incidentally related to actual subject and should be removed.

I propose that I re-write the introduction to the article with these new emphasis. I also propose that the sections of the article which equate specific technologies (OWL etc) be re-written to emphasize that this is one technology attempt to generalize ontology principles, and not an exclusive one.

I will wait for reactions to this proposals over on Talk before I move forward with them... perhaps indefinitely because "who has time?" In that case, I encourage anyone else to attempt to address these underlying issues in the article.

Ftrotter (talk) 04:39, 16 July 2019 (UTC)

I completely disagree with your proposal. It is true that there are other meanings for the term "ontology". In philosophy especially it means something different. That is why there are multiple articles on the topic. This article is about the specific meaning the term has in computer science, library science, and artificial intelligence. If you want to make changes to it the starting point should be reliable sources in that field that support your position, not essays that state your individual opinions, even if they are well thought out opinions as I think yours are. --MadScientistX11 (talk) 14:57, 27 April 2020 (UTC)

Rewrite or remove "Criticism" section[edit]

The content of this section either needs to be given some context or removed altogether since it adds little to no substance to the overall page. Even then, I'm not sure a rewrite would be worthwhile, given that neither of the two parts of this section amount to criticisms of ontologies. The first part refers to a debate over the utility of the realists' methodology for ontology engineering, while the second doesn't even pass as an actual criticism. SomeEnlightenedNarcissist (talk) 20:41, 5 October 2016 (UTC)

After almost 2 weeks with no response, I went ahead and removed the section myself. I'll leave this open in case someone decides that they'd like to discuss the edit, but I do intend to close this to commenting within 2 weeks. SomeEnlightenedNarcissist (talk) 16:50, 16 October 2016 (UTC)
I'm just looking at this talk page for the first time in a while and from what I remember, I felt the same about the "Criticism" section. I think I left it the last time I made any edits to this article because I didn't know of any good references for legitimate criticism (not saying there isn't any, I can think of several good critiques of specific technologies as well as the concept in general) hoping that someone would clean it up. But since no one did I think the correct thing to do was to just delete it until someone can write something encyclopedic. --MadScientistX11 (talk) 15:03, 27 April 2020 (UTC)

Topic notes[edit]

Following are reminders for topics to look into for potential coverage within the article.

  • There are different types of ontologies including domain ontologies, generic ontologies, application ontologies and representational ontologies.

A note on terminology[edit]

"In computer science and information science, ontologies are used to formally represent knowledge within a domain." Would "knowledge" here not be better phrased as "information"? — Preceding unsigned comment added by 67.193.98.171 (talk) 19:08, 9 October 2014 (UTC)

I see that someone has preceded me here, saying just what I also think and have implemented as an edit to the lead. Information science is primarily about information, not so focused on knowledge as such. Well, I hope I am not treading on toes!Chjoaygame (talk) 19:02, 14 November 2014 (UTC)
I see that these comments are old, but I'd still like to address them since nobody else did. The short answer to your question is 'no', simply because many people within the realms of philosophy, computer science, and information science would probably consider knowledge to be a type of information (i.e., information that we have confirmed to be both justifiable and true). If you're still interested, you may want to check out the DIKW Pyramid. SomeEnlightenedNarcissist (talk) 16:57, 16 October 2016 (UTC)
I agree with SomeEnlightenedNarcissist ontologies typically represent knowledge not information. Information is usually at the level of data bases or html files. Ontologies add a formal layer, usually grounded in Description Logic, which is a subset of First Order Logic. This often includes things like rules, subsumption hierarchies, etc. --MadScientistX11 (talk) 05:41, 25 August 2018 (UTC)

Knowledge is what people "know" - in other words, knowledge is in people's brains and nowhere else.
All the other stuff that is not in people's brains is "information".
Thus:
Knowledge is NOT information.
What some people call "knowledge graphs", or "domain ontologies" are better termed "domain information structures". Ken Evans 19:29, 26 April 2020 (UTC) — Preceding unsigned comment added by The ken evans (talkcontribs)

@The ken evans: You would need to provide reliable sources for your opinion before it could be added to the article: see WP:RS. Of course, if most people generally agreed with your opinion then they would already be using your preferred terms instead of the terms that you wish to replace as well as other terms that contradict your opinion such as distributed cognition, distributed knowledge, knowledge base, knowledge management, knowledge tagging, personal knowledge base, social information processing, etc. Biogeographist (talk) 20:01, 26 April 2020 (UTC)
@Biographist: What a dumb comment! The verb "to know" is about what individual people have stored in their brains. All of the references that you use are just rhetorical devices that have been used by their originators to create misleading hype for their opinions. Ken Evans 13:22, 31 May 2020 (UTC)
Mr. Evans, I suggest you familiarize yourself with some of the basic Wikipedia policies. First, we don't insult each other with name calling, we stick to issues about editing the articles and substantive discussion. Civility is one of the Five pillars that define what Wikipedia is about and how editors collaborate with each other. Second, the fact that you think people in the computer science and AI community such as the W3C, Google, Facebook, Stanford, Bioportal, and countless other leaders in the field use certain terminology incorrectly is irrelevant to Wikipedia. If you think that is the case then publish an article or blog or book or whatever and get those communities to start using other terminology. Wikipedia is an encyclopedia where we document certain topics based on reliable sources and terms like ontology and Knowledge Graph are used as described in this article every day by countless people in these fields including myself. --MadScientistX11 (talk) 14:46, 1 June 2020 (UTC)

mistaken IP edit[edit]

I undid an IP edit.

The undone edit replaced the word "fundamentally" with the word 'supposedly'. The edit was second-guessing the sentence that it changed. The sentence is "In computer science and information science, an ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that really or fundamentally exist for a particular domain of discourse." The edit intended that the word "fundamentally" assumed something that was actually 'supposed'. But that reading is mistaken. The word "fundamentally" is explanatory of "really", not alternative to it. The "domain of discourse" has its suppositions, and they are implied in the sentence. Repetition of the notion of implication does not improve the sense of the sentence.Chjoaygame (talk) 01:40, 18 May 2015 (UTC)

undid a valuable edit that needs work before it can be accepted[edit]

I have just undone a good faith edit that should have been supplied with a reliable source or should have just appeared on this talk page. The edit is probably valuable, but it has not been given the nurture that it needs to prepare it to appear in the article. The article is not the place for unsourced discussion. I trust that Editor Pacerier will take my undo in good part. I am guessing that he has the knowledge to post something in the article with adequate sourcing. Alternatively he might find in the article a specific statement or two that he can tag with request for sourcing, or otherwise challenge. Please excuse my incomplete edit summary: I hit a wrong key.Chjoaygame (talk) 18:02, 29 September 2015 (UTC)

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several minor edits, see talk page[edit]

I've added some citations, reworded a few sentences, pulled the etymology from the ontology(philosophy) page, and removed a few sentences to try to improve the flow. Since other articles have been merged into this one, it looks like it's in need of restructuring the sections. Also, I haven't touched the banner calling for more citations in the ontology editors section, and I haven't added any citations for the lengthy lists on editors, languages, applications, etc. I will try to keep working on this article to add any citations I can. — Preceding unsigned comment added by JDontology (talkcontribs) 20:17, 9 May 2018 (UTC)

Removed section[edit]

This section doesn't really belong here; I moved it from the article and tried to clean up parts that were not grammatical. A compressed few sentences might be appropriate at knowledge graph (information science) if someone can make sense of it. – SJ + 00:38, 30 June 2020 (UTC)

Using a knowledge graph to build ontologies[edit]

In the case of integrating supplemental data sources, a 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.

An SQL query is tightly coupled and rigidly constrained by datatype within the specific database. It can join tables and extract data from tables. The result is generally a table. A query can join tables by any columns which match by datatype. A SPARQL query is the standard query language and protocol for Linked Open Data on the Web. It is only 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, a SPARQL query reflects the relationships between entities in a way that is aligned with human's understanding of the domain, so the semantic intention of the query can be seen on the query itself. Unlike SPARQL an SQL query reflects the specific structure of the database and is derived from matching the relevant primary and foreign keys of tables. Thereby, it loses the semantics of the query by missing the relationships between entities.

In the case of machine learning, A knowledge graph can help find latent connections among items, help improve precision, and help identify a user's intention which was hidden only by the ML output. A knowledge graph can help to extend a user's interests reasonably using various relation types, increasing diversity. It can also help to generate different knowledge presentations oriented by interested items, augmenting the dataset with the distance values between entities.

Editing Tools Section[edit]

Someone recently added a tool called Gra.fo to the section on editors. The reference given was this: https://gra.fo/ If you look at that site it is little more than a "There will eventually be a company site here" site. There are just a few pages with almost no content. I think this tool is clearly not notable so I removed it. But I think it points to a bigger issue, I think many of the tools in this section (another example would be JOE) are quite probably not notable. If the only documentation about an editor comes from their own company or papers written by the developers then IMO it is not notable and should be removed from the article. --MadScientistX11 (talk) 14:39, 2 July 2020 (UTC)