Social Semantic Desktop Reference Architecture Evaluation

© NEPOMUK Consortium 2006 –

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Integrated Project n°27705 – Priority 2.4.7 – Semantic knowledge based

systems

Social Semantic DesktopReference Architecture

EvaluationDate

People

Reference Architecture Evaluation

2Social Semantic Desktop© NEPOMUK Consortium 2006 –

2008

• To involve various stakeholders of the Social Semantic Desktop community in the critical assessment of the Social Semantic Desktop Blueprint

• Refine the scope functional boundaries of the Social Semantic Desktop Blueprint

• To involve the stakeholders in the architectural design by bringing their experience and expertise

Evaluation purpose (generic)



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Evaluation process

• Preparation• Stakeholders profile – Questionnaire A (10 min.)• Introduction (5 min.)• Motivation and Overview (2 min.)• The Social Semantic Desktop Blueprint (40 min.)

• Analysis• Scenarios for the Social Semantic Desktop Blueprint (5

min.)• Interactive scenarios revision (20 min.)

• Synthesis• Assessment – Questionnaire B (10 min.)• Final assessment – processing the evaluation data to

formulate the evaluation results



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Please fill-in Questionnaire A …

http://www.surveymonkey.com/s.aspx?sm=Y7dGTHTbWa_2biudS_2fM8924g_3d_3d



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The Social Semantic Desktop

• Extension of the personal desktop …

• … into a collaboration environment

• Goals:• Improve personal information management

• Improve cross-media and cross-application linking

• Improve sharing and exchange across social and organizational relations.



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•Personal Information Management

•Distributed Information Management

•Social Networks and Community Services.

Social Semantic Desktop Layers



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SSD evolution



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SSD evolution (cont.)



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SSD evolution – current status



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The NEPOMUK IP Project

•FP6 Project IST

•17 Partners:• 8 research centres• 4 big industry

players• 3 SMEs

•Open source



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•Goals

• Definition of the Social Semantic Desktop Blueprint

• Standardization of ontologies and APIs

• Development• Prototypes• The reference Social Semantic Desktop

implementation

The NEPOMUK IP Project (cont.)



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Evaluation purpose (technical aspect)



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• Social Semantic Desktop Blueprint• Personal information management -> Personal

knowledge creation and organization

• Data interoperability -> Cross-media and cross-application linking

• (Distributed) Social collaboration -> Sharing, exchange and alignment of the personal knowledge in a distributed manner

• Evaluation = Proof that the SSD Blueprint is able to handle all the possible scenarios arising in the space of these three dimensions

Evaluation purpose (technical aspect)


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systems

The Social Semantic Desktop Blueprint



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Overview

•Social Semantic Desktop – Scenario

•Social Semantic Desktop – Engineering cycle

•Social Semantic Desktop Models

•The Social Semantic Desktop Blueprint



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Scenario



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Engineering cycle



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Engineering cycle (I)



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Usage – Scenarios – Functionalities



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Engineering cycle (II)



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• Knowledge Articulation and Visualization• Semantic data editing and presentation

• Standard Desktop Classification Structures• Standard set of vocabularies and ontologies (e.g.

calendar, task management)• Mapping and Aligning Algorithms

• Alignment of information from similar domains expressed with different schemas

• Wrapping of Legacy Information• Standardized semantic representation of structured

and unstructured data

Technical requirements (I)



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• Metadata Storage and Querying• Central place for storing and querying the information

and the associated metadata• Linking of Data Items and Relational

Metadata• Link of arbitrary information across different media

types, file formats and applications• Social Aspects

• Social relation building and knowledge sharing within social communities

• Open Architecture• Clearly defined and published interfaces• Open for integration with external adopters

Technical requirements (II)



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Engineering cycle (III)



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Models



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• Personal Information Model (PIM)• Vocabulary allowing individual persons to express

their own mental models in a structured way• Different mental models can be integrated based on

matching algorithms or on domain ontologies

• Information Element Model (IEM)• Vocabulary for describing information elements which

are commonly present on the semantic desktop

• Annotation Model (AM)• Vocabulary, commonly required to annotate resources

on the semantic desktop

Models



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NEPOMUK Models – PIMO



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• Requirements• a representation of abstract concepts: Love, Rome, Acme Inc. • a representation of concrete, addressable resources: "w3c

homepage at www.w3.org" • a representation of documents: "the document at

http://www.w3.org/" • multiple names for a thing: "Love", "Liebe“; "W3", "WWW" • same name for two different things: "Apache - helicopter",

"Apache - software". • class-subclass relations: a subclass has all properties of the

superclass + its own • class-instance relations • part-of relations: the city of Rome is part of Italy • related information: Spaghetti is related to Italy • data properties to describe details: Rome has a population of

2.8 mio • document-has-topic: the document

"http://www.w3.org/2001/sw" is about the "Semantic Web" • a representation of time: the document was created in 2005.

The project started on 1.1.2006




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•The NEPOMUK Information Element (NIE)

• Set of ontologies

• Vocabulary for describing information elements commonly present on the semantic desktop

NEPOMUK Models



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•NIE Core – NEPOMUK Information Element Core Ontology

•NFO – NEPOMUK File Ontology•NCO – NEPOMUK Contact Ontology•NMO – NEPOMUK Message Ontology•NCAL – NEPOMUK Calendar Ontology•NEXIF – NEPOMUK EXIF Ontology•NID3 – NEPOMUK ID3 Ontology

NEPOMUK Models – NIE



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NEPOMUK Models – NIE



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NEPOMUK Models – NIE Core



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NEPOMUK Models – NFO



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NEPOMUK Models – NMO



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NEPOMUK Models – NCAL



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NEPOMUK Models – NCO



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• The NEPOMUK Annotation Ontology (NAO)• Vocabulary that enables users to attach custom

descriptions, identifiers, tags and ratings to resources on their desktop

• Via other properties, the user is also able to make generic relationships between related resources explicit.

• Relationships between resources that are too general to be included at the domain ontology level are also defined in the annotation ontology

• Given the high-level status of this ontology, these properties can be used to link any related resources on the user's desktop, as well as provide custom human-readable textual annotations

NEPOMUK Models



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NEPOMUK Models – NAO

Basic

Specific

Conventional tagging



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Blueprint



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Blueprint (design rationale)



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Blueprint – Social Services



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•Foundational layer for achieving social collaboration

•Main functionality

• Distributed information management

• Communication

• Security

Social Services



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•Enables cross-desktop communication

•Handles both service-to-service and human-to-human communication

•Acts as message-carrier for the Notification service

Messaging service



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•Enables seamless management of groups of persons

•Should interact closely with the Messaging service and the Access Control Management

Group management



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•Defines and applies (fine-grained) access control on the resources present in the shared information space

•Should interact closely with the Group Management

Access control management



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•Represents the main access point to the social services infrastructure

•Should ensure a transparent single-sign-on process

Authentication



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•Represents the backbone of the Social Services layer

•Insures proper distributed storage and search of the resources present in the shared information space

•Should support in a transparent way both data and metadata

Distributed storage and search



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Overall interaction recommendation

OROR



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Blueprint – Semantic Desktop Services



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• Semantic Desktop middleware – foundational layer for achieving

• Personal Information Management• Data interoperability

• Structure• Core services – minimum set of services needed to have a

Semantic Desktop

• Data services – additional set of services needed to achieve data interoperability

• Other – extra set of services targeting different particular functionalities

• Service registry – main access point to the semantic desktop middleware

Semantic Desktop Services



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Core services



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• Central metadata and structured data store

• RDF Storage and access to semantic resources and their descriptions

• Provides full text indexing and inference

• Support for multiple query languages

• Main hub for data integration• Lifted data from the Data Wrapper and other services• User’s Personal Information Model

Local storage



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• High-level wrapper for the PIM concepts present in the Local Storage

• Implements commonly used methods to manipulate a PIM

• Fully dependent on the Local Storage solution

• Typical functionalities:• creating and deleting classes, properties, and

resources • adding and removing ontologies • various convenience methods • the URI identification of the user • the personal namespace of the user • the URI of the user's personal information model

PIM Service



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• Lifting the data present on the desktop

• Semantic wrapper for the data (RDF conversion)

• Uses plug-ins to interact with legacy systems – each plug-in is specialised on extracting the data from one application format (such as the addressbook) or a file-type (such as PDF, MS-Word)

• Stores the RDF into the Local Storage

Data wrapper



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• Providing an abstraction layer for accessing the Local Storage and possibly other service which may be involved in the search process

• Enables the community to build arbitrarily complex extensions to the query processing workflow, without changing the corresponding API.

• Support for arbitrary query languages (language can be specified as a parameter to the query)

• Support for full-text search and ranking

• Support for search shortcuts (similar to stored procedures) of more complex SPARQL queries (e.g., my recent documents)

Local search



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• Generic service for multiple notification mechanisms

• Represents a glue between the services that perform tasks based on the results of the behaviour of other services

• Some notifications can be directly translated into user feed-back

• Examples:• Black-board• Publish-subscribe

Notification



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Data services



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•Provides the mechanisms for:

• Finding mappings between ontologies

• Learning or enhancing mappings

•High-level information integration

•Querying data sources based on the learned mappings

•Examples: mappings between 2 PIMs

Social data alignment



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• Aligns data present in the Local Storage

• Suggests new links or annotations between information elements and resources

• Finds entities that are usable as entries in the user's PIM

• Unifies multiple representations of the same entity into one thing (such as two information elements representing the same person into one)

• Improves results based on learning from the user’s feed-back

Local data alignment



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•Translating an RDF graph from one ontology language into another

•Translation from the languages used for manipulating the data present in the Local Storage is other formats

• Example: RDF graphs from FOAF to vCard

Data translation



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•Recommends similar or related resources as well as additional resource descriptions to a particular resource

•Based on the metadata describing a resource other resources and/or additional metadata descriptions are recommended

•The recommendations are can be performed not only from the Local Storage, but also from the Shared Information Space

Resource and metadata recommender



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Other



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•Generic service for performing specific reasoning tasks

•Can be used for example by the recommendation services, or other services to reason on the data present in the Local Storage

Reasoning service



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•Creates abstract contexts and detects the current context by observing the user activity

•Should contain a user observation (logging) hub

•Generates context models to be used by the other services for learning or profiling

Context elicitation



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•Umbrella of services performing low-level natural language processing

•The results can be used for enriching the models of the information elements (lifting data)

•Examples• Keyword extraction• Speech acts detection• Rhetorical elements and relations extraction

Text analytics services group



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•Umbrella of specific services that compute recommendations for information elements, based on different criteria

•Examples:• Tag recommender• Community (group) recommender• Person recommender

Recommendation services group



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Service registry



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•Central access point to all the services present in the platform

•Allows publishing, removing and discovery of services

•Represents a wrapper of the Semantic Desktop middleware

Service registry



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Blueprint – Extensions and Presentation Layer



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•Services developed for specific needs

•Combine directly the functionality provided by the Semantic Desktop Middleware and the Social Services

•Can provide complex additional functionalities for the presentation layer

Semantic Desktop extensions



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• High level personal information management extension with focus on tasks

• Ad-hoc task planning and flexible changes

• Collaborative work on tasks

• Connecting tasks with personal models as well as group information objects, and integration into organizational processes

• Task patterns recognition and learning

Task Management extension



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•Knowledge workbench ~ ordinary day-to-day applications

•Integrate functionality provided by the layers below via plug-ins or add-ons

•Seamless integration of semantic features into the knowledge workbench

Presentation layer



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Scenario - revisited



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Scenario revisited – implementation


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systems

Analysis



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Objectives

•Is the current list of scenarios complete?

• Are there scenarios missing?• If yes, is the blueprint able to handle them?

•Does the blueprint support the revised list of scenarios?



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1. There should be a common framework for the customization and personalization of the desktop

2. There must be support of a single authentication step as a prerequisite for collaboration and access to the share information space

3. Access to the shared information space should be in a transparent manner

4. Collaboration should be transparently supported by context awareness

5. Document management and sharing should be in a transparent manner

6. Creation and management of collaborative groups should be transparent, supported by a simple management of people competence profile

7. Communication within a team should be automated supported by presence and context awareness mechanisms

8. There should be persistency of personal work spheres such that there is continuity between different working sessions

9. The document management of the work space should be semantically enriched, thus enabling sophisticated searches and filtering

Scenarios



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Desktop customization and personalization (1)

• (US) When Claudia starts her SSD she wants to see her desktop customized/organized according to her user profile, context, user preferences, interactions, collaborations and tasks she is involved in.

• (SS) System should be able to authenticate user based on his credentials. Once authentication is done, system should be able to process user context to determine the activity space user wants to login to. If there are specific user preferences to personalize the user desktop then they should be processed to customize the user desktop. The system should also be able to process the user context to retrieve all the files/folders owning to the user context.



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Single authentication (2)

• (US) Claudia is working in two different projects, with two slightly overlapping teams. Claudia wants to get simple access to sensitive data subject to strict security policy enforced in the two projects. She wants to be able to do this using a single login for all her profiles, involved in the different projects.

• (SS) The system will need to provide identity management for different profiles of the same user. The system will also need to provide an authentication mechanism to authenticate the profiles in the different shared information spaces. The shared information space system checks the user identity and in case of success, allows access to private data.

• Note: the same requirements are also relevant for the transparent access to the shared information space scenario



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Transparent collaboration based on context awareness (4)

• (US) Claudia wants to search and share specific project or group resources (based on the working context and document relationships) from different projects into a particular share information space (created for example for the company’s audit). Claudia also wants to build an ad-hoc team based on the working context (with the other project managers).

• (SS) The system should allow to manage (i.e. create or update) the working context for the resource (e.g. documents or folder). The system should be able to process the current working context to search the resources related to the particular working context. The system should allow uploading and sharing of documents. Sharing involves also sending notifications to relevant users.

• System should provide a mechanism to handle data interoperability issues while sharing different data objects.



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Please fill-in Questionnaire B …

http://www.surveymonkey.com/s.aspx?sm=61wctkDVyCsCDVx3xaCzEw_3d_3d


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systems

Thank you!

Social Semantic Desktop Reference Architecture Evaluation

Documents

Transcript of Social Semantic Desktop Reference Architecture Evaluation