Social tagging represents an innovative and powerful mechanism introduced by social Web: it shifts the task of classifying resources from a reduced set of knowledge engineers to the wide set of Web users. Tags generate folksonomies; in the current popular social tagging systems (such as delicious or Bibsonomy), they are difficult to manage, modify, and visualize in dynamic and personalized ways.
The aim of this paper is to describe Folkview, an innovative way to conceive a folksonomy in terms of a multi-agent system. Folkview is able to support specific modular tools for personalizing customized and dynamic visualization features allowing users to simply update, manage and modify a folksonomy.
Many latent (factorized) models have been
proposed for recommendation tasks like collaborative ïŹltering and for ranking tasks like
document or image retrieval and annotation.
Common to all those methods is that during inference the items are scored independently by their similarity to the query in the
latent embedding space. The structure of the
ranked list (i.e. considering the set of items
returned as a whole) is not taken into account. This can be a problem because the
set of top predictions can be either too diverse (contain results that contradict each
other) or are not diverse enough
Building a vietnamese dialog mechanism for v dlg~tabl systemijnlc
Â
This paper introduces a Vietnamese automatic dialog mechanism which allows the V-DLG~TABL system to automatically communicate with clients. This dialog mechanism is based on the Question â Answering engine of V-DLG~TABL system, and composes the following supplementary mechanisms: 1) the mechanism of choosing the suggested question; 2) the mechanism of managing the conversations and suggesting information; 3) the mechanism of resolving the questions having anaphora; 4) the scenarios of dialog (in this stage, there is only one simple scenario defined for the system).
Robust Coreference Resolution and Entity Linking on Dialogues: Character Iden...Jinho Choi
Â
This paper presents a novel approach to character identification, that is an entity linking task that maps mentions to characters in dialogues from TV show transcripts. We first augment and correct several cases of annotation errors in an existing corpus so the corpus is clearer and cleaner for statistical learning. We also introduce the agglomerative convolutional neural network that takes groups of features and learns mention and mention-pair embeddings for coreference resolution. We then propose another neural model that employs the embeddings learned and creates cluster embeddings for entity linking. Our coreference resolution model shows comparable results to other state-of-the-art systems. Our entity linking model significantly outperforms the previous work, showing the F1 score of 86.76% and the accuracy of 95.30% for character identification.
Many latent (factorized) models have been
proposed for recommendation tasks like collaborative ïŹltering and for ranking tasks like
document or image retrieval and annotation.
Common to all those methods is that during inference the items are scored independently by their similarity to the query in the
latent embedding space. The structure of the
ranked list (i.e. considering the set of items
returned as a whole) is not taken into account. This can be a problem because the
set of top predictions can be either too diverse (contain results that contradict each
other) or are not diverse enough
Building a vietnamese dialog mechanism for v dlg~tabl systemijnlc
Â
This paper introduces a Vietnamese automatic dialog mechanism which allows the V-DLG~TABL system to automatically communicate with clients. This dialog mechanism is based on the Question â Answering engine of V-DLG~TABL system, and composes the following supplementary mechanisms: 1) the mechanism of choosing the suggested question; 2) the mechanism of managing the conversations and suggesting information; 3) the mechanism of resolving the questions having anaphora; 4) the scenarios of dialog (in this stage, there is only one simple scenario defined for the system).
Robust Coreference Resolution and Entity Linking on Dialogues: Character Iden...Jinho Choi
Â
This paper presents a novel approach to character identification, that is an entity linking task that maps mentions to characters in dialogues from TV show transcripts. We first augment and correct several cases of annotation errors in an existing corpus so the corpus is clearer and cleaner for statistical learning. We also introduce the agglomerative convolutional neural network that takes groups of features and learns mention and mention-pair embeddings for coreference resolution. We then propose another neural model that employs the embeddings learned and creates cluster embeddings for entity linking. Our coreference resolution model shows comparable results to other state-of-the-art systems. Our entity linking model significantly outperforms the previous work, showing the F1 score of 86.76% and the accuracy of 95.30% for character identification.
Visualizing and Managing Folksonomies, SASWeb 2011 workshop, at UMAP 2011Antonella Dattolo
Â
Social tagging represents an innovative and powerful mechanism introduced by social Web: it shifts the task of classifying resources from a reduced set of knowledge engineers to the wide set of Web users. Tags generate folksonomies; in the current popular social tagging systems (such as delicious or Bibsonomy), they are difficult to manage, modify, and visualize in dynamic and personalized ways.
The aim of this paper is to describe Folkview, an innovative way to conceive a folksonomy in terms of a multi-agent system. Folkview is able to support specific modular tools for personalizing customized and dynamic visualization features allowing users to simply update, manage and modify a folksonomy.
Succession âLosersâ: What Happens to Executives Passed Over for the CEO Job?
By David F. Larcker, Stephen A. Miles, and Brian Tayan
Stanford Closer Look Series
Overview:
Shareholders pay considerable attention to the choice of executive selected as the new CEO whenever a change in leadership takes place. However, without an inside look at the leading candidates to assume the CEO role, it is difficult for shareholders to tell whether the board has made the correct choice. In this Closer Look, we examine CEO succession events among the largest 100 companies over a ten-year period to determine what happens to the executives who were not selected (i.e., the âsuccession losersâ) and how they perform relative to those who were selected (the âsuccession winnersâ).
We ask:
âą Are the executives selected for the CEO role really better than those passed over?
âą What are the implications for understanding the labor market for executive talent?
âą Are differences in performance due to operating conditions or quality of available talent?
âą Are boards better at identifying CEO talent than other research generally suggests?
PROPERTIES OF RELATIONSHIPS AMONG OBJECTS IN OBJECT-ORIENTED SOFTWARE DESIGNijpla
Â
One of the modern paradigms to develop a system is object oriented analysis and design. In this paradigm,
there are several objects and each object plays some specific roles. After identifying objects, the various
relationships among objects must be identified. This paper makes a literature review over relationships
among objects. Mainly, the relationships are three basic types, including generalization/specialization,
aggregation and association.This paper presents five taxonomies for properties of the relationships. The first
taxonomy is based on temporal view. The second taxonomy is based on structure and the third one relies on
behavioral. The fourth taxonomy is specified on mathematical view and fifth one related to the interface.
Additionally, the properties of the relationships are evaluated in a case study and several recommendations
are proposed.
Finding prominent features in communities in social networks using ontologycsandit
Â
Community detection is one of the major tasks in social networks. The success of any community
depends upon the features that were selected to form the community. So it is important to have
the knowledge of the main features that may affect the community. In this work we have
proposed a method to find prominent features based on which community can be formed.
Ontology has been used for the said purpose.
An integrated approach to discover tag semanticsDavide Eynard
Â
Talk presentation at SAC 2011. From the paper abstract: "Tag-based systems have become very common for online classification thanks to their intrinsic advantages such as self-organization and rapid evolution. However, they are still affected by some issues that limit their utility, mainly due to the inherent ambiguity in the semantics of tags. Synonyms, homonyms, and polysemous words, while not harmful for the casual user, strongly affect the quality of search results and the performances of tag-based recommendation
systems. In this paper we rely on the concept of tag relatedness in order to study small groups of similar tags and detect relationships between them. This approach is grounded on a model that builds upon an edge-colored multigraph of users, tags, and resources. To put our thoughts in practice, we present a modular and extensible framework of analysis for discovering synonyms, homonyms and hierarchical relationships amongst sets of tags. Some initial results of its application to the delicious database are presented, showing that such an approach could be useful to solve some of the well known problems of folksonomies.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Â
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, TwitterâŠetc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGIJwest
Â
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, TwitterâŠetc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
Visualizing and Managing Folksonomies, SASWeb 2011 workshop, at UMAP 2011Antonella Dattolo
Â
Social tagging represents an innovative and powerful mechanism introduced by social Web: it shifts the task of classifying resources from a reduced set of knowledge engineers to the wide set of Web users. Tags generate folksonomies; in the current popular social tagging systems (such as delicious or Bibsonomy), they are difficult to manage, modify, and visualize in dynamic and personalized ways.
The aim of this paper is to describe Folkview, an innovative way to conceive a folksonomy in terms of a multi-agent system. Folkview is able to support specific modular tools for personalizing customized and dynamic visualization features allowing users to simply update, manage and modify a folksonomy.
Succession âLosersâ: What Happens to Executives Passed Over for the CEO Job?
By David F. Larcker, Stephen A. Miles, and Brian Tayan
Stanford Closer Look Series
Overview:
Shareholders pay considerable attention to the choice of executive selected as the new CEO whenever a change in leadership takes place. However, without an inside look at the leading candidates to assume the CEO role, it is difficult for shareholders to tell whether the board has made the correct choice. In this Closer Look, we examine CEO succession events among the largest 100 companies over a ten-year period to determine what happens to the executives who were not selected (i.e., the âsuccession losersâ) and how they perform relative to those who were selected (the âsuccession winnersâ).
We ask:
âą Are the executives selected for the CEO role really better than those passed over?
âą What are the implications for understanding the labor market for executive talent?
âą Are differences in performance due to operating conditions or quality of available talent?
âą Are boards better at identifying CEO talent than other research generally suggests?
PROPERTIES OF RELATIONSHIPS AMONG OBJECTS IN OBJECT-ORIENTED SOFTWARE DESIGNijpla
Â
One of the modern paradigms to develop a system is object oriented analysis and design. In this paradigm,
there are several objects and each object plays some specific roles. After identifying objects, the various
relationships among objects must be identified. This paper makes a literature review over relationships
among objects. Mainly, the relationships are three basic types, including generalization/specialization,
aggregation and association.This paper presents five taxonomies for properties of the relationships. The first
taxonomy is based on temporal view. The second taxonomy is based on structure and the third one relies on
behavioral. The fourth taxonomy is specified on mathematical view and fifth one related to the interface.
Additionally, the properties of the relationships are evaluated in a case study and several recommendations
are proposed.
Finding prominent features in communities in social networks using ontologycsandit
Â
Community detection is one of the major tasks in social networks. The success of any community
depends upon the features that were selected to form the community. So it is important to have
the knowledge of the main features that may affect the community. In this work we have
proposed a method to find prominent features based on which community can be formed.
Ontology has been used for the said purpose.
An integrated approach to discover tag semanticsDavide Eynard
Â
Talk presentation at SAC 2011. From the paper abstract: "Tag-based systems have become very common for online classification thanks to their intrinsic advantages such as self-organization and rapid evolution. However, they are still affected by some issues that limit their utility, mainly due to the inherent ambiguity in the semantics of tags. Synonyms, homonyms, and polysemous words, while not harmful for the casual user, strongly affect the quality of search results and the performances of tag-based recommendation
systems. In this paper we rely on the concept of tag relatedness in order to study small groups of similar tags and detect relationships between them. This approach is grounded on a model that builds upon an edge-colored multigraph of users, tags, and resources. To put our thoughts in practice, we present a modular and extensible framework of analysis for discovering synonyms, homonyms and hierarchical relationships amongst sets of tags. Some initial results of its application to the delicious database are presented, showing that such an approach could be useful to solve some of the well known problems of folksonomies.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Â
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, TwitterâŠetc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGIJwest
Â
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, TwitterâŠetc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Â
Social Networks has become one of the most popular platforms to allow users to communicate, and share
their interests without being at the same geographical location. With the great and rapid growth of Social
Media sites such as Facebook, LinkedIn, Twitter...etc. causes huge amount of user-generated content.
Thus, the improvement in the information quality and integrity becomes a great challenge to all social
media sites, which allows users to get the desired content or be linked to the best link relation using
improved search / link technique. So introducing semantics to social networks will widen up the
representation of the social networks.
Entity Resolution is a problem that occurs in many information integration processes and applications. The primary goal of entity resolution is to resolve data references to the corresponding same real world entity. The ambiguity in references comes in various net- works such as social network, biological network, citation graphs and many others. Ambiguity in references not only leads to data redundancy but also inaccuracies in knowledge representation, extraction and query processing. Entity resolution is the solution to this problem. There have been many approaches such as pair-wise similarity over attributes of references, a parallel approach for morphing the graph data on to cluster of nodes (P-Swoosh) [2] and relational clustering that makes use of relational information in addition to the attribute similarity. In this article, we make use of relational culstering to resolve author name ambiguities in a subset of a real-world dataset: US patent network consisting of more than 650,000 author references.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Â
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
Â
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Â
Clients donât know what they donât know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clientsâ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Â
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Â
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
Â
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
Â
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
âą Communication Mining Overview
âą Why is it important?
âą How can it help todayâs business and the benefits
âą Phases in Communication Mining
âą Demo on Platform overview
âą Q/A
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
Â
In this second installment of our Essentials of Automations webinar series, weâll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
Weâll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether youâre tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Letâs turn complexity into clarity and make your workspaces work wonders!
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Â
Monitoring and observability arenât traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current companyâs observability stack.
While the dev and ops silo continues to crumbleâŠ.many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bankâs data infrastructure but also positioned them as pioneers in the banking sectorâs adoption of graph technology.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Â
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
Â
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJSâŠ
GridMate - End to end testing is a critical piece to ensure quality and avoid...
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Visualizing and Managing Folksonomies, SASWeb 2011 workshop, at UMAP 2011
1. Visualizing and Managing Folksonomies
Antonina Dattolo { Emanuela Pitassi
University of Udine, Via delle Scienze 206, I-33100 Udine, Italy
June 25, 2011
2. Outline
Background
Open Issues
Our Proposal
Folkview: the formal model
Folkview views and authoring
Related works
Conclusion and future work
3. Background
I The advent of Web 2.0 shifts the task of classifying resources
from a reduce set of experts, to the wide set of Web users.
I Free classi
4. cations are made by user thanks to tags, which
generate a folksonomy (Vander Wal, 2004; Mathes, 2004)
I Several social tagging systems such as Bibsonomy (Hotho et
al, 2006) or delicious allow users to classify resources and
generate folksonomies, but they are dicult to manage,
modify and visualize in dynamic and personalized ways.
6. ned and personalized sub-portion of an
entire hyperspace has already been considered in dierent
settings.
I Adptive bookmarking systems, e.g.:
I PowerBookmarks (Li et al, 1999)
I Siteseer (Rucker et al, 1997)
I Web Tagger (Keller et al, 1997)
I Start pages on Web browser, e.g.:
I Netvibes (http://www.netvibes.com.it)
I My Yahoo (http://my.yahoo.com)
I iGoogle (http://www.google.it/ig)
7. Open Issues
I Social Tagging Systems suer from dierent issues:
I the lack and the exigence of general methotodologies for
extracting semantic information (Dattolo et al., 2010)
I the lack and the exigence of personalized and dynamic
workspaces in wich users
I Can visualize personalized views of the folksonomy
I Can apply personal changes
I A crucial task for developers of current Web application is
how to model and create speci
8. c tools for providing
personalized views to the users.
9. Open Issues
I A folksonomy is usually represented by a tripartite graph or
network Various researches have dealt with the issue related
to the complexity of the nature of the graph itself, projecting
a folksonomy on simpli
10. ed structures (Lambiotte, 2006;
Dattolo, 2011).
I Generally a folksonomy is represented by a tag-cloud, but this
kind of visualization is not sucient as the sole means of
navigation (Sinclair, 2007).
I Possible multiple visualizations of a folksonomy allow users
to:
I have a more eective comprehension of the semantic relations
of a folksonomy
I have a more useful navigation through the involved elements
I manipulate the existing relations among tags and resources
according to the user needs.
11. Our Proposal
I The main aim of this work is to propose and describe a novel,
distributed, modular system called Folkview, whereby a
folksonomy is conceived dynamically through the use of
multiple agents.
I These agents will be capable of
I managing the structural and semantic properties;
I cooperating for obtaining common objectives;
I oering personalized and dynamic views.
I Steps:
I De
12. nition of the Formal Model which exploits multi-agents
system
I Personalized views Folkview Prototype
13. The formal model
I Traditionally, given the sets of users U , tags T and and
resources R , a folksonomy is de
14. ned as the set of tag
assignments
(u ; r ; t ) P U Âą T Âą R
i j k
where i = 1; : : : ; jU j; j = 1; : : : ; jT j; k = 1; : : : ; jR j, each of
them indicating that user u has tagged the resource r with
i j
t .
k
I User pro
15. les, functions, metrics or semantic relations among
users, tags, resources and tas are not intrinsic properties of
the folksonomy.
17. ne a F , we identify three classes of sets:
I T 2 T is the set of tags used by u on r ;
ui ;rj i j
I R 2 R is the set of resources tagged by u with t ;
ui ;tk i k
I U is the set of users that tagged r with t .
tk ;rj j k
I Each set represents a structural component of the folksonomy,
and we call it structural ; the tags are grouped associating to
them a semantic label for identifying their meaning in that
dimension.
18. Our representation of a folksonomy
Figure: 6 structural dimensions (left) and the corresponding folksonomy
(right)
The
19. gure above represents three linear paths that contain the
resources tagged by user u , using respectively t , t and t .
1 1 2 3
The labels associated with them are respectively u ; t , u ; t and
1 1 1 2
u ; t , and represent a sub-portion of her personomy.
1 3
22. nition
A structural dimension is a labeled path
Dui ;rj = (V ; E ; )
where
I V =T is the set of vertices,
ui ;rj
I E is the set of edges,
I (e ) = (u ; r ) Ve P E is an edge labeling, and
degree (t ) = 0; 1; 2 Vt P T
i j
k . k ui ;rj
In particular, degree (t ) = 0; 1; 2 only if jT j = 1.
k ui ;rj
De
23. nition
A static folksonomy F is a labeled multigraph given by the union
of three families of structural dimensions.
F =
[ Dui ;rj â
[ Dui ;tk â
[ Dtk ;rj
i ;k i ;j j ;k
25. nition of the dimension h ui ; r j based on
the structural dimension D . ui ;rj
De
26. nition
A dimension h = (Ts ; En; Re ; Ac ) is an agent where
I Ts = D ; ui ;rj
I En = fu ; r ; t ; : : : ; t g;
i j 1 n
I Re = fYg, initially;
I Ac = fadd -tag ; delete -tag ; modify -tag ; : : : g
Analogously, we can de
27. ne new classes of agent dimensions, not
only for structural dimensions. New dimensions can be created
directly from the user, or computed by the system applying speci
28. c
metrics, or generated applying ontological models:
I each dimension can contain other dimensions; dimension
associates a semantics to the set of grouped entities.
31. nition
A folksonomy F is a multi-agent system formally described as a
labeled multigraph of agent entities, organized in semantic
contexts, called dimensions.
p=
[h
n
i
i =1
All in a folksonomy is a computational agent, equipped with a set
of local variables, that de
32. ne its internal state, and a modular and
extensible set of procedural skills.
33. Folkview views and authoring
In the labeled multigraph contained in the de
34. nition of p we can
recognize the zz-structures (Nelson, 2004): they are
non-hierarchical, minimalist, scalable structure for storing,
linking and manipulating dierent kind of data.
From these structures, we inherit many strengths, such as their
intrinsic capability to preserve contextual interconnections
among dierent information, thanks to their particular properties.
36. gure the presence of a black triangle symbol, in two
positions, correspond to selected/not selected : these triangles are
associated to scripts related to the session agent of the current
visualization, and represent the mean to interact with the
cell-agent.
When selected, the session agent asks to the chosen resource (r , 9
in our example) the set of actions Ac that can be activated on it.
Then r sends a multicast message to all the dimensions in
9
which it is included, and a run-time created contextual menu,
organized in three meta-categories (views, metrics and semantics)
is shown.
I The
37. rst category menu is concerning the dierent kinds of
possible views
I The other two categories of functions oered by the menu, are
related to:
I the computation of an extensible set of metrics ,
I the application of opportune semantic relations and ontologies
in order to generate, for example, speci
39. Related works
I Few works have addressed the problem of interactive
visualizations of folksonomies:
I customized cluster maps for visualizing both the overview
and the detail of semantic relationships intrinsic in the
folksonomy (Montero et al., 2007);
I using information visualization techniques (IF) to discover
implicit relationships between users, tags and bookmarks,
end-users have dierent ways to discover content and
information otherwhise dicult to understand (Klerx and
Duval, 2009)
I the TagGraph project (http://taggraph.com/) is a folksonomy
navigator which visualizes the relationships between Flickr tags.
I Nevertheless these works do not provide neither
personalized views nor eective dynamic changes
according to the user needs or preferences.
40. Conclusion and future work
I We have proposed an innovative way to conceive a
folksonomy in terms of a multi-agent system,
42. ning a
formal model and then showing Folkview.
I We have built a partial, but modular and extensible,
prototype, based on a public dataset taken from delicious,
and that implements the structural aspects of the considered
folksonomy.
I As future work we want to extend the prototype:
I to all the main functionality we discussed, focusing our
attention on a semantic personalization;
I to extract data from a large number of social tagging systems.