LarKC Tutorial at ISWC 2009 - Introduction

Agenda for today October 2010 @ ISWC Time Presentation Title Presenter 08.30-09.00 Setup 09.00 – 09.30 Introduction to LarKC Frank van Harmelen 09.30 – 10:30 LarKC Architecture Michael Witbrock 10:30 – 11:00 Coffee break 11:00 – 11:30 Hands-on: work with an existing LarKC workflow Florian Fischer 11:30 – 12:00 LarKC Data Layer Florian Fischer 12:00 – 13.00 Builder a LarKC DECIDEr & create a workflow from existing plugins Luka Bradesko, Blaz Fortuna 13.00 – 14:00 Lunch 14:00 – 14:30 Distributed Processing in LarKC Michael Witbrock 14:30 – 15:30 Hands-on: Building a LarKC Plugin and integrating it in a worflow Florian Fischer 15.30 – 16:00 Coffee break Eyal Oren 16:00 – 17:00 Hands-on: the Urban computing workflow Emanuele della Valle 17.00 – 18:00 Wrap up - Discussion and feedback Frank van Harmelen

Welcome to the 2nd LarKC Early Adopters Workshop Frank van Harmelen Vrije Universiteit Amsterdam

Health Warning

Today is a WORK shop

we first tell you some stuff,

then you do stuff

(repeat)

Goal of today:

ours: show LarKC to outsiders < who are we >,

yours: <tell us now>

Goals of today

At the end of today you will

understand the goals of LarKC

understand the architecture of LarKC

have hands on experience with platform and plugins

At the end of the day, you will be able to:

roll your own LarKC plugin

roll your own LarKC application

Goals of LarKC LarKC = a platform for large scale reasoning “ LarKC's value is as an experimental platform . LarKC is as an environment where people can go to replicate (or extend) their results in an environment where all the infrastructural heavy lifting has already been taken care of ” Quote from EU Project Officer:

Goals of LarKC LarKC = a platform for large scale reasoning Semantic web research is stifled by the complexity of writing a large scale engine, with services for data access, storage, aggregation, inference, transport, transformation, etc, Physics research has dealt with a similar problem by providing large scale infrastructure into which experiments can be plugged. The idea behind LarKC, which I found so compelling, is that people who wanted to build small scale plugins, for example, plugins for some non-standard deduction, or transformation of text to triples, or estimating the weights for relational models, could do so, taking advantage of the EU's investment in a platform with significant capabilities .“ Quote from US high-tech CTO:

Goals of LarKC LarKC = a platform for large scale reasoning “ Significant progress is sometimes made not by making something possible that was impossible before, but by substantially lowering the costs of something that was only possible before at high cost” Quote from EU Reviewer:

What do we mean by:

reusable components

reconfigurable workflows

provide infrastructure needed by all users:

storage & retrieval

registration of plugins

communication (plugin2datalayer, plugin2plugins)

synchronisation (anytime behaviour)

remote execution (abstracts from local/remote storage)

remote data-access (abstracts from local/remote invation)

(will) provide instrumentation & measuring

(will) provide caching & data-locality

integration of very heterogeneous components

heterogeneous data: unstructured text, (semi)structured data

heterogeneous code: Java, scripts, remote services ("wrap & integrate")

LarKC = a platform for large scale reasoning

What do we mean by: LarKC = a platform for large scale reasoning

raw large numbers

from performant data-layer

from parallel deployment of plugins

from load-balancing strategies

…

interaction of multiple components

e.g. avoid reasoning through selection: SELECT + REASON

allowing for incompletenes and anytime behaviour

but also from not only from

What do we mean by:

not only: deductive inference over given axioms

but also:

LarKC = a platform for large scale reasoning LarKC = a platform for large scale reasoning LarKC = a platform for large scale reasoning LarKC = a platform for large scale reasoning where do the axioms come from? ( IDENTIFY ) which part of knowledge & data is required ( SELECT ion) when is an answer "good enough" or "best possible" ( DECIDE r) non-deductive inference (inductive, statistical) ( REASON er) “ ReaSearch: integrating reasoning and search "

Overall approach of LarKC

Very lightweight platform

communication, synchronisation, registration

LarKC = “SPARQL endpoint on steroids”

The real work happens in the plugins

LarKC gives you:

very scalable datalayer

standardised interfaces for combining components

utilities & infrastructure

Three types of LarKC users:

people building plugins

people configuring workflows

people using workflows

How to deploy LarKC

All local:

platform local, plugins local

Example: workstation

Calling remote plugins:

platform local, (some) plugins remote

Example: laptop

Fully remote

platform remote (eg. as a web-service)

plugins remote

Example: cluster

Why would people (like you) want to use LarKC

workflow builders:

easier to get some application scenario running

Plugin builders:

easier integration with components by others,

wider take up of your own component by others

What does a workflow look like? Identifier Info Set Transformer Reasoner Decider Selector Query Transformer Data Layer

What does a workflow look like? Identifier Info Set Transformer Reasoner Decider Selector Query Transformer Data Layer Data Layer Data Layer Data Layer Data Layer

What does a workflow look like? Identifier Info Set Transformer Reasoner Decider Selector Query Transformer

What does a workflow look like? Identifier Reasoner Decider Selector

What does a workflow look like? Reasoner Decider Selector

What does a workflow look like? Identifier Info Set Transformer Reasoner Decider Selector Query Transformer Identifier Identifier Info Set Transformer ETCETERA

What does a DECIDEr look like?

Can be a hardcoded sequence of plugins

Can be a self-configuring selection of plugins

Can make run-time decisions on progress and resource consumption

Coded as:

Java

a Cyc knowledge base

...

as long as it complies with the DECIDE r API

Already any plugins available?

5x IDENTIFY

3x TRANSFORM

10x SELECT

4x REASON

4x DECIDE

Sometimes sophisticated, sometimes simple

Sometimes novel, sometimes wrapped

existing web-services (e.g. Sindice, Swoogle)

another RDF store (geo-queries in Allegrograph)

a very large (workflow-based) system (GATE)

existing reasoners (Jena, Pellet, Cyc, IRIS)

XSLT scripts (XML-2-RDF)

spreading activitation (new)

RDF-2-weightedRDF (new)

Goals of LarKC, and where we are

Scalable: > 10 9 triples, lazy pipes

Reconfigurable: plugins with standard API’s

Open: Apache license

heterogenous: TRANSFORM, wrappers

experimentation: wrap & integrate

allow incompleteness: IDENTIFY, SELECT

enable distribution: plugin containers

anytime behaviour: streaming APIs

web-enabled: remote plugins & data

What we will not show today

Available but not demo’d:

lot’s of plugins

C-SPARQL: extension of SPARQL to enable stream-querying

cognition-based heuristics (e.g. selection rules, stopping rules)

very cool data-sets

Linked Life Data (1.4B explicit, 2.3B closure, 1.3M links)

Milan traffic grid (2M explicit +2Tb sensor-data (to come))

Interest-enhanced DBLP (615k authors + interests)

LDSR (358M explit + 512 inferred, 100m URIs)

very large/fast inference engines: MarVIN, Reasoning-Hadoop

Not yet available (but will be):

plugin-farming on remote CPU’s (cloud, cluster)

instrumentation & measuring

smart data caching

Agenda for today October 2010 @ ISWC Time Presentation Title Presenter 08.30-09.00 Setup 09.00 – 09.30 Introduction to LarKC Frank van Harmelen 09.30 – 10:30 LarKC Architecture Michael Witbrock 10:30 – 11:00 Coffee break 11:00 – 11:30 Hands-on: work with an existing LarKC workflow Florian Fischer 11:30 – 12:00 LarKC Data Layer Florian Fischer 12:00 – 13.00 Builder a LarKC DECIDEr & create a workflow from existing plugins Luka Bradesko, Blaz Fortuna 13.00 – 14:00 Lunch 14:00 – 14:30 Distributed Processing in LarKC Michael Witbrock 14:30 – 15:30 Hands-on: Building a LarKC Plugin and integrating it in a worflow Florian Fischer 15.30 – 16:00 Coffee break Eyal Oren 16:00 – 17:00 Hands-on: the Urban computing workflow Emanuele della Valle 17.00 – 18:00 Wrap up - Discussion and feedback Frank van Harmelen