Modeling Items of Wit and Wisdom

Semantic Web Interest Group,

Hello. I am pleased to share an exciting advancement pertaining to information retrieval: story-based search and recommendation. In this approach, individuals provide stories to retrieve content that is to be useful for selected story characters, e.g., advice or items of wit and wisdom. The stories they provide could be real-world stories and the characters they select, in these cases, could be themselves or other people.

Individuals’ social media posts could be utilized to provide stories and situational contextual cues with which to receive useful recommendations. In the future, users of social media could be provided with menu options for browsing content pertinent to the situations described in their recent or selected posts, content aligned with their preferences and aesthetic tastes, while having the capability to provide feedback on the contextual recommendations and the content recommended.

At least initially, individuals might receive, in response, paginated lists of items. Eventually, more advanced systems might be able to, including by means of initiating elaborative dialogues or generating input forms for completion, more intelligently sort these items, refine these items, and even ultimately select, or decide upon, a single item.

This approach involves resources, resembling dictionaries and thesauruses, for proverbs and other items of wit and wisdom rather than lexemes. Items of wit and wisdom (e.g., allegories, anecdotes, aphorisms, apologues, fables, historical analogues, jokes, literature, lyrics, parables, poems, proverbs, quotations, stories, and witticisms) and their interpretations can be described using metadata schemas and interrelated to one another using formal ontologies.

Please do take a moment to read the following article which describes these ideas in greater detail: . I welcome any questions, comments, and feedback with which to improve the article by email. Thank you.

Best regards,
Adam Sobieski

Received on Monday, 22 January 2024 13:00:32 UTC