[[edit source](/w/index.php?title=Semantic_Web&action=edit&section=15 "Edit section's source code: Practical feasibility")] Critics question the basic feasibility of a complete or even partial fulfillment of the Semantic Web, pointing out both difficulties in setting it up and a lack of general-purpose usefulness that prevents the required effort from being invested. In a 2003 paper, Marshall and Shipman point out the cognitive overhead inherent in formalizing knowledge, compared to the authoring of traditional web [hypertext](https://en.wikipedia.org/wiki/Hypertext "Hypertext"):[[52]](#cite_note-which-52) > While learning the basics of HTML is relatively straightforward, learning a knowledge representation language or tool requires the author to learn about the representation's methods of abstraction and their effect on reasoning. For example, understanding the class-instance relationship, or the superclass-subclass relationship, is more than understanding that one concept is a "type of" another concept. [...] These abstractions are taught to computer scientists generally and knowledge engineers specifically but do not match the similar natural language meaning of being a "type of" something. Effective use of such a formal representation requires the author to become a skilled knowledge engineer in addition to any other skills required by the domain. [...] Once one has learned a formal representation language, it is still often much more effort to express ideas in that representation than in a less formal representation [...]. Indeed, this is a form of programming based on the declaration of semantic data and requires an understanding of how reasoning algorithms will interpret the authored structures. According to Marshall and Shipman, the [tacit](https://en.wikipedia.org/wiki/Tacit_knowledge "Tacit knowledge") and changing nature of much knowledge adds to the [knowledge engineering](https://en.wikipedia.org/wiki/Knowledge_engineering "Knowledge engineering") problem, and limits the Semantic Web's applicability to specific domains. A further issue that they point out are domain- or organization-specific ways to express knowledge, which must be solved through community agreement rather than only technical means.[[52]](#cite_note-which-52) As it turns out, specialized communities and organizations for intra-company projects have tended to adopt semantic web technologies greater than peripheral and less-specialized communities.[[53]](#cite_note-Herman000-53) The practical constraints toward adoption have appeared less challenging where domain and scope is more limited than that of the general public and the World-Wide Web.[[53]](#cite_note-Herman000-53) Finally, Marshall and Shipman see pragmatic problems in the idea of ([Knowledge Navigator](https://en.wikipedia.org/wiki/Knowledge_Navigator "Knowledge Navigator")-style) intelligent agents working in the largely manually curated Semantic Web:[[52]](#cite_note-which-52) > In situations in which user needs are known and distributed information resources are well described, this approach can be highly effective; in situations that are not foreseen and that bring together an unanticipated array of information resources, the Google approach is more robust. Furthermore, the Semantic Web relies on inference chains that are more brittle; a missing element of the chain results in a failure to perform the desired action, while the human can supply missing pieces in a more Google-like approach. [...] cost-benefit tradeoffs can work in favor of specially-created Semantic Web metadata directed at weaving together sensible well-structured domain-specific information resources; close attention to user/customer needs will drive these federations if they are to be successful. [Cory Doctorow](https://en.wikipedia.org/wiki/Cory_Doctorow "Cory Doctorow")'s critique ("[metacrap](https://en.wikipedia.org/wiki/Metacrap "Metacrap")")[[54]](#cite_note-54) is from the perspective of human behavior and personal preferences. For example, people may include spurious metadata into Web pages in an attempt to mislead Semantic Web engines that naively assume the metadata's veracity. This phenomenon was well known with metatags that fooled the [Altavista](https://en.wikipedia.org/wiki/Altavista "Altavista") ranking algorithm into elevating the ranking of certain Web pages: the Google indexing engine specifically looks for such attempts at manipulation. [Peter Gärdenfors](https://en.wikipedia.org/wiki/Peter_G%C3%A4rdenfors "Peter Gärdenfors") and [Timo Honkela](https://en.wikipedia.org/wiki/Timo_Honkela "Timo Honkela") point out that logic-based semantic web technologies cover only a fraction of the relevant phenomena related to semantics.[[55]](#cite_note-Gardenfors04-55)[[56]](#cite_note-Honkela08-56)

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  "text": "[[edit source](/w/index.php?title=Semantic_Web&action=edit&section=15 \"Edit section's source code: Practical feasibility\")]\n\nCritics question the basic feasibility of a complete or even partial fulfillment of the Semantic Web, pointing out both difficulties in setting it up and a lack of general-purpose usefulness that prevents the required effort from being invested. In a 2003 paper, Marshall and Shipman point out the cognitive overhead inherent in formalizing knowledge, compared to the authoring of traditional web [hypertext](https://en.wikipedia.org/wiki/Hypertext \"Hypertext\"):[[52]](#cite_note-which-52)\n\n> While learning the basics of HTML is relatively straightforward, learning a knowledge representation language or tool requires the author to learn about the representation's methods of abstraction and their effect on reasoning. For example, understanding the class-instance relationship, or the superclass-subclass relationship, is more than understanding that one concept is a \"type of\" another concept. [...] These abstractions are taught to computer scientists generally and knowledge engineers specifically but do not match the similar natural language meaning of being a \"type of\" something. Effective use of such a formal representation requires the author to become a skilled knowledge engineer in addition to any other skills required by the domain. [...] Once one has learned a formal representation language, it is still often much more effort to express ideas in that representation than in a less formal representation [...]. Indeed, this is a form of programming based on the declaration of semantic data and requires an understanding of how reasoning algorithms will interpret the authored structures.\n\nAccording to Marshall and Shipman, the [tacit](https://en.wikipedia.org/wiki/Tacit_knowledge \"Tacit knowledge\") and changing nature of much knowledge adds to the [knowledge engineering](https://en.wikipedia.org/wiki/Knowledge_engineering \"Knowledge engineering\") problem, and limits the Semantic Web's applicability to specific domains. A further issue that they point out are domain- or organization-specific ways to express knowledge, which must be solved through community agreement rather than only technical means.[[52]](#cite_note-which-52) As it turns out, specialized communities and organizations for intra-company projects have tended to adopt semantic web technologies greater than peripheral and less-specialized communities.[[53]](#cite_note-Herman000-53) The practical constraints toward adoption have appeared less challenging where domain and scope is more limited than that of the general public and the World-Wide Web.[[53]](#cite_note-Herman000-53)\n\nFinally, Marshall and Shipman see pragmatic problems in the idea of ([Knowledge Navigator](https://en.wikipedia.org/wiki/Knowledge_Navigator \"Knowledge Navigator\")-style) intelligent agents working in the largely manually curated Semantic Web:[[52]](#cite_note-which-52)\n\n> In situations in which user needs are known and distributed information resources are well described, this approach can be highly effective; in situations that are not foreseen and that bring together an unanticipated array of information resources, the Google approach is more robust. Furthermore, the Semantic Web relies on inference chains that are more brittle; a missing element of the chain results in a failure to perform the desired action, while the human can supply missing pieces in a more Google-like approach. [...] cost-benefit tradeoffs can work in favor of specially-created Semantic Web metadata directed at weaving together sensible well-structured domain-specific information resources; close attention to user/customer needs will drive these federations if they are to be successful.\n\n[Cory Doctorow](https://en.wikipedia.org/wiki/Cory_Doctorow \"Cory Doctorow\")'s critique (\"[metacrap](https://en.wikipedia.org/wiki/Metacrap \"Metacrap\")\")[[54]](#cite_note-54) is from the perspective of human behavior and personal preferences. For example, people may include spurious metadata into Web pages in an attempt to mislead Semantic Web engines that naively assume the metadata's veracity. This phenomenon was well known with metatags that fooled the [Altavista](https://en.wikipedia.org/wiki/Altavista \"Altavista\") ranking algorithm into elevating the ranking of certain Web pages: the Google indexing engine specifically looks for such attempts at manipulation. [Peter Gärdenfors](https://en.wikipedia.org/wiki/Peter_G%C3%A4rdenfors \"Peter Gärdenfors\") and [Timo Honkela](https://en.wikipedia.org/wiki/Timo_Honkela \"Timo Honkela\") point out that logic-based semantic web technologies cover only a fraction of the relevant phenomena related to semantics.[[55]](#cite_note-Gardenfors04-55)[[56]](#cite_note-Honkela08-56)",
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