Reducing Ambiguity in Tagging Systems with Folksonomy Search Expansion
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Reducing Ambiguity in Tagging Systems with Folksonomy Search Expansion Jeff Z. Pan, Stuart Taylor, and Edward Thomas Dept. of Computing Science Univ. of Aberdeen Aberdeen, UK Abstract. Search facilities are vital for folksonomy (or social tagging mecha- nism) based systems. Although these systems allow great malleability and adapt- ability, they also surfer from problems, such as ambiguity in the meaning of tags, flat organisation of tags and some degree of unstabilising factor on consensus about which tags best describe some certain Web resources. It has been argued that folksonomy structure can be enhanced by ontologies; however, as suggested by Hotho et al., a key question remains open: how to exploit the benefits of on- tologies without bothering untrained users with its rigidity. In this paper, we pro- pose an approach to address the problem of ambiguity in tagging systems by expanding folksonomy search with ontologies, which are completely transparent to users. Preliminary implementations and evaluations on the efficiency and the usefulness of such expansions are very promising. 1 Introduction Web 2.0 applications are emerging providers of a new type of Web resources. On the one hand, more and more Web resources become available in popular folksonomy (or social tagging mechanism) based systems, such as Flickr1, YouTube2 and del.icio.us3. For example, in April 2008 alone, there are about 2.5 million photos geotagged4 in Flickr. On the other hand, mashup applications combine Web resources from more than one folksonomy based system into various integrated experience [11]. These two ob- servations indicate that search facilities are vital both within folksonomy based systems and across folksonomy based systems. There are advantages and disadvantages of the tagging approach [9]. The folksonomy based systems allow great malleability and adaptability, and the simplicity of adding and using tags allows them to be more accessible to users than well defined (more com- plex) classification systems. However, these systems also surfer from problems [17], such as ambiguity in the meaning of tags and flat organisation of tags (see Section 3.1). For example, in the results from a search on YouTube for “Focus” (the Dutch pro- gressive rock band), only 3 of the top 20 items in the list are relevant to that band. The 1 http://www.flickr.com/ 2 http://www.youtube.com/ 3 http://del.icio.us/ 4 Geotagging is the art of adding location information to things like a photo, according to Flickr. L. Aroyo et al. (Eds.): ESWC 2009, LNCS 5554, pp. 669–683, 2009. c Springer-Verlag Berlin Heidelberg 2009 670 J.Z. Pan, S. Taylor, and E. Thomas remaining 17 search results relate to alternative meanings of the word “focus”. This simple example illustrates that ambiguity in the meaning of tags is a significant prob- lem for domain specific folksonomy searches. Furthermore, for each of the 3 relevant results, the tags list for each result contained few other relevant tags than “Focus” and the name of the song in the video. In every case, information on at least of: the type of music, the album, the band members and other relevant information was omitted from the tag list. Halpin et al. expressed a major concern of the folksonomy approach, i.e., whether or not in the folksonomy based system consensus about which tags best describing some certain Web resources can be stabilised, so that such best tags are used most often with the folksonomy based systems [9]. There could be a serious problem if no coherent categorisation scheme can emerge at all from coolaborative tagging. This is a crucial point because ranking techniques employed by major Web search engines today are, to a large extent, influenced by people’s opinion of Web resources [10]. Although Halpin et al. concluded that tagging distributions tend to stabilise into power law distributions, the process of stabilisation (w.r.t. certain contexts) depends on the number of active users and to some extent on the temporal duration of the tagging process, according to their empirical study. Concrete predictions are not (yet) available on the number of active users and duration of time needed to complete the stabilisation process. The situation of searching across multiple folksonomy based system could be even worse, since different styles and environments provided by different systems could make it harder to reach stabilised consensus. Some might even argue that it is impossible to reach such Web scale stabilised consensus, as consensus from different systems might be conflicting with each other. There has been significant interests on combining Semantic Web and Web 2.0 [3,6,12]. It has been suggested [10,17,1,4] that Semantic Web technologies could help improve the internal structure of folksonomies. Passant proposed to encourage users to link tags with concepts and individuals in ontologies [17]. However, as suggested by Hotho et al. [10], a key question remains open: how to exploit the benefits of ontology without bothering untrained users with its rigidity. In this paper, we propose a novel approach to using ontology entailments to expand folksonomy search, without bothering untrained users to use ontologies directly. More precisely, the paper makes the following major contributions: 1. Our approach allows Web application developers to exploit the benefits of ontol- ogy without bothering untrained users with its rigidity. Ontology transparency is achieved by (i) automatic associating keywords with classes, properties and indi- viduals in domain ontologies (Section 4.1) and (ii) relating the search keyword(s) with keywords based on the structure of domain ontologies (Section 4.2). 2. We propose a general framework of ontology-based folksonomy search expansion methods for reducing ambiguity (Section 4), based on some use cases and observa- tions on search expansions (Section 3). For example, a search for “focus” could be expanded to include (some) album titles, songs titles, live performance venues, as well as keywords related to the domain in general. Hence, more meaningful results are returned to users than making use of the tagging systems directly. An interest- ing feature of the proposed methods is that all the required ontology entailments Reducing Ambiguity in Tagging Systems with Folksonomy Search Expansion 671 are included in OWL2 QL semantic approximations [15] of given domain ontolo- gies (Section 2). This suggests that theoretically the proposed methods can be very efficient. 3. We report the implementations in our ontology infrastructure of the proposed auto- matic keyword-ontology association and the proposed ontology-based folksonomy search expansion methods (Section 5). Preliminary evaluations indicate that these expansion methods are efficient even for some very large ontologies (Section 6). 4. We present a semantic mashup application, called MusicMash2, to demonstrate how the above ontology infrastructure could enhance current Web 2.0 application and experience (Section 5.2). MusicMash2 provides music domain-specific search features to retrieve data from folksonomy based systems, offering search by genre, artist, release and song, etc. We show the usefulness of our proposed methods by comparing MusicMash2 against using some folksonomy based system directly. To the best of our knowledge, this is the first published application which shows both (i) that expressive ontology languages like OWL DL can be useful for Web applica- tions and (ii) that querying service for OWL DL can be scalable enough to support such applications. 2 Preliminary 2.1 Folksonomies Informally speaking, a folksonomy describes users, resources and tags in such a way that users assign tags to resources [10]. Here we follow Mika’s [13] and Hotho et al.’s [10] formal model of folksonomies. Formally, a folksonomy is a tuple F = U, T , R,A,Θ,whereU, T , R are finite sets of users, tags and Web resources, resp., A ⊆ U×T ×R is called tag assignments, Θ is a set of tag relations; i.e., for each member ≺ of Θ, ≺⊆ U ×T ×T is a tag relation. As Mika [13] indicated, tag relations are often thesaurus relations, such as those specified in 5 the SKOS specification . In this paper, we consider tag relations such as ‘narrower’ ≺n, ‘broader’ ≺b and ‘related’ ≺r. For example, the following assignment u1, “cat”,r1 u1 says user u1 uses “cat” to tag the Web resource r1; the following tag relation “cat” ≺n “animal” says that the tag “cat” is narrower than the tag “animal” for user u1. There are some properties of the above three tag relations: u u 1. If tag1 ≺n tag2,thentag2 ≺b tag1, and vice versa. u u 2. If tag1 ≺r tag2,thentag2 ≺r tag1. It should also be noted that here we simply present a formal model of tag relations. How to extract such relations from folksonomy systems goes beyond the scope of the paper. 2.2 Ontologies and Their Semantic Approximations As a ‘representation of a shared conceptualisation’ of a specific domain [7,21], ontology is regarded as a key component of the Semantic Web. Practically, ontologies are used to 5 http://www.w3.org/TR/swbp-skos-core-spec/ 672 J.Z. Pan, S. Taylor, and E. Thomas capture the intended meaning of a set of shared and common vocabulary of a specific domain, including important concepts (such as Artist, Album and Song), properties (such as hasAlbum and hasSong) and individuals (objects). Recently, W3C standardised the OWL Web Ontology Language [2] in its Semantic Web Activity. It has been argued that the Web needs lightweight ontologies [13]. Recently, Cal- vanese et al proposed the OWL2 QL (also called DL-Lite) [5] ontology language which can express most features in UML class diagrams but still has a low reasoning overhead (worst case polynomial time, compared to worst case exponential time in the case of most widely used Description Logics-based ontology languages).