Bento Box Searching

Paul Jackson — Wed, 20 Dec 2017 17:53:52 +0000

Bento is Japanese for ‘lunch box.’ How it became a name for a Search engine plug in is beyond me unless one thinks of the smorgasbord of the information it supplies as “results” of a single search through many databases. We’ve seen something like this with the Knowledge Graph in Google, the box to the right of search results. We’ve seen it in early Clustering search engines such as Clusty, now called Yippy!, with categorizing on the left side of the screen taking you to sites under each subcategory.

So it seems libraries, at least a few academic libraries and public libraries, have caught up with this single search process, known also as federated search, (rather than searching fields in the library catalog,) as a way to introduce the researcher to articles, books, and resources valued enough to show up in the search. Which algorithms are used to go search sites other than just the library or libraries within academic institutions are not always given. For example with Google, we don’t know if there are items valued by payment, by peer review, or customer searching; giving the searcher what the computer thinks the customer wants.

I’ve been a critique of searching different engines which seem to want to tell the customer, “This is what we think you asked for,” instead of giving results one might have wanted which show both sides or even every perspective of a given event, subject, or category. This may be the reason there are now many private search engines. These are the engines that do not track what you search and do not leave cookies on your machine. Oscobo, WhaleSlide, Gyffu, and GoodGopher which have been launched in the last two or three years. Other well-known engines are StartPage, DuckDuckGo, Mojeek, and Privatelee.

So as we start using these ‘boxes’ of information we hope we will see complete information, not algorithm-generated results, nor subjective selection. One search engine used mainly for scientific research may be the way of future searching. Semantic Scholar’s AI analyzes research papers, articles, journals, and through data mining pulls out authors, references, figures, and topics. It then links all of this information together into a comprehensive picture of cutting-edge research. This engine sounds like a few of the early MIND-mapping or VISUAL mapping engines which brought related topics to the surface from searching related topics. Unfortunately we don’t see those engines anymore, only the mind-mapping do it yourself software. Over the last ten years there have been several books about semantic search; several available from ALA. We are now seeing many books using terms like Knowledge Graph equating much with the same process as semantic searching.

Resources:

Mother lode of searching databases:

Looking for a specific category database?

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

Searching the CIA declassified database.

https://www.muckrock.com/news/archives/2017/sep/22/crest-search-guide/

Ways to search breaking news stories.

http://blog.archive.org/2017/09/21/tv-news-chyron-data/

Tracking corporate violations of law and regulations.

Article: http://www.prnewswire.com/news-releases/from-enron-to-wells-fargo-expanded-violation-tracker-now-covers-18-years-of-corporate-crime–misconduct-300521979.html

Databases:

https://www.goodjobsfirst.org/violation-tracker

https://www.goodjobsfirst.org/

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Bento Box Searching