Proceedings of I-SEMANTICS ’08 Graz, Austria, September 3-5, 2008
Semantic Tagging and Inference in Online Communities Ahmet Yıldırım (Bo˘gazi¸ci University, Turkey
[email protected]) ¨ udarlı Suzan Usk¨ (Bo˘gazi¸ci University, Turkey
[email protected])
Abstract: In this paper we present UsTag, an approach for providing user defined semantics for user generated content (UGC) and process those semantics with user defined rules. User semantics is provided with a tagging mechanism extended in order to express relationships within the content. These relationships are translated to RDF triples. RDF triples along with user defined rules enable the creation of an information space, where the content and its interpretation is provided by users. Key Words: semantic web, metadata, online communities, collective knowledge, tagging, semantic tagging Category: M.0, M.7
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Introduction
Recent advances in Web technologies, such as wikis and weblogs (blogs) have enabled novice users to become content producers in addition to consumers. Such technologies have dramatically increased user contribution resulting in a massive amount of content ranging across all human interests. While there is no shortage of Web content, technologies for effectively finding and utilizing this content remains quite limited. Our work focuses on enabling and utilizing user generated semantics. We introduce an extension to tagging for the purpose of providing user defined relationships between content. We, furthermore, introduce a mechanism for providing user defined rules for processing these relationships. Due to severe space limitations, we are only able to provide our work outline. In Section 2, we present related work, in Section 3, we describe our approach, in Section 4, we give future work and conclusions related to this work.
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Related Work
There are other approaches that also aim to enrich user generated content. Semantic Wikipedia enables users to embed relations in articles by extending the link syntax [Volkel et al.(2006)Volkel, Krotzsch, Vrandecic, Haller, and Studer].
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These markups relate the article to another subject with user defined relations. RDF triples are generated from these relations enabling semantic searching. SemKey [Marchetti et al.(2007)Marchetti, Tesconi, and Ronzano] enables associating tags with Web resources. Triples of are created, where the relations of three types: hasAsTopic, hasAsKind, and myOpiononIs. These relations are considered the most useful. Flick [Flickr(2004)] provides an API that helps users define machine processable information for the content. This API supports Machine Tags [Flickr(2008)] that essentially enable users to add extra information with the help of the tag syntax. Flickr machine tags have a namespace, a predicate, and a value. The value is not a semantic web URI, but can be considered as a semantic web literal value. Flickr does not export machine tags as RDF. Searching a content semantically can be done using Flickr API. MOAT [MOAT(2008)] is a framework that provides a mechanism for users to define tags and their meanings using semantic web URIs. MOAT comes with a client and a server. A moat client interacts with the server to retrieve tags and their meaning. While the user is entering the tag, if the intended meaning is not found, the user defines a URI for that tag. MOAT uses FOAF to identify people and relate tags to creators. EntityDescriber [Connotea(2008)] lets Connotea users tag resources with the terms coming from structured knowledge systems such as Freebase or ontologies.
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Our Approach: UsTag
UsTag is a User Generated Content (UGC) environment, that enables users to tag the content with its semantics and to define rules to process these semantics with an inference mechanism implemented in the system. These definitions lead to better search results, and easily finding and utilizing the content. Considering that we want average users to provide semantics, we need an easy and familiar mechanism. Users define the semantics by adding a predicate to the tag. The scenario that we envision is that someone will make a contribution, others will make corrections, additions, and define relationships by semantically tagging the content. 3.1
Definitions
We use the term “Conventional Tag” to refer a tag which is only a label seen in existing tagging systems. We use the term “predicate” to refer to the type of a relationship constructed by semantic tagging. A predicate can be entered while users are tagging the content. UsTag supports conventional tags by relating the content to the tag with the predicate “is-about”. “is-about” is the default predicate, but can be changed
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while installing the system. A predicate is not required to be entered. In this case, user feels like using a conventional tagging system. “Subject” is the tagged content, and “object” is the tag itself. A subject and an object can be related using a predicate. All subjects, objects and predicates are URI’s in the system. A subject, an object, and a predicate create a relation which is output as RDF triple. If user desires to input semantic information about the content, he clicks on the Tag button. The user enters a tag and a predicate for the tag. This type of tag is a “semantic tag”. While tagging, tags starting with what the user is typing are suggested via an auto complete area. A rule is used to process the inserted relationships. A rule is defined by the user via the rule definition interface for a specific predicate. A rule consist of an IF part and a THEN part. If the IF part of the rule is satisfied, then the relations defined in the THEN part are inserted into the system. 3.2
Semantic Search
In addtion to basic text based search, UsTag supports semantic search. User can ask a query in novel-author domain such as “Find Movements that influenced authors who are influenced by Modernism”. This query is not asked in natural language, but through semantic search interface. 3.3
Inference
UsTag supports inference when rules are input by user. As rules are processed, new relationships appears as results and these relationships are added to the relationship repository. Predicates and tags for a content are listed below the content in an infobox. The inferred relationships are also included in the infobox like user defined relationships. Both in basic search and semantic search, inferred relationships are taken into account. We have implemented inference mechanism using Jena[HPLabs.(2003)]. With an example, we will explain the inference mechanism. We will use “subject:predicate:object” notation to represent a relationship. Suppose that we have user defined relationships: “Berlin:located-in:Germany”, “Berlin:is-a:city”, and “Germany:located-in:Europe”. If a user defines “located-in” predicate as transitive, and when we query cities in Europe, Berlin appears in results. Defining “located-in” as transitive is a primitive action. User just clicks on the transitive button in the predicate properties page to declare it as transitive. In addition, the system allows definition of complex rules. For instance, in novels-authors domain, a user can define a rule such as “If a novel is influenced by a movement, then the author of the novel is also influenced by that movement.”. Rules are not defined in natural language, but in rule definition interface.
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Future Work and Conclusions
We have explained related work and UsTag. UsTag supports user defined rules and processing user defined rules with user defined relationships. These relationships emerges into the system by semantic tagging. For the future, we are planning to develop a simpler user interface for rule definition and semantic search. We are also planning to open the system for large scale user test and evaluation. Our initial experience for UsTag is that the system achieves its goals of remaining lightweight, as no tags need to be given and common tagging behavior is supported. The use of semantic tags are optional, however when given, they nicely extend the utility of the system with better search results that lead to more comprehensive content creation. User defined rules enabled inference and introduction of new relationships that are not input by users. This paper presents the first results of this work. The early prototype has been very beneficial in getting early feedback and provides a very useful platform for experimentation. Our experience is encouraging with respect three are primary motivation of enabling easy user content creation that is machine processable, processing this content for eliciting information that is not input by user, and effective information retrieval. We are continuing work on the approach as well as the system. Acknowledgements This work has been partially supported by BAP07A107 and BAP08A103 funding.
References [Connotea(2008)] Connotea. Entitydescriber, 2008. URL http://www.connotea.org/ wiki/EntityDescriber. [Flickr(2004)] Flickr. Flickr - The best way to store, search, sort and share your photos. http://www.flickr.com, 2004. URL http://www.flickr.com. [Flickr(2008)] Flickr. Machine tags wiki, 2008. URL http://www.machinetags.org/ wiki/. [HPLabs.(2003)] HPLabs. Jena Semantic Web Framework. http://jena. sourceforge.net/, 2003. URL http://jena.sourceforge.net/. [Marchetti et al.(2007)Marchetti, Tesconi, and Ronzano] Andrea Marchetti, Maurizio Tesconi, and Francesco Ronzano. Semkey: A semantic collaborative tagging system. 2007. [MOAT(2008)] MOAT. Meaning of a tag, 2008. URL http://moat-project.org/. [Volkel et al.(2006)Volkel, Krotzsch, Vrandecic, Haller, and Studer] Max Volkel, Markus Krotzsch, Denny Vrandecic, Heiko Haller, and Rudi Studer. Semantic wikipedia. In WWW ’06: Proceedings of the 15th international conference on World Wide Web, pages 585–594, New York, NY, USA, 2006. ACM Press. ISBN 1595933239. doi: 10.1145/1135777.1135863. URL http://portal.acm.org/citation.cfm?id=1135777.1135863.
