Presentation given at "Global Interoperability and Linked Data in Libraries", University of Florence, 18 Jun 2012

1. L ink ed Da ta
social challenges
som e tech &
michele barbera
<barbera@netseven.it> @barbz79it

2. 1

3. TECH

4. CHALLENGES
2

5. SOCIAL

6. CHALLENGES
3

7. LINKED

8. DATA

9. ECONOMY

10. smart data now! Unità Web of Data
spaziodati.eu netseven.it fbk.eu

11. Is

12. the

13. Semantic

14. Web

15. real?

16. no*.
*

17. I’m

18. provocative

19. we

20. aimed

21. at

22. this:

24. and

25. failed*.
*

26. But

27. produced

28. ~170k

29. research

30. papers

31. in

32. 11

33. years,

34. not

35. bad!

36. Pizza

37. ontology?!

38. well,

39. not

40. really

41. failed...

42. we’re

43. still

44. working

45. on

46. it

47. less

48. pizza

49. more

50. engineering

51. A

52. little

53. semantics

54. goes
a

55. long

56. way...
Semantic

57. Web
Linked

58. Data

59. Semantic

60. Web
Linked

61. Data
Web

62. of

63. Data

64. it’s

65. not

66. just

67. technology

68. it’s

69. definetely

70. not

71. AI

72. it’s

73. just

74. about

75. linking

76.
things

77. together

78. your web site
DATA IS LESS VALUABLE WHEN SILOED

79. because

80. value

81. is

82. in

83. context

85. content

86. is

87. king

88. x
content

89. is

90. king

91. linking

92. t e ch
s o m e es
i s s u

93. 1

94. SCALABILITY

95. is

96. it

97. all

98. about

99. size?
Flexibility,

100. dinamicity,

101. scalability
by Giovanni Tummarello

102. dataspaces
by Giovanni Tummarello

103. Large

104. Scale

105. RDF

106. summaries
12M

107. relationships
Class Level
http://test01.sindice.net/szydan/dataset-view/dataset/default/www.bbc.co.uk
by Giovanni Tummarello

108. Large

109. Scale

110. RDF

111. summaries
12M

112. relationships
Class Level
10B

113. relationships
http://test01.sindice.net/szydan/dataset-view/dataset/default/www.bbc.co.uk
by Giovanni Tummarello

114. 2

115. -

116. streaming

117. linked

118. data

119. 3

120. -

121. versioning
moved
deleted
SPOT

122. THE

123. DIFFERENCE

124. self_promotion

127. SIREn
Data Collection Settings
 500M web data documents  Cluster of 4 nodes
(RDF, RDFa, Microformat, etc.)  2 nodes for indexing
 200K datasets  2 nodes for querying
 50B triples  Replication
Indexing Performance Services
 Full index construction takes  Keyword and structured queries
approx 24 hours  Dataset search
 436K triples / second  99% uptime

128. SIREn
Data Collection Settings
 500M web data documents  Cluster of 4 nodes
(RDF, RDFa, Microformat, etc.)  2 nodes for indexing
 200K datasets  2 nodes for querying
 50B triples  Replication
Indexing Performance Services
 Full index construction takes  Keyword and structured queries
approx 24 hours  Dataset search
 436K triples / second  99% uptime

129. SIREn
Data Collection Settings
 500M web data documents  Cluster of 4 nodes
(RDF, RDFa, Microformat, etc.)  2 nodes for indexing
 200K datasets  2 nodes for querying
 50B triples  Replication
Indexing Performance Services
 Full index construction takes  Keyword and structured queries
approx 24 hours  Dataset search
 436K triples / second  99% uptime

130. SIREn
Data Collection Settings
 500M web data documents  Cluster of 4 nodes
(RDF, RDFa, Microformat, etc.)  2 nodes for indexing
 200K datasets  2 nodes for querying
 50B triples  Replication
Indexing Performance Services
 Full index construction takes  Keyword and structured queries
approx 24 hours  Dataset search
 436K triples / second  99% uptime

131. SIREn
Data Collection Settings
 500M web data documents  Cluster of 4 nodes
(RDF, RDFa, Microformat, etc.)  2 nodes for indexing
 200K datasets  2 nodes for querying
 50B triples  Replication
spaziodati.3scale.net
Indexing Performance Services
 Full index construction takes  Keyword and structured queries
approx 24 hours  Dataset search
 436K triples / second  99% uptime

132. SIREn
Data Collection Settings
 500M web data documents  Cluster of 4 nodes
(RDF, RDFa, Microformat, etc.)  2 nodes for indexing
 200K datasets  2 nodes for querying
 50B triples  Replication
spaziodati.3scale.net
Indexing Performance Services
 Full index construction takes  Keyword and structured queries
approx 24 hours  Dataset search
 436K triples / second  99% uptime

133. SIREn
Data Collection Settings
 500M web data documents  Cluster of 4 nodes
(RDF, RDFa, Microformat, etc.)  2 nodes for indexing
 200K datasets  2 nodes for querying
 50B triples  Replication
spaziodati.3scale.net
Indexing Performance Services
 Full index construction takes  Keyword and structured queries
approx 24 hours  Dataset search
 436K triples / second  99% uptime

134. /self_promotion

135. som e

136. so ci al
is su es

137. 1

138. THINKING

139. IN

140. THE

141. GRAPH

142. 1

143. -

144. thinking

145. in

146. tables

147. 1

148. -

149. thinking

150. in

151. tables

152. 1

153. -

154. thinking

155. in

156. tables

157. 1

158. -

159. thinking

160. in

161. tables

162. 1

163. -

164. thinking

165. in

166. tables

167. 1

168. -

169. thinking

170. in

171. tables

172. 1

173. -

174. thinking

175. in

176. tables

177. 1

178. -

179. thinking

180. in

181. tables

182. 1

183. -

184. thinking

185. in

186. tables

187. thinking

188. in

189. tables
u_id f_id
1 2
1 3
3 4
4 3
id name age affiliation
1 Michele 33 net7
2 Mario 32 unipi
3 Silvia 28 unifi
4 Irene 27 unitn Institution City
net7 pisa
unipi pisa
unifi firenze
unitn trento

190. thinking

191. in

192. graphs?
pisa Firenze
place
Trento
e
e
plac
plac
unipi
net7 unifi
e
plac
ks
ks
unitn
wor
wor
friend
frien
ks
d
michele

193. (33)
wor
silvia

194. (28)
frien
d irene

195. (27)
fri
end
mario

196. (32)

197. e.g.
soci
al

198. gr
ap hs
Mario

199. 342-2345672
,

200. 12

201. Apr,

202. Via

203. san

204. G
Giovanni

205. 333-231345 iuseeppe

206. 34
0,

207. Bologna,

208. via

209. Ugo
Anna

210. 328-3422345,

211. Bassi

212. 12

213. Trento,

214. p.zza

215. Ver
Mamma

216. 050-342212 di

217. 11
4,

218. Pisa
Antonio

219. 051-34245
6,

220. Bologna

221. 2

222. -

223. A.A.A.*
*

224. “you

225. don’t

226. know

227. what

228. you’re

229. talking

230. about”

231. AAA
library wikidb
scholarly

232. community

233. tbl
“
The

234. less

235. inviting

236. side

237. of

238. sharing

239. is

240. losing

241.
some

242. control.

243. Indeed,

244. at

245. each

246. layer

247. ---

248. “
Net,

249. Web,

250. or

251. Graph

252. ---

253. we

254. have

255. ceded

256.
some

257. control

258. for

259. greater

260. benefits”
“ It

261. is

262. about

263. getting

264. excited

265. about

266.
“
connections,

267. rather

268. than

269. nervous”

270. 3)
info

271. vs.

272. non-info

273. http-range-14
http://example.com/resource/CNR
http://example.com/page/CNR 303

274. redirection?
http://example.com/data/CNR
http://www.cnr.it/homepage#CNR
http://www.cnr.it/homepage
hash

275. uri?

276. caution!
http://universities.org/italy#cnr
ns:president a_person
ns:department some_department
ns:department some_department
owl:sameAs
http://www.example.com/cnr
ns:creator jonnhy

277. web

278. developer
ns:date 12

279. Jun

280. 2011
ns:name “The

281. Website”

282. 4)
Open

283. World

284. Assumption

285. Kbase
Seat

286. 14

287. is

288. reserved
Seat

289. 27

290. is

291. reserved
OWA CWA
is

292. seat

293. 28

294. reserved?
UNKNOWN NO

295. -

296. OWA

297. is

298. not

299. difficult

300. to

301. understand
-

302. OWA

303. is

304. good

305. to

306. deal

307. with

308. inconsistencies

309. and
universal

310. systems
-

311. We’re

312. more

313. familiar

314. with

315. CW

316. reasoning
-

317. many

318. existing

319. tools

320. are

321. CW

322. D a ta
d i e my?
k o
n n
a

323. L o
e c

324. -

325. ~

326. 300

327. datasets
-

328. not

329. frequently

330. updated
Linked

331. Data
-

332. 0,1

333. %

334. of

335. the

336. Web

337. of

338. Data

339. Web

340. of

341. Data

343. h1 id=name
span class=fn n
span class=given-nameMichele
/span
span class=family-nameBarbera
/span /span/h1

344. www.rottentomatoes.com

348. Schema.org
http://www.linkedopendata.it/schema-org-e-le-responsabilita-dei-
monopolisti

350. G

351. knowledge

352. graph

354. Freebase

355. +

356. Geonames

357. +

358. DBpedia

359. +

360.
schema.org

361. +

362. search

363. statistics?
opaque/hidden

364. identifiers

365. =

366. not

367. reusable

368. 5 billion 40%global data
mobile phones 30 billion
pieces of content shared
growth in
projected
generated per year vs 5%
on facebook every month
235
terabytes
15 out of 17
data collected
by US library
of Congress
60%
potential increas in retailers’
sectors in US have more data stored
per company than the US Library
in april 2011 operating margins possible
with big data
of Congress
BIG DATA AND INFO OVERLOAD IN USE IN 2010:
250$ billion
potential annual value
600$ billion
300$ to Europe’s public sector
potential annual consumer
surplus from using
billion administration - more
than GDP of Greece
personal location data globally
potential annual value
to US health care
(more than double
the total annual 60% 140.000-190.000
more deep analytical talent positions
health care potential increase and 1,5 million more data-savvy managers
spending in Spain) in retailers’ operating margins need to take full advantage of big data
possible wiith big data
with big dat only in United States

369. “ “
The

370. real

371. value

372. of

373. the

374. GKG

375. may

376. be

377. in

378. what

379. gets

380.
deleted

381. instead

382. of

383. what

384. gets

385. added.
Paul

386. Houle,

387. http://lists.w3.org/Archives/Public/public-lod/2012Jun/0038.html

388. Open

389. Data

390. (and

391. digital

392. public

393. goods)

394.
r e p r e s e n t s

395. a n

396. u n p r e c e d e n t e d

397.
opportunity

398. to

399. build

400. a

401. (local?

402.
vertical?)

403. data

404. economy

405. and

406. to

407.
preserve

408. our

409. cultural

410. diversity

411. “ The

412. gist

413. of

414. the

415. matter

416. is

417. to

418. turn

419. large

420. streams

421. of

422. data

423. into

424.
added

425. value

426. for

427. the

428. public

429. and

430. private

431. sector

432. [...]
Clearly,

433. research,

434. engineering,

435. policy

436. making

437. for

438. the

439. Data

440.
Economy

441. and

442. the

443. exploitation

444. of

445. the

446. unprecedented

447. wealth

448. of

449.
“
data

450. have

451. become

452. keys

453. to

454. the

455. Future

456. of

457. Europe.

458. WE CAN DO IT!!!

459. Thank

460. you.
@barbz79it