sure that utilizes statistics of both kinds of metadata to incrementally weave individual hierarchies into a deeper, more complete folksonomy. The approach has several advantages over previous work. Specifically, it: (1) better addresses the challenges of sparse, shallow, ambiguous, noisy and inconsistent data; (2) the approach is more scalable, especially when the learned folksonomies are deep; (3) it produces more consistent and richer folksonomies. We demonstrate the utility of our present approach on real-world data from Flickr, and introduce a simple metric, which evaluates the quality of the learned folksonomies in terms of depth and bushiness.

## 2. Structured Social Metadata

In addition to tagging content, some social Web sites also allow users to organize it hierarchically. Delicious **users can**
group related tags into bundles, and Flickr **users can group**
related photos into sets **and then group related sets in** collections**. While the sites themselves do not impose any constraints on the vocabulary or semantics of the hierarchies,**
in practice users employ them to represent both subclass relationships ('dog' is a kind of 'mammal') and part-of relationship ('my kids' is a part of 'family'). Users appear to express both types of relations (and possibly others) through personal hierarchies, in effect using the hierarchies to specify broader/narrower relations. Even without strict semantics being attached to these relations, we believe that personal hierarchies represent a novel, rich source of evidence for learning folksonomies.

![0_image_0.png](0_image_0.png)

Figure 1: Personal hierarchies specified by a Flickr user. (a) Some of the collections created by the user and (b) sets associated with the Plant Pests **collection, and (c) tags associated with an image in the**
Caterpillars **set.**
We briefly describe how this feature is implemented on the social photo-sharing site, Flickr (**http://www.flickr.com**).

Flickr allows users to group their photos in album-like folders, called sets**. Users can also group sets into "super" albums, called** collections.

1 **Both sets and collections are**
named by the owner of the image. A photo can be part of multiple sets.

While Flickr does not enforce any specific rules about how to organize photos or how to name them, most users group
"similar" or "related" photos into the same set and related sets into the same collection. Some users create multi-level hierarchies containing collections of collections, etc., **but the**
vast majority of users create shallow hierarchies, consisting of collections and their constituent sets. Figure 1(a)
shows some of the collections created by an avid naturalist on Flickr. These collections reflect the subjects she likes to photograph: Birds, Mammals, Plants, Mushrooms & Fungi, Plant Pests, Plant Diseases**, etc. Figure 1(b) shows sets of** the Plant Pests **collection:** Plant Parasites, Sap Suckers, Plant Eaters**, and** Caterpillars**. Each set contains one or more**
photos, which are tagged by the user. For example, a photograph in the set Caterpillars **(Figure 1(c)), is annotated**
with multiple tags describing it:(Animal, Lepidoptera, **Moth**,
larva, Caterpillar), its color (Black and orange), condition (on Senecio, eating), and location (**North Seatac**
Park, King County, WA, **North America**).

## 3. Challenges In Learning From Structured Metadata Learning Folksonomies From Social Metadata, Specifically, From Structured Metadata, Presents A Number Of Challenges: 3.1 Sparseness

Social metadata is usually very sparse. Users provide 4
–7 tags per bookmark on Delicious **in our data set and 3**.74 tags per photo on Flickr **[13]. Sparseness is also manifested**
in the hierarchical organization created by an individual. In our Flickr data set, we found only 600 out of 21, **792 users —**
approximately 0.**02 percent - who created multi-level (collections of collections) hierarchies. Most users define** shallow
(single-level) hierarchies. Moreover, among these shallow hierarchies, few users organize content the same way. For instance, of the 433 users who created an animal **collection,**
only a few created common child sets, such as bird, cat, dog or insect**. In order to learn a rich and complete folksonomy,** we have to aggregate social metadata from many different users.

## 3.2 Noisy Vocabulary

Vocabulary noise has several sources. One common source is variations and errors in spelling. Noise also arises from users' idiosyncratic naming conventions. While such names as not sure, pleaseaddthistothethemecomppoll, mykid may be meaningful to image owner and her narrow interest group, they are relatively meaningless to other users.

## 3.3 Ambiguity

An individual tag is often ambiguous [9, 5]. For example, jaguar **can be used to refer to a mammal or a luxury**
car. Similarly, terms that are used to name collections and 1**The collection feature is limited to paid "pro" users. Pro**
users can also create unlimited number of photo sets, while free membership limits a user to three sets.