Fresh & New(er)

Synonymiser is an experimental micro-application that returns related words from search data relationships held in the Powerhouse Museum’s collection database. These ‘synonyms’ are dynamically generated from realtime user interaction with the collection database.

On the Synonymiser site you can enter any search word or phrase and it will return a list of ‘related’ words or phrases and a measure of relationship.

Of course, the results are not synonyms in the dictionary sense of the word, but instead show meaning relationships specific to the way in which users use our collection database.

The idea is that these word relationships can then be used to query other data sources. In this case we retreive images from Flickr™ to demonstrate the concept. It is possible to merge terms and/or offer alternative terms to improve results using this.

There is a proposal to make these synonym relationships available via an API to allow other museums to use and build upon our usage data to improve their own search tools.

Is this useful? Would you like to be involved or help with this?

What is ‘synonym promiscuity’?

‘Synonym promiscuity’ is our term for describing the uniqueness of a relationship of one word to another. If the value is low (less than 10) then the synonym has a very close relationship with the word entered. If the value is high then the synonym is related to many other words (high promiscuity). We are currently refining the mathematics behind the calculation of these values – but they current figures shoukld provide a means for comapring words.

Continuing on the additions from yesterday.

Today we added the top three search terms for each object to the USER KEYWORD section of an object page. This section is where folksonomy tags can be added and deleted.

Here is a 1960s raincoat for example.

Why did we add this to the USER KEYWORDS section?

What we are finding through our ongoing analysis of folksonomy tagging behaviour is that users are generally adding synonyms. What folksonomies are doing in this instance is effectively ‘crowd-sourcing’ synonym generation. Now when a user searches for term and selects an object from a list of results they are making an association between the object and that term. In many instances these may be tentative associations and generally full of false drops, but when aggregated patterns begin to emerge (see Chan 2006 forthcoming). Effectively on our site we are noticing that the search terms are beginning to offer the same kind of synonym behaviour that tags do.

We are presenting them together as a way of making explicit the terms that are already associated with an object (automatically). This way we hope to improve the diversity of user tags (why tag with a word/term that already appears?). This is just a trial though and if we see little or no change then we may move the search terms off to a separate section.

I’d welcome any comments or thoughts on this as we’re experimenting here.

Take a look at the new ‘related searches’ feature on our collection search.

Now a search for ‘glass‘ will these ‘similar searches’ –

glassware vase bottles bowl bowls

This result will change over time. Hopefully we will implement a timescale simulator in our upcoming ‘experimental’ browsing section which will allow users to view the changing search language over time.

How does it work?

Because we keep a large store of relationships between search terms and clicked objects, we are able to reverse query terms such as ‘glass’ and see what terms have been used to find similar objects. We currently show only the top 5 terms – aggregated – which lessens the probability of ‘false drops’. False drops are most likely to occur for uncommon search terms although this will change over time, too.

Does this use folksonomy tags?

Yes. We allow user tags to be included in the search terms and from time to time certain objects will be most visited as a result of their user tag.