The document proposes tag-resource taxonomies as a way to more efficiently navigate social tagging systems compared to traditional tag taxonomies. It introduces an algorithm to generate tag-resource hierarchies that organizes both tags and resources. An evaluation of tag-resource taxonomies generated for a wiki dataset shows they have lower collision rates and better semantic structure than other taxonomy methods, and a user study found they allowed for more accurate navigation than a degree centrality/co-occurrence tag taxonomy.
Christoph Trattner gave a presentation on applying cognitive models to recommender systems. He discussed how ACT-R, an influential cognitive architecture, has been used to model temporal tagging behavior with power functions performing better than exponential functions. Experiments applying this approach achieved state-of-the-art results in predicting tag reuse and recommending tags. A two-step collaborative filtering approach that integrated tags and time (CIRTT) also outperformed baselines in recommending items. The presentation concluded with details on an open-source code framework for this research.
From Search to Predictions in Tagged Information SpacesChristoph Trattner
Tagging gained tremendously in popularity over past few years. When looking into the literature of tagging we find a lot of work regarding people's tagging motivation, their behavior, models that describe the folksonomy generation process, emergent semantic structures, etc., but interestingly we find quite little research showing the value of tags for searching an overloaded information space. Furthermore, there is lot of literature on the tag or item prediction problem, but interestingly almost all of them lookat the issue from a data-driven perspective. To bridge this gap in the literature, we have conducted several in-depth studies in the past showing the value of tags for lookup and exploratory search. We looked at the problem from a network theoretic and interface perspective and we will show how useful tags are for searching. Furthermore, we reviewed literature on memory processes from cognitive science and have invented a number of novel recommender algorithms based on the ACT-R and MINERVA2 theory. We will show that these approaches can not only predict tags and items extremely well, but also reveal how these models can help in explaining the recommendation processes better than current approaches.
This document summarizes Christoph Trattner's presentation on crowd-based evaluations. It discusses that Trattner is from Austria and currently works at the Know-Center in Graz, Austria, where his group conducts research using social data and crowdsourcing. The presentation defines crowdsourcing and crowd-based evaluation, and provides examples of popular crowdsourcing platforms like Amazon Mechanical Turk and CrowdFlower that are commonly used for evaluations. It also shares statistics about the demographics of workers on these platforms and discusses whether crowdsourcing is a reasonable alternative to traditional user studies.
Evaluating Tag-Based Information Access in Image CollectionsChristoph Trattner
The availability of social tags has greatly enhanced access to information.
Tag clouds have emerged as a new “social” way to find
and visualize information, providing both one-click access to information
and a snapshot of the “aboutness” of a tagged collection.
A range of research projects explored and compared different tag
artifacts for information access ranging from regular tag clouds to
tag hierarchies. At the same time, there is a lack of user studies that
compare the effectiveness of different types of tag-based browsing
interfaces from the users point of view. This paper contributes to
the research on tag-based information access by presenting a controlled
user study that compared three types of tag-based interfaces
on two recognized types of search tasks – lookup and exploratory
search. Our results demonstrate that tag-based browsing interfaces
significantly outperform traditional search interfaces in both performance
and user satisfaction. At the same time, the differences
between the two types of tag-based browsing interfaces explored in
our study are not as clear.
Je t’aime… moi non plus: reporting on the opportunities, expectations and cha...Christoph Trattner
This presentation will be a live exchange of ideas & arguments, between a representative of a start up working on agricultural information management and discovery, and a representative of academia that has recently completed his PhD and is now leading a young and promising research team.
The two presenters will focus on the case of a recommendation service that is going to be part of a web portal for organic agriculture researchers and educators (called Organic.Edunet), which will help users find relevant educational material and bibliography. They currently develop this as part of an EU-funded initiative but would both be interested to find a way to further sustain this work: the start up by including this to the bundle of services that it offers to the users of its information discovery packages, and the research team by attracting more funding to further explore recommendation technologies.
The start up representative will describe his evergoing, helpless and aimless efforts to include a research activity on recommender systems within the R&D strategy of the company, for the sakes of the good-old-PhD-times. And will explain why this failed.
The academia representative will describe the great things that his research can do to boost the performance of recommendation services in such portals. And why this does-not-work-yet-operationally because he cannot find real usage data that can prove his amazing algorithm outside what can be proven in offline lab experiments using datasets from other domains (like MovieLens and CiteULike).
Both will explain how they started working together in order to design, experimentally test, and deploy the Organic.Edunet recommendation service. And will describe their expectations from this academic-industry collaboration. Then, they will reflect on the challenges they see in such partnerships and how (if) they plan to overcome them.
Towards a Big Data Recommender Engine for Online and Offline MarketplacesChristoph Trattner
Recommender systems aim at helping users to find relevant information in an overloaded information space.
Although there are well known methods (Content-based, Collaborative Filtering, Matrix Factorization) and libraries to implement, evaluate and extend recommenders (Apache Mahout, Graphlab, MyMediaLite, among others), the deployment of a real-time recommender from scratch which considers a combination of algorithms and various data sources (e.g., social, transactional, and location) remains unsolved.
In this talk, we report on the challenges towards such a recommender systems in the context of online of offline marketplaces. In particular, we describe our solution in terms of the requirements, the data model and algorithms that allows modularity and extensibility, as well as the system architecture to facilitate the scaling of our approach to big data for online and offline marketplaces.
This document summarizes research on improving the navigability of social tagging systems. It discusses how tagging systems work and why tags are useful for both system designers and users. It then analyzes how navigability in networks is defined and evaluates the navigability of social tagging networks. The document proposes constructing hierarchical resource lists and tag clouds using background knowledge from structured sources like Wikipedia. An evaluation of this approach through network analysis and a user study found it significantly improved users' ability to navigate the tagging system compared to reverse chronological ordering of resources.
Christoph Trattner gave a presentation on applying cognitive models to recommender systems. He discussed how ACT-R, an influential cognitive architecture, has been used to model temporal tagging behavior with power functions performing better than exponential functions. Experiments applying this approach achieved state-of-the-art results in predicting tag reuse and recommending tags. A two-step collaborative filtering approach that integrated tags and time (CIRTT) also outperformed baselines in recommending items. The presentation concluded with details on an open-source code framework for this research.
From Search to Predictions in Tagged Information SpacesChristoph Trattner
Tagging gained tremendously in popularity over past few years. When looking into the literature of tagging we find a lot of work regarding people's tagging motivation, their behavior, models that describe the folksonomy generation process, emergent semantic structures, etc., but interestingly we find quite little research showing the value of tags for searching an overloaded information space. Furthermore, there is lot of literature on the tag or item prediction problem, but interestingly almost all of them lookat the issue from a data-driven perspective. To bridge this gap in the literature, we have conducted several in-depth studies in the past showing the value of tags for lookup and exploratory search. We looked at the problem from a network theoretic and interface perspective and we will show how useful tags are for searching. Furthermore, we reviewed literature on memory processes from cognitive science and have invented a number of novel recommender algorithms based on the ACT-R and MINERVA2 theory. We will show that these approaches can not only predict tags and items extremely well, but also reveal how these models can help in explaining the recommendation processes better than current approaches.
This document summarizes Christoph Trattner's presentation on crowd-based evaluations. It discusses that Trattner is from Austria and currently works at the Know-Center in Graz, Austria, where his group conducts research using social data and crowdsourcing. The presentation defines crowdsourcing and crowd-based evaluation, and provides examples of popular crowdsourcing platforms like Amazon Mechanical Turk and CrowdFlower that are commonly used for evaluations. It also shares statistics about the demographics of workers on these platforms and discusses whether crowdsourcing is a reasonable alternative to traditional user studies.
Evaluating Tag-Based Information Access in Image CollectionsChristoph Trattner
The availability of social tags has greatly enhanced access to information.
Tag clouds have emerged as a new “social” way to find
and visualize information, providing both one-click access to information
and a snapshot of the “aboutness” of a tagged collection.
A range of research projects explored and compared different tag
artifacts for information access ranging from regular tag clouds to
tag hierarchies. At the same time, there is a lack of user studies that
compare the effectiveness of different types of tag-based browsing
interfaces from the users point of view. This paper contributes to
the research on tag-based information access by presenting a controlled
user study that compared three types of tag-based interfaces
on two recognized types of search tasks – lookup and exploratory
search. Our results demonstrate that tag-based browsing interfaces
significantly outperform traditional search interfaces in both performance
and user satisfaction. At the same time, the differences
between the two types of tag-based browsing interfaces explored in
our study are not as clear.
Je t’aime… moi non plus: reporting on the opportunities, expectations and cha...Christoph Trattner
This presentation will be a live exchange of ideas & arguments, between a representative of a start up working on agricultural information management and discovery, and a representative of academia that has recently completed his PhD and is now leading a young and promising research team.
The two presenters will focus on the case of a recommendation service that is going to be part of a web portal for organic agriculture researchers and educators (called Organic.Edunet), which will help users find relevant educational material and bibliography. They currently develop this as part of an EU-funded initiative but would both be interested to find a way to further sustain this work: the start up by including this to the bundle of services that it offers to the users of its information discovery packages, and the research team by attracting more funding to further explore recommendation technologies.
The start up representative will describe his evergoing, helpless and aimless efforts to include a research activity on recommender systems within the R&D strategy of the company, for the sakes of the good-old-PhD-times. And will explain why this failed.
The academia representative will describe the great things that his research can do to boost the performance of recommendation services in such portals. And why this does-not-work-yet-operationally because he cannot find real usage data that can prove his amazing algorithm outside what can be proven in offline lab experiments using datasets from other domains (like MovieLens and CiteULike).
Both will explain how they started working together in order to design, experimentally test, and deploy the Organic.Edunet recommendation service. And will describe their expectations from this academic-industry collaboration. Then, they will reflect on the challenges they see in such partnerships and how (if) they plan to overcome them.
Towards a Big Data Recommender Engine for Online and Offline MarketplacesChristoph Trattner
Recommender systems aim at helping users to find relevant information in an overloaded information space.
Although there are well known methods (Content-based, Collaborative Filtering, Matrix Factorization) and libraries to implement, evaluate and extend recommenders (Apache Mahout, Graphlab, MyMediaLite, among others), the deployment of a real-time recommender from scratch which considers a combination of algorithms and various data sources (e.g., social, transactional, and location) remains unsolved.
In this talk, we report on the challenges towards such a recommender systems in the context of online of offline marketplaces. In particular, we describe our solution in terms of the requirements, the data model and algorithms that allows modularity and extensibility, as well as the system architecture to facilitate the scaling of our approach to big data for online and offline marketplaces.
This document summarizes research on improving the navigability of social tagging systems. It discusses how tagging systems work and why tags are useful for both system designers and users. It then analyzes how navigability in networks is defined and evaluates the navigability of social tagging networks. The document proposes constructing hierarchical resource lists and tag clouds using background knowledge from structured sources like Wikipedia. An evaluation of this approach through network analysis and a user study found it significantly improved users' ability to navigate the tagging system compared to reverse chronological ordering of resources.
The document discusses a framework for evaluating concept hierarchies without relying on a golden standard. It introduces Kleinberg's idea of hierarchical decentralized search, where background knowledge defines the distance between nodes. The framework determines a concept hierarchy's pragmatic usefulness by modeling how efficiently a decentralized searcher could find information in the hierarchy. The researchers believe this framework can objectively evaluate automatically generated hierarchies from folksonomies without a pre-defined taxonomy.
Recommending Items in Social Tagging Systems Using Tag and Time InformationChristoph Trattner
In this work we present a novel item recommendation ap- proach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this can- didate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation com- ing from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data- sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.
On the Utility of Tags for Search and Navigation in Online Information SystemsChristoph Trattner
This document discusses a presentation given by Christoph Trattner at Graz University of Technology. The presentation focuses on the usefulness of tags and tag clouds for search and navigation in online information systems. Trattner analyzes tagging systems as graphs and defines navigability. The results of the study indicate that tags form navigable networks that allow for efficient navigation, though the utility of tag clouds for navigation is less clear and may depend on factors like cloud size and pagination of resources.
The document discusses network navigability and presents research on how structural properties of networks enable efficient decentralized search. It introduces key concepts of network navigability such as short path lengths between nodes and bounded effective diameter. The document explains that while global knowledge allows centralized search algorithms, real networks only provide local knowledge. It argues that power-law degree distributions, network hubs and clustered connections allow algorithms to find short paths using only local information by guiding selection of optimal next nodes.
Studying Online Food Consumption and Production Patterns: Recent Trends and C...Christoph Trattner
Food is a fundamental concept in our daily lives and is one of the most important factors that shape how healthy we are or how good we feel. Although research on the users’ food preferences has been a well-established research area over the last decades, only very little research was devoted yet to understand, how the World Wide Web influences the way we consume or produce food offline.
In this talk, I will therefore highlight recent research in online food communities and interesting findings in terms of online food recipe consumption and production patterns. I will show, how these studies might be useful when drawing conclusions about health related issues, such as obesity or diabetes of a large population, and how these insights might be used to tune current recommender approaches in this domain.
Last but not least, I will discuss the limitations of these studies and will highlight the need for a joined European taskforce, that studies food, health related issues and recommender systems on a much larger and more useful way, as proposed by current research in this area.
Understanding the Impact of Weather for POI RecommendationsChristoph Trattner
POI recommender systems for location-based social network services, such as Foursquare or Yelp, have gained tremendous popularity in the past few years. Much work has been dedicated into improving recommendation services in such systems by integrating different features that are assumed to have an impact on people's preferences for POIs, such as time and geolocation. Yet, little attention has been paid to the impact of weather on the users' final decision to visit a recommended POI. In this paper we contribute to this area of research by presenting the first results of a study that aims to recommend POIs based on weather data. To this end, we extend the state-of-the-art Rank-GeoFM POI recommender algorithm with additional weather-related features, such as temperature, cloud cover, humidity and precipitation intensity. We show that using weather data not only significantly increases the recommendation accuracy in comparison to the original algorithm, but also outperforms its time-based variant. Furthermore, we present the magnitude of impact of each feature on the recommendation quality, showing the need to study the weather context in more detail in the light of POI recommendation systems.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
Social Computing in the area of Big Data at the Know-Center Austria's leading...Christoph Trattner
Nowadays, social networks and media, such as Facebook, Twitter & Co, affect our communication and our exchange of knowledge more than ever. But which additional benefits can offer social media apart from easy interaction with friends and how can they be used to create additional value for companies and institutions? These are the questions that the area Social Computing at Know-Center addresses in detail.
In this talk we will give a brief overview of industry and non-industry related research projects which we have been involved in recently with my group, Social Computing at the Know-Center, in the context of Big Data and social media. In particular, the talk will highlight specific research project outcomes and work-in-progress that make use of social media data to help people to explore the vastly growing overloaded information space more efficiently.
Recommending Tags with a Model of Human CategorizationChristoph Trattner
Social tagging involves complex processes of human categorization that have been the topic of much research in the cognitive sciences. In this paper we present a recommender approach for social tags whose principles are derived from some of the more prominent and empirically well-founded models from this research tradition. The basic architecture is a simple three-layers connectionist model. The input layer encodes patterns of semantic features of a user-specific re- source, which are either latent topics elicited through Latent Dirichlet Allocation (LDA) or available external categories. The hidden layer categorizes the resource by matching the encoded pattern against already learned exemplar patterns. The latter are composed of unique feature patterns and associated tag distributions. Finally, the output layer samples tags from the associated tag distributions to verbalize the preceding categorization process. We have evaluated this approach on a real-world folksonomy gathered from Wikipedia bookmarks in Delicious. In the experiment our approach outperformed LDA, a well-established algorithm. We at- tribute this to the fact that our approach processes seman- tic information (either latent topics or external categories) across the three different layers, and this substantially enhances the recommendation performance. With this paper, we demonstrate that a theoretically guided design of algorithms not only holds potential for improving existing recommendation mechanisms, but it also allows us to derive more generalizable insights about how human information interaction on the Web is determined by both semantic and verbal processes.
Stop thinking, start tagging - Tag Semantics emerge from Collaborative VerbosityInovex GmbH
Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.
This document discusses navigability in social tagging systems. It begins by defining social tagging systems and folksonomies. It then examines factors that influence navigability in social tagging systems like motivations for tagging. It analyzes how tag clouds and hierarchies can be used for navigation but notes that user interface constraints like tag cloud size and pagination can impair navigability. It concludes that certain popular approaches to tag clouds do not support navigability and new approaches are needed that consider the trade-off between semantic and navigational properties.
This document describes THIC (Thresholded Hierarchical Itemset Clustering), a clustering algorithm that allows experts to provide feedback to guide the clustering process. THIC aims to produce more interpretable clusters compared to traditional algorithms. It clusters datasets in an interactive interface that lets users select features, group priorities, and number of groups. THIC explains cluster patterns based on user-defined preferences rather than just optimal clustering. The goal is to help users better understand complex datasets by finding patterns not evident otherwise.
This document describes a study that aims to improve tag clouds as visual information retrieval interfaces. The researchers propose selecting tags based on their usefulness in representing resources rather than just frequency of use. They define tag usefulness based on how well a tag represents the resources it is assigned to compared to other tags of those resources, as well as how many unique resources it covers. They also propose grouping tags visually based on similarity using clustering techniques. An evaluation on a sample of tagged web resources found their proposed tag selection method achieved better coverage of resources with less overlap between tags compared to traditional frequency-based selection.
Towards Mining Semantic Maturity in Social Bookmarking SystemsInovex GmbH
The document discusses mining for indicators of "semantic maturity" in tags assigned in social bookmarking systems. It explores using tag frequency, centrality within co-occurrence networks, and pattern mining to discover combinations of properties that correlate with two measures of semantic maturity: identification of tag synonyms and relationships within WordNet taxonomies. Analysis of a dataset from Delicious identified patterns where combined properties like degree centrality and user frequency achieved higher maturity rates than individual properties. This work demonstrates the potential of mining approaches to assess semantic development of tags in folksonomies over time.
Ontology engineering, along with semantic Web technologies, allow the semantic development and modeling of the operational flow required for blockchain design. The semantic Web, in accordance with W3C, "provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries" and can be seen as an integrator for various content, applications and information systems. The most widely used blockchain modelling system, by abstract representation, description and definition of structure, processes, information and resources, is the enterprises modelling. Enterprise modelling uses domain ontologies by model representation languages.
DOI: 10.13140/RG.2.2.19062.24642
A Model For Semantic Annotation Of Environmental Resources The Tatoo Semanti...Andrew Molina
The document discusses the TaToo semantic framework, which aims to improve discovery of environmental resources by semantically annotating them. It does this using a hybrid ontology approach, with a shared "bridge" ontology and linked domain-specific ontologies. This allows formal annotations that machines can understand, while integrating different sub-domains. The current framework links geospatial and temporal ontologies through the bridge ontology to enable cross-domain search based on location and time.
Improving Personal Tagging Consistency Through Visualization Of TagQin Gao
This document summarizes a study that examined how visualizing tag frequency and semantic relationships between tags impacts personal tagging consistency. The study found that visualizing semantic relationships between tags through clustering significantly improved tagging consistency, supporting the hypothesis. However, visualizing only tag frequency did not significantly impact consistency and increased mental workload. When combined with semantic clustering, frequency visualization reduced physical demands but increased mental demands. The results provide empirical support for visualizing semantic relationships to aid tagging consistency over visualizing frequency alone.
FaceTag is a working prototype of a semantic collaborative tagging tool conceived for bookmarking information architecture resources. It aims to show how the flat keywords space of user-generated tags can be effectively mixed with a richer faceted classification scheme to improve the system information architecture.
FaceTag is a social tagging system that aims to improve navigation and findability of user-generated content by integrating bottom-up collaborative tagging with top-down classification approaches like taxonomies and facets. It analyzes user tags and assigns them to predefined facets like date, people, and language to provide a more structured tagging experience. The system also provides hierarchical tag suggestions and faceted browsing to help users more easily explore and discover information across large collections of resources.
FaceTag: Integrating Bottom-up and Top-down Classification in a Social Taggin...Andrea Resmini
FaceTag is a working prototype of a semantic collaborative tagging tool conceived for bookmarking information architecture resources.
It aims to show how the widespread homogeneous and flat keywords' space created by users while tagging can be effectively mixed with a richer faceted classification scheme to improve the �information scent� and �berrypicking� capabilities of the system. The additional semantic structure is aggregated both implicitly observing user behaviour and explicitly introducing a compelling user experience that facilitates the end-user creation of relationships between tags.
FaceTag current implementation is written in PHP / SQL and includes an open API which allows querying and integration from other applications.
Semantic domain ontologies are increasingly seen as the key for enabling
interoperability across heterogeneous systems and sensor-based applications.
The ontologies deployed in these systems and applications are developed by
restricted groups of domain experts and not by semantic web experts. Lately,
folksonomies are increasingly exploited in developing ontologies. The
“collective intelligence”, which emerge from collaborative tagging can be
seen as an alternative for the current effort at semantic web ontologies.
However, the uncontrolled nature of social tagging systems leads to many
kinds of noisy annotations, such as misspellings, imprecision and ambiguity.
Thus, the construction of formal ontologies from social tagging data remains
a real challenge. Most of researches have focused on how to discover
relatedness between tags rather than producing ontologies, much less domain
ontologies. This paper proposed an algorithm that utilises tags in social
tagging systems to automatically generate up-to-date specific-domain
ontologies. The evaluation of the algorithm, using a dataset extracted from
BibSonomy, demonstrated that the algorithm could effectively learn a
domain terminology, and identify more meaningful semantic information for
the domain terminology. Furthermore, the proposed algorithm introduced a
simple and effective method for disambiguating tags.
The document discusses a framework for evaluating concept hierarchies without relying on a golden standard. It introduces Kleinberg's idea of hierarchical decentralized search, where background knowledge defines the distance between nodes. The framework determines a concept hierarchy's pragmatic usefulness by modeling how efficiently a decentralized searcher could find information in the hierarchy. The researchers believe this framework can objectively evaluate automatically generated hierarchies from folksonomies without a pre-defined taxonomy.
Recommending Items in Social Tagging Systems Using Tag and Time InformationChristoph Trattner
In this work we present a novel item recommendation ap- proach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this can- didate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation com- ing from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data- sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.
On the Utility of Tags for Search and Navigation in Online Information SystemsChristoph Trattner
This document discusses a presentation given by Christoph Trattner at Graz University of Technology. The presentation focuses on the usefulness of tags and tag clouds for search and navigation in online information systems. Trattner analyzes tagging systems as graphs and defines navigability. The results of the study indicate that tags form navigable networks that allow for efficient navigation, though the utility of tag clouds for navigation is less clear and may depend on factors like cloud size and pagination of resources.
The document discusses network navigability and presents research on how structural properties of networks enable efficient decentralized search. It introduces key concepts of network navigability such as short path lengths between nodes and bounded effective diameter. The document explains that while global knowledge allows centralized search algorithms, real networks only provide local knowledge. It argues that power-law degree distributions, network hubs and clustered connections allow algorithms to find short paths using only local information by guiding selection of optimal next nodes.
Studying Online Food Consumption and Production Patterns: Recent Trends and C...Christoph Trattner
Food is a fundamental concept in our daily lives and is one of the most important factors that shape how healthy we are or how good we feel. Although research on the users’ food preferences has been a well-established research area over the last decades, only very little research was devoted yet to understand, how the World Wide Web influences the way we consume or produce food offline.
In this talk, I will therefore highlight recent research in online food communities and interesting findings in terms of online food recipe consumption and production patterns. I will show, how these studies might be useful when drawing conclusions about health related issues, such as obesity or diabetes of a large population, and how these insights might be used to tune current recommender approaches in this domain.
Last but not least, I will discuss the limitations of these studies and will highlight the need for a joined European taskforce, that studies food, health related issues and recommender systems on a much larger and more useful way, as proposed by current research in this area.
Understanding the Impact of Weather for POI RecommendationsChristoph Trattner
POI recommender systems for location-based social network services, such as Foursquare or Yelp, have gained tremendous popularity in the past few years. Much work has been dedicated into improving recommendation services in such systems by integrating different features that are assumed to have an impact on people's preferences for POIs, such as time and geolocation. Yet, little attention has been paid to the impact of weather on the users' final decision to visit a recommended POI. In this paper we contribute to this area of research by presenting the first results of a study that aims to recommend POIs based on weather data. To this end, we extend the state-of-the-art Rank-GeoFM POI recommender algorithm with additional weather-related features, such as temperature, cloud cover, humidity and precipitation intensity. We show that using weather data not only significantly increases the recommendation accuracy in comparison to the original algorithm, but also outperforms its time-based variant. Furthermore, we present the magnitude of impact of each feature on the recommendation quality, showing the need to study the weather context in more detail in the light of POI recommendation systems.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
Social Computing in the area of Big Data at the Know-Center Austria's leading...Christoph Trattner
Nowadays, social networks and media, such as Facebook, Twitter & Co, affect our communication and our exchange of knowledge more than ever. But which additional benefits can offer social media apart from easy interaction with friends and how can they be used to create additional value for companies and institutions? These are the questions that the area Social Computing at Know-Center addresses in detail.
In this talk we will give a brief overview of industry and non-industry related research projects which we have been involved in recently with my group, Social Computing at the Know-Center, in the context of Big Data and social media. In particular, the talk will highlight specific research project outcomes and work-in-progress that make use of social media data to help people to explore the vastly growing overloaded information space more efficiently.
Recommending Tags with a Model of Human CategorizationChristoph Trattner
Social tagging involves complex processes of human categorization that have been the topic of much research in the cognitive sciences. In this paper we present a recommender approach for social tags whose principles are derived from some of the more prominent and empirically well-founded models from this research tradition. The basic architecture is a simple three-layers connectionist model. The input layer encodes patterns of semantic features of a user-specific re- source, which are either latent topics elicited through Latent Dirichlet Allocation (LDA) or available external categories. The hidden layer categorizes the resource by matching the encoded pattern against already learned exemplar patterns. The latter are composed of unique feature patterns and associated tag distributions. Finally, the output layer samples tags from the associated tag distributions to verbalize the preceding categorization process. We have evaluated this approach on a real-world folksonomy gathered from Wikipedia bookmarks in Delicious. In the experiment our approach outperformed LDA, a well-established algorithm. We at- tribute this to the fact that our approach processes seman- tic information (either latent topics or external categories) across the three different layers, and this substantially enhances the recommendation performance. With this paper, we demonstrate that a theoretically guided design of algorithms not only holds potential for improving existing recommendation mechanisms, but it also allows us to derive more generalizable insights about how human information interaction on the Web is determined by both semantic and verbal processes.
Stop thinking, start tagging - Tag Semantics emerge from Collaborative VerbosityInovex GmbH
Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.
This document discusses navigability in social tagging systems. It begins by defining social tagging systems and folksonomies. It then examines factors that influence navigability in social tagging systems like motivations for tagging. It analyzes how tag clouds and hierarchies can be used for navigation but notes that user interface constraints like tag cloud size and pagination can impair navigability. It concludes that certain popular approaches to tag clouds do not support navigability and new approaches are needed that consider the trade-off between semantic and navigational properties.
This document describes THIC (Thresholded Hierarchical Itemset Clustering), a clustering algorithm that allows experts to provide feedback to guide the clustering process. THIC aims to produce more interpretable clusters compared to traditional algorithms. It clusters datasets in an interactive interface that lets users select features, group priorities, and number of groups. THIC explains cluster patterns based on user-defined preferences rather than just optimal clustering. The goal is to help users better understand complex datasets by finding patterns not evident otherwise.
This document describes a study that aims to improve tag clouds as visual information retrieval interfaces. The researchers propose selecting tags based on their usefulness in representing resources rather than just frequency of use. They define tag usefulness based on how well a tag represents the resources it is assigned to compared to other tags of those resources, as well as how many unique resources it covers. They also propose grouping tags visually based on similarity using clustering techniques. An evaluation on a sample of tagged web resources found their proposed tag selection method achieved better coverage of resources with less overlap between tags compared to traditional frequency-based selection.
Towards Mining Semantic Maturity in Social Bookmarking SystemsInovex GmbH
The document discusses mining for indicators of "semantic maturity" in tags assigned in social bookmarking systems. It explores using tag frequency, centrality within co-occurrence networks, and pattern mining to discover combinations of properties that correlate with two measures of semantic maturity: identification of tag synonyms and relationships within WordNet taxonomies. Analysis of a dataset from Delicious identified patterns where combined properties like degree centrality and user frequency achieved higher maturity rates than individual properties. This work demonstrates the potential of mining approaches to assess semantic development of tags in folksonomies over time.
Ontology engineering, along with semantic Web technologies, allow the semantic development and modeling of the operational flow required for blockchain design. The semantic Web, in accordance with W3C, "provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries" and can be seen as an integrator for various content, applications and information systems. The most widely used blockchain modelling system, by abstract representation, description and definition of structure, processes, information and resources, is the enterprises modelling. Enterprise modelling uses domain ontologies by model representation languages.
DOI: 10.13140/RG.2.2.19062.24642
A Model For Semantic Annotation Of Environmental Resources The Tatoo Semanti...Andrew Molina
The document discusses the TaToo semantic framework, which aims to improve discovery of environmental resources by semantically annotating them. It does this using a hybrid ontology approach, with a shared "bridge" ontology and linked domain-specific ontologies. This allows formal annotations that machines can understand, while integrating different sub-domains. The current framework links geospatial and temporal ontologies through the bridge ontology to enable cross-domain search based on location and time.
Improving Personal Tagging Consistency Through Visualization Of TagQin Gao
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FaceTag is a working prototype of a semantic collaborative tagging tool conceived for bookmarking information architecture resources. It aims to show how the flat keywords space of user-generated tags can be effectively mixed with a richer faceted classification scheme to improve the system information architecture.
FaceTag is a social tagging system that aims to improve navigation and findability of user-generated content by integrating bottom-up collaborative tagging with top-down classification approaches like taxonomies and facets. It analyzes user tags and assigns them to predefined facets like date, people, and language to provide a more structured tagging experience. The system also provides hierarchical tag suggestions and faceted browsing to help users more easily explore and discover information across large collections of resources.
FaceTag: Integrating Bottom-up and Top-down Classification in a Social Taggin...Andrea Resmini
FaceTag is a working prototype of a semantic collaborative tagging tool conceived for bookmarking information architecture resources.
It aims to show how the widespread homogeneous and flat keywords' space created by users while tagging can be effectively mixed with a richer faceted classification scheme to improve the �information scent� and �berrypicking� capabilities of the system. The additional semantic structure is aggregated both implicitly observing user behaviour and explicitly introducing a compelling user experience that facilitates the end-user creation of relationships between tags.
FaceTag current implementation is written in PHP / SQL and includes an open API which allows querying and integration from other applications.
Semantic domain ontologies are increasingly seen as the key for enabling
interoperability across heterogeneous systems and sensor-based applications.
The ontologies deployed in these systems and applications are developed by
restricted groups of domain experts and not by semantic web experts. Lately,
folksonomies are increasingly exploited in developing ontologies. The
“collective intelligence”, which emerge from collaborative tagging can be
seen as an alternative for the current effort at semantic web ontologies.
However, the uncontrolled nature of social tagging systems leads to many
kinds of noisy annotations, such as misspellings, imprecision and ambiguity.
Thus, the construction of formal ontologies from social tagging data remains
a real challenge. Most of researches have focused on how to discover
relatedness between tags rather than producing ontologies, much less domain
ontologies. This paper proposed an algorithm that utilises tags in social
tagging systems to automatically generate up-to-date specific-domain
ontologies. The evaluation of the algorithm, using a dataset extracted from
BibSonomy, demonstrated that the algorithm could effectively learn a
domain terminology, and identify more meaningful semantic information for
the domain terminology. Furthermore, the proposed algorithm introduced a
simple and effective method for disambiguating tags.
FACILITATING VIDEO SOCIAL MEDIA SEARCH USING SOCIAL-DRIVEN TAGS COMPUTINGcsandit
Online video search or stream live on social media has become tremendous widespread and
speedy increased continuously in recent years. Most of the videos shared on social media are
aimed at the more number of views from audiences. What and how many videos the users
shared all around the world have created a great amount and varied videos and the other data
into Internet cloud’s database and even can be viewed as a kind of big data of digital contents.
This research is to present how to implement a social-driven tags computing (SDT) which can
be used to facilitate online video search on social media platforms
This document summarizes an algorithm for efficiently clustering short messages like tweets into general domains or topics. The algorithm breaks the clustering task into two stages: (1) batch clustering of user-annotated data using hashtags to create dense virtual documents, and (2) online clustering of new tweets using the centroids from stage 1. Experimental results show the algorithm outperforms other clustering methods and can accurately cluster large streams of sparse, short messages efficiently.
Data mining , knowledge discovery is the process
of analyzing data from different perspectives and summarizing it
into useful information - information that can be used to increase
revenue, cuts costs, or both. Data mining software is one of a
number of analytical tools for analyzing data. It allows users to
analyze data from many different dimensions or angles, categorize
it, and summarize the relationships identified. Technically, data
mining is the process of finding correlations or patterns among
dozens of fields in large relational databases. The goal of
clustering is to determine the intrinsic grouping in a set of
unlabeled data. But how to decide what constitutes a good
clustering? It can be shown that there is no absolute “best”
criterion which would be independent of the final aim of the
clustering. Consequently, it is the user which must supply this
criterion, in such a way that the result of the clustering will suit
their needs.
For instance, we could be interested in finding
representatives for homogeneous groups (data reduction), in
finding “natural clusters” and describe their unknown properties
(“natural” data types), in finding useful and suitable groupings
(“useful” data classes) or in finding unusual data objects (outlier
detection).Of late, clustering techniques have been applied in the
areas which involve browsing the gathered data or in categorizing
the outcome provided by the search engines for the reply to the
query raised by the users. In this paper, we are providing a
comprehensive survey over the document clustering.
User issues in top-down bottom-up tagging applications: FaceTagAndrea Resmini
This document discusses user issues that arise in FaceTag, a bottom-up social tagging application that also incorporates top-down facets. It notes linguistic and user experience disadvantages to pure tagging, such as polysemy and visual clutter. FaceTag introduces facets to provide structure and context to tags, allowing navigation along multiple dimensions simultaneously and iterative query refinement. However, the first iteration of FaceTag's user interface had usability issues. A second iteration was designed using user-centered design with paper prototyping and user testing. The document concludes that back-end issues around entering tags still need investigation.
Applications Of Clustering Techniques In Data Mining A Comparative StudyFiona Phillips
This document discusses and compares various clustering techniques used in data mining. It begins with an introduction to data mining and clustering. It then discusses different types of clustering (hard vs soft), popular clustering methodologies like K-means, hierarchical, density-based etc. It provides examples of clustering applications. The document also discusses challenges in clustering large datasets and proposes approaches like MapReduce. It evaluates pros and cons of different clustering algorithms and their real-world applications.
Meaning as Collective Use: Predicting Semantic Hashtag Categories on TwitterGabriela Agustini
This paper sets out to explore whether data about the us-
age of hashtags on Twitter contains information about their
semantics. Towards that end, we perform initial statisti-
cal hypothesis tests to quantify the association between us-
age patterns and semantics of hashtags. To assess the util-
ity of pragmatic features { which describe how a hashtag
is used over time { for semantic analysis of hashtags, we
conduct various hashtag stream classication experiments
and compare their utility with the utility of lexical features.
Our results indicate that pragmatic features indeed contain
valuable information for classifying hashtags into semantic
categories. Although pragmatic features do not outperform
lexical features in our experiments, we argue that pragmatic
features are important and relevant for settings in which tex-
tual information might be sparse or absent (e.g., in social
video streams).
This document describes a study that developed a framework to analyze the conceptual structure of sustainability from social and scholarly perspectives. Researchers collected term data from academic and consumer sources and analyzed similarities between terms to cluster them and identify higher-level "motifs". Three major semantic domains of sustainability were identified: environmental/farming, politics/economics, and healthy lifestyle. The study found differences in how scholars and consumers perceive sustainability concepts and that aggregating data sources revealed similarities not seen from individual sources. The framework was proposed as a transferable method for analyzing fuzzy concepts.
This document discusses guided interactive discovery of e-government services using dynamic taxonomies. It begins by noting that e-government services are critical for citizens but are currently discovered through search rather than exploration. Traditional search techniques are ineffective for discovery. The document then introduces dynamic taxonomies as a model that can guide exploratory browsing through iterative zooming on related concepts. Dynamic taxonomies provide transparent discovery of unexpected relationships through a simple yet powerful interface.
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Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
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Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
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Chapter 5
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Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Enhancing the Navigability of Social Tagging Systems with Tag Taxonomies
1. Enhancing the Navigability of Social Tagging Systems
with Tag Taxonomies
Christoph Trattner & Christian K¨rner & Denis Helic
o
KMI, TU Graz
September 8, 2011
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 1 / 26
2. Introduction
“Tagging gained tremendously in popularity over the past few years”
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 2 / 26
3. Introduction
Figure: Tags on Flickr
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 3 / 26
4. Introduction
Figure: Tags on Amazon
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 4 / 26
5. Introduction
Figure: Tags on LastFM
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 5 / 26
6. Introduction
What we also like about tags, apart form the fact that they represent
a cheap and light-weight alternative to common key-word based
semantic enrichment, is the fact that they allow us to invent tools to
explore or navigate an information system in a light-weight and
concept driven manner.
A popular example of such a tool are tag taxonomies!
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 6 / 26
7. Introduction
Q: What is a tag taxonomy?
A: A tool that allows us to navigate information items in an
information system in a concept driven and hierarchical manner.
Figure: Tag Taxonomy
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 7 / 26
8. Introduction
Popular examples of tag taxonomy induction algorithms are:
The graph based approach of Heymann (Heymann et al. 2009)
Affinity Propagation (Lerman et al. 2010)
Hierarchical K-Means (Dhillon et al. 2001)
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 8 / 26
9. Why usefulness of tag taxonomies for navigation is limited?
What we also observed in recent research regarding tagging is the fact
that tag based navigation has also it’s limitations (Helic et al. 2010).
The problem with tagging is basically the fact that people do not
apply tags to all resources of an information system system in a
uniform manner.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 9 / 26
10. Why usefulness of tag taxonomies for navigation is limited?
Actually, it was observed (H. Halpin et al. 2007) that the tag distribution
of almost all tagging systems follows a power-law function, i.e. there are
many tags that refer to a large number of resources.
(a) Austria-Forum (b) BibSonomy (c) CiteULike
Figure: Tag distributions.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 10 / 26
11. Why usefulness of tag taxonomies for navigation is limited?
Hence, to navigate from one resource to another resource in an
information system with the help of a tag taxonomy the user would have to
click many many times in the worst case to reach a desired target resource.
Figure: Result list of the tag “blog” in the bookmarking system Delicious.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 11 / 26
12. Why usefulness of tag taxonomies for navigation is limited?
Now, to support the user in the process to also navigate to the
resources of a tagging system in an efficient manner, we invented the
approach of the so-called tag-resource taxonomies.
Car
Car
Tire Motor
Tire Motor
Mercedes VOLVO VW BMW
VW BMW VW BMW
(a) Tag Taxonomy (b) Tag-Resource Taxonomy
Figure: Tag Taxonomy vs. Tag-Resource Taxonomy.
The beauty of such tag-resource hierarchies is that the result lists are
limited to a certain branching factor b and the maximum number of clicks
is bounded by log(n), where n are the number of resources.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 12 / 26
13. Why usefulness of tag taxonomies for navigation is limited?
Sample calculations of a tag taxonomy vs. a tag-resource taxonomy for
the max number of clicks for three different tagging datasets with
branching factor b = 10.
Austria-Forum BibSonomy CiteULike
max{click(Ttag )} 184 5,278 20,799
max{click(Tres )} 6.1 7.7 8.5
Table: Tag Taxonomy vs. Tag-Resource Taxonomy.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 13 / 26
14. Why usefulness of tag taxonomies for navigation is limited?
Sample calculations of a tag taxonomy vs. a tag-resource taxonomy for
the mean number of clicks for three different tagging datasets with
branching factors ranging from b = 2 − 10.
b Austria-Forum BibSonomy CiteULike
mean{click(Tres )} 2 14.2 17.8 19.8
mean{click(Ttag )} 2 29.5 22.4 30.7
mean{click(Tres )} 5 6.1 7.6 8.5
mean{click(Ttag )} 5 11.6 9.2 12.3
mean{click(Tres )} 10 4.3 5.3 5.9
mean{click(Ttag )} 10 6.4 5.6 7.3
Table: Tag Taxonomy vs. Tag-Resource Taxonomy.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 14 / 26
15. Creating tag-resource Taxonomies
“How do we create tag-resource hierarchies?”
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 15 / 26
16. Creating tag-resource Taxonomies
Actually, the first step to create a tag-resource hierarchy is to create a
resource hierarchy out of a tagging dataset.
1. Computer Degree centrality for each resource of the tagging
dataset and take the most general resource as our root
2. Compute cosine-similarity for all resources that are related to the
root node
3. Re-rank nodes according to their cosine*centrality values
4. Attach max. b resources as childs to the root.
5. Set next child as root and go to step 2.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 16 / 26
17. Creating tag-resource Taxonomies
To generate the actual tag-resource taxonomy we invented a hierarchical
labeling algorithm. Basically the algorithm works as follows:
1. Traverse the resource taxonomy in left-order and calculate a
co-occurance vector for the currently processed resource.
2. Remove all tags from the co-occ. vector that are not in the tag set
of the currently processed resource.
3. Try to apply most general tag of the co-ooc. vector. If the
candidate tag has already been applied to one of the parent resources
of the currently processed resource, take the next candidate tag from
the co-occ. vector.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 17 / 26
18. Evaluating Tag-Resource Taxonomies
In order to evaluate our approach, we conducted basically 3 different
experiments
As dataset for our analysis we used a tagging dataset from a large
Wiki based information system called the Austria-Forum.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 18 / 26
19. Evaluating Tag-Resource Taxonomies
Since our tag-taxonomy induction algorithm is not to 100% free of
collisions, we conducted a simple experiment were we measured the
number of collisions that occur during the labeling process.
Example of a collision: car > bmw > bmw
For that purpose we generated three different tag-resource
taxonomies with different branching factors ranging from b = 2 − 10
and investigated the collision rate.
Name b n CR (%)
Res2 2 19,430 0.1%
Res5 5 19,430 0.2%
Res10 10 19,430 0.2%
Table: Collision Rates (CR) for different resource taxonomies with different
branching factor b.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 19 / 26
20. Evaluating Tag-Resource Taxonomies
In the second experiment we measured the semantic structure of the
tag-resource taxonomy compared to popular tag taxonomy induction
algorithms such as Heymann, K-Means, Affinity Propagation and
Co-Occurance
As measure for this experiment we used Taxonomic Recall/Prec. and
Overlap.
As Ground truth we used the Germanet ontholoy
For the experiment we again generated three different tag-resource
taxonomies with different branching factors b.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 20 / 26
21. Evaluating Tag-Resource Taxonomies
0.4
Taxonomic F−Measure
0.35 Taxonomic Overlap
0.3
Count (1 = 100%)
0.25
0.2
0.15
0.1
0.05
0
Res2 Res5 Res10 Deg/Cooc Aff. Prop K−Means Heymann
Figure: Results of the semantic evaluation of the three generated tag-resource
taxonomies Res2, Res5 and Res10.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 21 / 26
22. Evaluating Tag-Resource Taxonomies
In the third and last experiment a user study was conducted to
evaluate weather our approach is also useful for humans and could be
used in a practical setting
To compare our approach against a golden standard we used for the
experiment so far best known tag taxonomy induction algorithm
(Deg/Cooc)
To measure the performance of our approach, we invited 9 test users
to judge 200 tag trails extracted from both hierarchies
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 22 / 26
23. Evaluating Tag-Resource Taxonomies
To ensure that the user would not know which trail she is actually
judging, we mixed the trails up uniform at random
To actually evaluate the trails, we asked our test users to start from
the most left concept and to move on to the most right concept in
the trail
The evaluation schema given to the user was the following:
Classification Description
Correct Correct hierarchy relation
Related Correct relation, but not hierarchical
or reverse hierarchical
Equivalent Synonym
Not Related The relations do not have anything
to do with each other
Unknown The evaluator does not recognize
the meaning of the tag(s)
Table: Classification Labels for the User Evaluation.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 23 / 26
24. Evaluating Tag-Resource Taxonomies
The user study showed a high performance of our approach compared to a
Deg/Cooc tag taxonomy.
Name b Correct (%) Related (%) Equivalent (%) Not Related (%) Unknown(%)
Deg/Cooc10 10 33.2 27.3 13 21.9 5.1
Res10 10 27.3 36.2 12.3 19.8 4.2
Table: Results of the empirical analysis of the tag-resource taxonomy with
branching factor b = 10 compared to a Deg/Cooc tag taxonomy with branching
factor b = 10.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 24 / 26
25. Summary
We showed that tag taxonomies are in general not very well suited for
finding resources in an efficient number of clicks.
To tackle that issue we introduced a novel approach of the so-called
tag-resource hierarchies.
We illustrated in theory that with the approach of a tag-resource
taxonomy it is possible to navigate to resources efficiently.
Additionally to these findings, we introduced an algorithm to generate
such hierarchies and presented in a number of experiments that
proofed that tag-resource taxonomies perform on a semantic level
nearly as good or even better than popular tag taxonomy approaches.
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 25 / 26
26. End of presentation
Thank you very much for your attention!
Christoph Trattner (ctrattner@iicm.edu)
Christoph Trattner & Christian K¨rner & Denis Helic (KMI, Navigability of Social Tagging Systems with Tag Taxonomies
o Enhancing the TU Graz) September 8, 2011 26 / 26