Extracted Concept - Bivee

beeuppityΤεχνίτη Νοημοσύνη και Ρομποτική

19 Οκτ 2013 (πριν από 3 χρόνια και 10 μήνες)

72 εμφανίσεις

12/03/2013








1

Second International Workshop on

New Generation Enterprise and Business Innovation

NGEBIS 2013


Cross Domain
Crawling

for

Innovation


Pieruigi

Assogna
,
Francesco Taglino

CNR
-
IASI (Italy)


12/03/2013








2

Outline

Motivations

&
Objectives

Methodological

approach

Technological

approach

Conclusions


12/03/2013








3

Motivations

and
Objectives

In any kind of organization, creativity and
innovation come from people

Tools aiming at supporting creativity need to be
based on the most accredited theories related
to how people use their knowledge to act on the
environment, adapt to new situations, invent.


The method proposed here aims at providing
knowledge “raw material”, capable of triggering
out
-
of
-
the
-
box ideas

12/03/2013








4

Constructivism

According to Constructivism a person’s
culture is an integrated network of
concepts and models

This guides the person’s activity, and is
consolidated, enriched, modified by each
new experience

Apart from pathological situations
(schizophrenia) each person’s structure is
anyway connected


12/03/2013








5

New
Paths

The connections between concepts create
paths that, with time, our mind travels
more or less automatically

In new situations we have to “take the
lead” and try new paths, possibly linking
different and distant clusters

This is for instance what is favored by
“lateral thinking” methods

12/03/2013








6

Knowledge

Base

In general a domain Knowledge Base (KB)
is a tool for maintaining and enriching its
users’ focused knowledge

In particular the KB’s ontology mimics
their focused conceptual structure

When the users are confronted by new
issues, a search on the KB or on the Net
(on the base of the domain ontology)
typically keeps them within this focused
ground


12/03/2013








7

The
Methodology

We propose a way to extend a focused
knowledge domain to support diversions from
usual thinking paths

We use the domain ontology to search the Net
for documents that address key topics of the
domain together with topics belonging to
different ones

These documents have good probability of
containing considerations, theories, metaphors
that link the person’s knowledge clusters with
“exotic” ones, able to trigger ideas out
-
of
-
the
-
box


12/03/2013








8

Semantics
-
based cross
-
domains
crawling

12/03/2013








9

Documental Resources Space

where we search for interesting documents

websites

(e.g., MIT website on
innovations),
RSS feeds
, and
public
documents repositories

(e.g., BBC news)

In our example we focus on Robotics and
Machine Vision (R&MV) domain

12/03/2013








10

Linked

Data

A set
of

principles

to

allow

Standard
description

of

data (
RDF
-
based
)

Standard way of accessing data (HTTP)

Linking

resources
/data
among

them


Linking

Open Data
as

a project
for

publishing

datasets

(e.g.,
Dbpedia
) in a
Linked

Data fashion

12/03/2013








11

The
Linking

Open Data
cloud

DBpedia

12/03/2013








12

Reference

ontology

and bridge

to

the LOD
cloud

Within

the BIVEE project
we

have

built

a
glossary

of

600
concepts

on
R&MV

We

enriched

such

concepts

with

DBpedia

entries

(
owl
:
sameAs
)

Photodiodes

R&MV

reference

ontology

DBpedia

Photodiode

http://dbpedia.org/page/Photodiode

owl
:
sameAs

Camera

Camera

http://dbpedia.org/page/Camera


owl
:
sameAs

12/03/2013








13

Terms

extraction

from

analyzed

document

Extracted terms/concepts are
representative and somehow synthesize
the document’s content

We analyzed different tools for extracting
knowledge from documents

Zemanta
,
Alchemy
,
OpenCalais
, FISE

AlchemyAPI
: extract concepts from a text

relevance value

link to
DBpedia

and other LOD dataset

12/03/2013








14

Semantic

Filter

over

a doc

Two

steps

Identify

the
extracted

concepts

related

to

our

domain
of

interest

Identify

good

candidate and
discarding

not

interesting

documents

12/03/2013








15

Semantic

Filter

over

a doc:
step

1

Identify

the
extracted

concepts

related

to

our

domain
of

interest (e.g.,
R&MV
)

Given

an

extracted

concept

ec
,
it

exists

at
least

one

reference

concept

rc
,
such

that






Extracted

Concept

(
ec
)

(
r
1

=
ref
.
to

Dbpedia

entry)

Reference

Ontology

Concept

(
rc
)

(
r
2

=
ref
.
to

Dbpedia

entry)

(
r
1

dc
:
subject
)
r

AND (
r
2

dc
:
subject

r
)

where

r
is

a
resources


r
1

=
r
2

OR

12/03/2013








16

Semantic

Filter

over

a doc:
step

2

Let be
S1

the set of extracted concepts
related
to our domain

Let be
S2

the set of extracted concepts
NOT related

to our
domain

A document is a good candidate if

(a) t1<Sum(
relVal
(S1))<t2 AND

t
1
=0.1, t
2
=0.4

(b) Sum(
relVal
(S2))>t3



t
3
=0.4


(a) ensures that the analyzed document deals with our
reference domain, but in a
small

manner,

(b) second constraint ensures that the analyzed document
deals with other topics in a considerable measure.

12/03/2013








17

Filtering
:
example

1

Extracted

Concepts

and

Relevance

The document is about extracting energy from insects

SUGGESTED AS
INTERESTING

12/03/2013








18

Filtering
:
example

2

Extracted

Concepts

and

Relevance

The document is about supporting shoppers get the right fit
when buying clothes online

SUGGESTED AS
INTERESTING

12/03/2013








19

Filtering
:
example

3

Extracted

Concepts

and

Relevance

The document does not consider Robotics and Machine Vision at all

NOT INTERESTING
document

12/03/2013








20

Filtering
:
example

4

Extracted

Concepts

and

Relevance

The document is
too much Robotics oriented
, so it can be surely useful
for experts in the Robotics field, but it does not appear inspiring for lateral
thinking

NOT INTERESTING
document

12/03/2013








21

Conclusions

and Outlook

Very

preliminary

work on
supporting

lateral

thinking

activities

More
experimentation

Using

the LOD
cloud

as

much

as

possible

12/03/2013








22

Questions

&

Answers