This is a presentation delivered at SharePoint Saturday DC on 15 May 2010. It is a newer version of a presentation given at Interop in 2009, but with a focus on adoption needs and SharePoint 2010.
A presentation on tagging and folksonomy presented to the Association of Alternative Newsweeklies. One thing that came out of discussions and presentation preparation was a need to better monitor and make use of the tagging people are placing in other services.
Keynote presentation delivered at the WWW Conference 2007 in Banff, in the Tagging Workshop. Includes much new material that has not been publicly presented.
Presentation of the paper titled "Leveraging Semantic Parsing for Relation Linking over Knowledge Bases" at the ISWC 2020 - Research Track.
@inproceedings{mihindu-sling-2020,
title = "Leveraging Semantic Parsing for Relation Linking over Knowledge Bases",
author = "Mihindukulasooriya, Nandana and Rossiello, Gaetano and Kapanipathi, Pavan and Abdelaziz, Ibrahim and Ravishankar, Srinivas and Yu, Mo and Gliozzo, Alfio and Roukos, Salim and Gray, Alexander",
booktitle="The Semantic Web -- ISWC 2020",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="402--419",
url = "https://link.springer.com/chapter/10.1007/978-3-030-62419-4_23",
doi = "10.1007/978-3-030-62419-4_23"
}
A presentation on tagging and folksonomy presented to the Association of Alternative Newsweeklies. One thing that came out of discussions and presentation preparation was a need to better monitor and make use of the tagging people are placing in other services.
Keynote presentation delivered at the WWW Conference 2007 in Banff, in the Tagging Workshop. Includes much new material that has not been publicly presented.
Presentation of the paper titled "Leveraging Semantic Parsing for Relation Linking over Knowledge Bases" at the ISWC 2020 - Research Track.
@inproceedings{mihindu-sling-2020,
title = "Leveraging Semantic Parsing for Relation Linking over Knowledge Bases",
author = "Mihindukulasooriya, Nandana and Rossiello, Gaetano and Kapanipathi, Pavan and Abdelaziz, Ibrahim and Ravishankar, Srinivas and Yu, Mo and Gliozzo, Alfio and Roukos, Salim and Gray, Alexander",
booktitle="The Semantic Web -- ISWC 2020",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="402--419",
url = "https://link.springer.com/chapter/10.1007/978-3-030-62419-4_23",
doi = "10.1007/978-3-030-62419-4_23"
}
Presentation given at the National Federation of Advanced Information Services (NFAIS) conference: "Improving the User Search Experience" October 2010, in Philadelphia, PA
Social Web and Semantic Web: towards synergy between folksonomies and ontologiesFreddy Limpens
Web social et web sémantique : tagging et ontologies.
L’essor du tagging et des folksonomies pour l’organisation des ressources partagées au sein du Web social et collaboratif constitue une opportunité pour l’acquisition des connaissances par ceux-là même qui les manipulent. Cependant l’absence de liens sémantiques entre les tags, ou la variabilité d’écriture de certains tags appauvrissent les potentiels de navigation et de recherche d’information. Pour remédier à ces limitations, nous proposons d’exploiter l’interaction entre les utilisateurs et les systèmes à base de folksonomies pour valider ou invalider des traitements automatiques effectués sur les tags. Ces opérations se basent sur notre modèle pour l’assistance à la structuration des folksonomies qui autorise des vues conflictuelles portant sur les liens entre les tags, tout en permettant aux concepteurs des systèmes d’exploiter la diversité de ces descriptions sémantiques afin d’offrir des fonctionnalités de navigation enrichies.
Orbyfy’s Fabric+ is the data fabric for the Metaverse, providing a unified data network, simplified data management, built-in data
integration, self-service, centralized data store, and federated governance. Integrated data management for the generative AI revolution & more.
Keynote at 2012 Semantic Technology and Business Conference
Scale, Structure, and Semantics
Daniel Tunkelang, LinkedIn
Science fiction has a mixed track record when it comes to anticipating technological innovations. While Jules Verne fared well with with his predictions of submarine and space technology, artificial intelligence hasn't produced anything like Arthur C. Clarke's HAL 9000.
Instead, we've managed to elicit intelligence from machines through unexpected means. Search engines have achieved remarkable success in organizing the world's information by crawling the web, indexing documents, and exploiting link structure to establish authoritativeness. At LinkedIn, we apply large-scale analytics to terabytes of semistructured data to deliver products and insights that serve our 150M+ members. Semantics emerge when we apply the right analytical techniques to a sufficient quality and quantity of data.
In this talk, I will describe how LinkedIn's huge and rich graph of relationship data that powers the products our users love. I believe that the lessons we have learned apply broadly to other semantic applications. While quantity and quality of data are the key challenges to delivering a semantically rich experience, the key is to create the right ecosystem that incents people to give you good data, which then forms the basis for great data products.
A Small Overview of Big Data Products, Analytics, and Infrastructure at LinkedInAmy W. Tang
This talk was given by Bhaskar Ghosh (Senior Director of Engineering, LinkedIn Data Infrastructure), at the Yale Oct 2012 Symposium on Big Data, in honor of Martin Schultz.
Content, Connections, and Context
Daniel Tunkelang, LinkedIn
Keynote at Workshop on Recommender Systems and the Social Web
At 6th ACM International Conference on Recommender Systems (RecSys 2012)
Recommender systems for the social web combine three kinds of signals to relate the subject and object of recommendations: content, connections, and context.
Content comes first - we need to understand what we are recommending and to whom we are recommending it in order to decide whether the recommendation is relevant. Connections supply a social dimension, both as inputs to improve relevance and as social proof to explain the recommendations. Finally, context determines where and when a recommendation is appropriate.
I'll talk about how we use these three kinds of signals in LinkedIn's recommender systems, as well as the challenges we see in delivering social recommendations and measuring their relevance.
Presentation given at the National Federation of Advanced Information Services (NFAIS) conference: "Improving the User Search Experience" October 2010, in Philadelphia, PA
Social Web and Semantic Web: towards synergy between folksonomies and ontologiesFreddy Limpens
Web social et web sémantique : tagging et ontologies.
L’essor du tagging et des folksonomies pour l’organisation des ressources partagées au sein du Web social et collaboratif constitue une opportunité pour l’acquisition des connaissances par ceux-là même qui les manipulent. Cependant l’absence de liens sémantiques entre les tags, ou la variabilité d’écriture de certains tags appauvrissent les potentiels de navigation et de recherche d’information. Pour remédier à ces limitations, nous proposons d’exploiter l’interaction entre les utilisateurs et les systèmes à base de folksonomies pour valider ou invalider des traitements automatiques effectués sur les tags. Ces opérations se basent sur notre modèle pour l’assistance à la structuration des folksonomies qui autorise des vues conflictuelles portant sur les liens entre les tags, tout en permettant aux concepteurs des systèmes d’exploiter la diversité de ces descriptions sémantiques afin d’offrir des fonctionnalités de navigation enrichies.
Orbyfy’s Fabric+ is the data fabric for the Metaverse, providing a unified data network, simplified data management, built-in data
integration, self-service, centralized data store, and federated governance. Integrated data management for the generative AI revolution & more.
Keynote at 2012 Semantic Technology and Business Conference
Scale, Structure, and Semantics
Daniel Tunkelang, LinkedIn
Science fiction has a mixed track record when it comes to anticipating technological innovations. While Jules Verne fared well with with his predictions of submarine and space technology, artificial intelligence hasn't produced anything like Arthur C. Clarke's HAL 9000.
Instead, we've managed to elicit intelligence from machines through unexpected means. Search engines have achieved remarkable success in organizing the world's information by crawling the web, indexing documents, and exploiting link structure to establish authoritativeness. At LinkedIn, we apply large-scale analytics to terabytes of semistructured data to deliver products and insights that serve our 150M+ members. Semantics emerge when we apply the right analytical techniques to a sufficient quality and quantity of data.
In this talk, I will describe how LinkedIn's huge and rich graph of relationship data that powers the products our users love. I believe that the lessons we have learned apply broadly to other semantic applications. While quantity and quality of data are the key challenges to delivering a semantically rich experience, the key is to create the right ecosystem that incents people to give you good data, which then forms the basis for great data products.
A Small Overview of Big Data Products, Analytics, and Infrastructure at LinkedInAmy W. Tang
This talk was given by Bhaskar Ghosh (Senior Director of Engineering, LinkedIn Data Infrastructure), at the Yale Oct 2012 Symposium on Big Data, in honor of Martin Schultz.
Content, Connections, and Context
Daniel Tunkelang, LinkedIn
Keynote at Workshop on Recommender Systems and the Social Web
At 6th ACM International Conference on Recommender Systems (RecSys 2012)
Recommender systems for the social web combine three kinds of signals to relate the subject and object of recommendations: content, connections, and context.
Content comes first - we need to understand what we are recommending and to whom we are recommending it in order to decide whether the recommendation is relevant. Connections supply a social dimension, both as inputs to improve relevance and as social proof to explain the recommendations. Finally, context determines where and when a recommendation is appropriate.
I'll talk about how we use these three kinds of signals in LinkedIn's recommender systems, as well as the challenges we see in delivering social recommendations and measuring their relevance.
Big Data and Data Standardization at LinkedInAlexis Baird
From a talk I gave to a group of Connecticut College students in November of 2012. This looks at some of the challenges of dealing with huge amounts of member-inputted data as well as techniques used to solve these challenges and product applications of that member-inputted data.
This talk introduces Linked Data and Semantic Web by using two examples - population sciences grid and semantAqua - a semantically enabled environmental monitoring. It shows a few tools and the semantic methodology and opens discussion for LOD and team science
Similar to Understanding Tagging and Folksonomy - SharePoint Saturday DC (20)
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51. Folksonomy: Definition
❖ Folksonomy is the result of personal free
tagging of pages and objects for one's own
retrieval
❖ The tagging is usually done in a social
environment (shared and open to others)
❖ The act of tagging is done by the person
consuming the information
InfoCloud Solutions, Inc. - 2010
52. Folksonomy:Value
The value in this external tagging is derived
from people using their own vocabulary
and adding explicit meaning, which may
meaning
come from inferred understanding of the
information/object.
People are not so much categorizing, as
providing a means to connect items
(placing hooks) to provide their meaning in
their own understanding.
InfoCloud Solutions, Inc. - 2010
53. Every person is an
expert in their own
vocabulary (tags)
InfoCloud Solutions, Inc. - 2010
54. Every Tag is Sacred
InfoCloud Solutions, Inc. - 2010
58. Scaling and Functionality
D A - Personal Use
B - Serendipity
C C - Social Tagging
# People Tagging
Mature
B D - Complex Social
System
A
Times Object is Tagged
InfoCloud Solutions, Inc. - 2010
59. Phases of Interaction
❖ Saving and tagging
❖ Refinding
❖ Clicking, pivoting, exploring
❖ Searching
❖ One’s own tags
❖ Other’s tags
❖ Group
❖ Everybody
❖ Group Social Interaction
InfoCloud Solutions, Inc. - 2010
60. Personal to Social
Personal Serendipity Social Complex
Mature
Save & Tag X X X X
Refind X X X X
Pivot & Explore X X X
Search X X
Group Interaction - X
InfoCloud Solutions, Inc. - 2010
61. Social Context
Personal - Social -
❖ Capture ❖ Share
❖ Hook/Copy ❖ Point
❖ Annotate ❖ Collaborate
❖ Refind ❖ Filter
❖ Privacy ❖ Trusted Groups
InfoCloud Solutions, Inc. - 2010
62. Folksonomy
vs.
Taxonomy
InfoCloud Solutions, Inc. - 2010
63. The Value of Tagging
for Business
InfoCloud Solutions, Inc. - 2010
64. Tagging: Definition
❖ Simple data/metadata externally applied to
an object
❖ Used for sorting
❖ A hook for aggregating
❖ Provides identifier and/or description
❖ Personal markers
InfoCloud Solutions, Inc. - 2010
65. Taxonomy vs. Folksonomy
Business Employee
Taxonomy Object Folksonomy
InfoCloud Solutions, Inc. - 2010
67. 70% of Folksonomy tag
terms not in Taxonomy
J. Trant regarding Steve.museum
InfoCloud Solutions, Inc. - 2010
68. Terms Around an Object
Taxonomy Folksonomy
- Ball (89)
- Ball - Circle (63)
- Sphere - Blue (23)
- Blue - Orb (11)
- #A6437 - Sphere (6)
- Gradient (3)
- ToBuy (3)
- Darkblue (2)
- Round (2)
- … 26 more
InfoCloud Solutions, Inc. - 2010
69. Part #A6473 Terms
Distribution of Terms
Validate A6473
taxonomy 90.0
or add to
taxonomy Value in
Interest
Synonyms &
67.5 & watch
Unique views
45.0
22.5
0
InfoCloud Solutions, Inc. - 2010
70. Part #A6473 Terms
Distribution of Terms
Azure
Validate A6473
taxonomy 90.0
or add to
taxonomy Interest
67.5 & watch
45.0
22.5
0
InfoCloud Solutions, Inc. - 2010
71. Business Tensions
Naming control People’s vocabulary
Sample groups Every perspective
In-house Outside service
$$$ w/ value $ w/ unknown value
Consistent Emergent
InfoCloud Solutions, Inc. - 2010