Linked Data: some social challenges
19 likes18,693 views
The document discusses the challenges and approaches related to linked data and the semantic web, highlighting the importance of context and content linking for enhancing data value. It reflects on the shortcomings of existing semantic technologies and emphasizes ongoing efforts to improve these systems. Notably, it mentions data collection settings and performance metrics associated with large-scale linked data projects.
Linked Data: some social challenges
- 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. ---
- 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
- 267. rather
- 268. than
- 269. nervous”
- 270. 3) info
- 271. vs.
- 272. non-info
- 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
- 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
- 381. instead
- 382. of
- 383. what
- 384. gets
- 385. added. Paul
- 386. Houle,
- 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
- 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
- 441. and
- 442. the
- 443. exploitation
- 444. of
- 445. the
- 446. unprecedented
- 447. wealth
- 448. of
- 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