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466
Efficient semantic matching
, 2004
"... We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. We concentrate on classifications with tree structures. In semantic matching, correspondences are discovered by translating the natural language labels of nodes into prop ..."
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Cited by 855 (68 self)
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We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. We concentrate on classifications with tree structures. In semantic matching, correspondences are discovered by translating the natural language labels of nodes into propositional formulas, and by codifying matching into a propositional unsatisfiability problem. We distinguish between problems with conjunctive formulas and problems with disjunctive formulas, and present various optimizations. For instance, we propose a linear time algorithm which solves the first class of problems. According to the tests we have done so far, the optimizations substantially improve the time performance of the system.
Information retrieval in folksonomies: Search and ranking
- The Semantic Web: Research and Applications, volume 4011 of LNAI
, 2006
"... Abstract. Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, there exists n ..."
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Cited by 238 (32 self)
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Abstract. Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, there exists no foundational research for these systems. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. 1
Flickr tag recommendation based on collective knowledge
- IN WWW ’08: PROC. OF THE 17TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB
, 2008
"... Online photo services such as Flickr and Zooomr allow users to share their photos with family, friends, and the online community at large. An important facet of these services is that users manually annotate their photos using so called tags, which describe the contents of the photo or provide addit ..."
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Cited by 224 (1 self)
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Online photo services such as Flickr and Zooomr allow users to share their photos with family, friends, and the online community at large. An important facet of these services is that users manually annotate their photos using so called tags, which describe the contents of the photo or provide additional contextual and semantical information. In this paper we investigate how we can assist users in the tagging phase. The contribution of our research is twofold. We analyse a representative snapshot of Flickr and present the results by means of a tag characterisation focussing on how users tags photos and what information is contained in the tagging. Based on this analysis, we present and evaluate tag recommendation strategies to support the user in the photo annotation task by recommending a set of tags that can be added to the photo. The results of the empirical evaluation show that we can effectively recommend relevant tags for a variety of photos with different levels of exhaustiveness of original tagging.
The complex dynamics of collaborative tagging
- IN PROCEEDINGS OF INTERNATIONAL CONFERENCE
, 2007
"... The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including wheth ..."
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Cited by 177 (7 self)
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The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from tagged sites on the social bookmarking site del.icio.us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for “popular ” sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used analyze the meaning of particular tags given their relationship to other tags.
Optimizing web search using social annotations
- IN: WWW ’07
, 2007
"... This paper explores the use of social annotations to improve web search. Nowadays, many services, e.g. del.icio.us, have been developed for web users to organize and share their favorite web pages on line by using social annotations. We observe that the social annotations can benefit web search in t ..."
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Cited by 171 (2 self)
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This paper explores the use of social annotations to improve web search. Nowadays, many services, e.g. del.icio.us, have been developed for web users to organize and share their favorite web pages on line by using social annotations. We observe that the social annotations can benefit web search in two aspects: 1) the annotations are usually good summaries of corresponding web pages; 2) the count of annotations indicates the popularity of web pages. Two novel algorithms are proposed to incorporate the above information into page ranking: 1) SocialSimRank (SSR) calculates the similarity between social annotations and web queries; 2) SocialPageRank (SPR) captures the popularity of web pages. Preliminary experimental results show that SSR can find the latent semantic association between queries and annotations, while SPR successfully measures the quality (popularity) of a web page from the web users ’ perspective. We further evaluate the proposed methods empirically with 50 manually constructed queries and 3000 auto-generated queries on a dataset crawled from del.icio.us. Experiments show that both SSR and SPR benefit web search significantly.
Measuring semantic similarity between words using web search engines
- in Proceedings of WWW
, 2007
"... Measuring the semantic similarity between words is an important component in various semantic web-related applications such as community mining, relation extraction and au-tomatic meta data extraction. Despite the usefulness of semantic similarity measures in these applications, accurately measuring ..."
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Cited by 138 (7 self)
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Measuring the semantic similarity between words is an important component in various semantic web-related applications such as community mining, relation extraction and au-tomatic meta data extraction. Despite the usefulness of semantic similarity measures in these applications, accurately measuring semantic similarity between two words (or enti-ties) remains a challenging task. We propose a semantic similarity measure that uses the information available on the Web to measure similarity between words or entities. The pro-posed method exploits page counts and text snippets returned by a Web search engine. We define various similarity scores for two given words P and Q, using the page counts for the queries P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using automatically extracted lexical-syntactic patterns from text snippets. These different similarity scores are integrated using support vector machines, to leverage a robust semantic similarity measure. Experimental results on Miller-Charles benchmark dataset show that the proposed measure outperforms all the existing web-based semantic similar-ity measures by a wide margin, achieving a correlation coefficient of 0.867. Moreover, the proposed semantic similarity measure significantly improves the accuracy (F-measure of 0.78) in a community mining task, and in an entity disambiguation task, thereby verifying the capability of the proposed measure to capture semantic similarity using web content. Key words: semantic similarity measures, web mining, community extraction
Tag recommendations in folksonomies
- In PKDD
, 2007
"... Abstract. Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the pro ..."
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Cited by 123 (11 self)
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Abstract. Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare two recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graphbased recommender outperforms existing methods considerably. 1
Improving Tag-Clouds as Visual Information Retrieval Interfaces
- MERÍDA, INSCIT2006 CONFERENCE
, 2006
"... Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to refinding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksono ..."
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Cited by 114 (0 self)
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Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to refinding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud’s tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.
/facet: A Browser for Heterogeneous Semantic Web Repositories
, 2006
"... Facet browsing has become popular as a user friendly interface to data repositories. We extend facet browsing of Semantic Web data in five ways. First, users are able to select and navigate through facets of resources of any type and to make selections based on properties of other, semantically rela ..."
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Cited by 113 (8 self)
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Facet browsing has become popular as a user friendly interface to data repositories. We extend facet browsing of Semantic Web data in five ways. First, users are able to select and navigate through facets of resources of any type and to make selections based on properties of other, semantically related, types. We address a disadvantage of hierarchy-based navigation by adding a keyword search interface with semantic autocompletion. The interface of our browser, /facet, allows the inclusion of facet-specific display options that go beyond the hierarchical navigation that characterizes current facet browsing. Finally, the browser works on any RDFS dataset without any additional configuration.
Collective knowledge systems: Where the social web meets the semantic web
- Web Semantics: Science, Services and Agents on the World Wide Web
, 2008
"... Abstract: What can happen if we combine the best ideas from the Social Web and Semantic Web? The Social Web is an ecosystem of participation, where value is created by the aggregation of many individual user contributions. The Semantic Web is an ecosystem of data, where value is created by the integ ..."
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Cited by 111 (0 self)
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Abstract: What can happen if we combine the best ideas from the Social Web and Semantic Web? The Social Web is an ecosystem of participation, where value is created by the aggregation of many individual user contributions. The Semantic Web is an ecosystem of data, where value is created by the integration of structured data from many sources. What applications can best synthesize the strengths of these two approaches, to create a new level of value that is both rich with human participation and powered by well-structured information? This paper proposes a class of applications called collective knowledge systems, which unlock the "collective intelligence " of the Social Web with knowledge representation and reasoning techniques of the Semantic Web.
