Tutorial Cognition - Irene

COGNITION FOR THE SEMANTIC WEB 1

Cognition for the Semantic WebIrene Celino + Tomi Kauppinen

SSWS 2016 - Bertinoro, 22th July 2016


Cognition… what? Cognitive Computing refers to systems that learn at scale and interact naturally with people to extend what humans or machines can do alone

(Eleni Pratsini – IBM Research, ESWC 2016 Keynote)

Cognitive Systems studies aim at (1) gaining a deeper understanding of how humans realize their unique intelligent potential and (2) developing artificial cognitive agents to assist humans in performing demanding tasks in order to strengthen their autonomy

(Cognitive Systems Group, University of Bremen)



This morning tutorial: Cognition for the Semantic Web Irene will talk about “Involving Humans in Semantic Data Management”

methods, techniques and tools to include humans in a Semantic Web pipeline

leverage humans to develop better Semantic Web systems

Tomi’s talk title is “Let us build cognitive & semantic systems to support understanding”

via visual examples on why and how to make sense of data and to understand underlying phenomena

methods, evaluation, discoveries


Cognition for the Semantic Web 4

Cognition for the Semantic WebInvolving Humans in Semantic Data Management

Irene Celino ([email protected]) CEFRIEL – Milano, Italy



Agenda Methods to involve people (a.k.a. crowdsourcing and its brothers)

Motivation and incentives (a.k.a. let’s have fun with games) Crowdsourcing and the Semantic Web (a.k.a. this is SSSW after all…)



Methods to involve people

What goals can humans help machines to achieve? Which scientific communities “exploit” people? How to involve a crowd of persons?

- Citizen Science - Crowdsourcing - Human Computation



Wisdom of crowds [1]

“Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations”

Criteria for a wise crowd Diversity of opinion (importance of interpretation) Independence (not a “single mind”) Decentralization (importance of local knowledge) Aggregation (aim to get a collective decision) The are also failures/risks in crowd decisions: Homogeneity, centralization, division, imitation, emotionality



Citizen Science [2]


Problem: a scientific experiment requires the

execution of a lot of simple tasks, but researchers are

busySolution: engage the general

audience in solving those tasks, explaining that they are

contributing to science, research and the public good

Example: https://www.zooniverse.org/

https://www.zooniverse.org/




Crowdsourcing [3]


Problem: a company needs to execute a lot of simple tasks,

but cannot afford hiring a person to do that job Solution: pack tasks in

bunches (human intellingence tasks or HITs) and outsource

them to a very cheap workforce through an online

platform

Example: https://www.mturk.com/

https://www.mturk.com/

https://www.mturk.com/


Human Computation [4]


Problem: an Artificial Intelligence algorithm is

unable to achieve an adequate result with a satisfactory level

of confidence

Solution: ask people to intervene when the AI system

fails, “masking” the task within another human process

Example: https://www.google.com/recaptcha/

https://www.google.com/recaptcha/

https://www.google.com/recaptcha/


Spot the difference… Similarities: - Involvement of people - No automatic replacement

Variations: - Motivation - Reward (glory, money, passion)

Hybrids or parallel!


Citizen Science

Crowdsourcing

Human Computation


Motivation and incentives

Apart from extrinsic rewards (money, prizes, etc.) what are the intrinsic incentives we can adopt to motivate people? How can we leverage “fun” through games and game-like applications? - Gamification - Games with a Purpose (GWAP)



Gamification [5,6]


Problem: motivate people to execute boring or mandatory

tasks (especially in cooperative environments)

that they are usually not very happy to do

Solution: introduce typical game elements (e.g. points, badges, leaderboards) within

the more traditional processes and systems

Example: https://badgeville.com/

https://badgeville.com/

https://badgeville.com/


Games with a Purpose (GWAP) [7,8]


Problem: same of Human

Computation (ask humans when AI

fails)

Solution: hide the task within a game, so that users are

motivated by game challenges, often remaining unaware of the hidden purpose, task solution

comes from agreement between players

Example: http://www.gwap.com/

http://www.gwap.com/

http://www.gwap.com/


Crowdsourcing and the Semantic Web

Can we involve people in Semantic Web systems? What semantic data management tasks can we effectively “outsource” to humans?



Why Crowdsourcing in the Semantic Web? Knowledge-intensive and/or context-specific character of Semantic Web tasks: e.g., conceptual modelling, multi-language resource labelling,

content annotation with ontologies, concept/entity similarity recognition, …

Crowdsourcing can help to engage users and involve them in executing tasks: e.g., wikis for semantic content authoring, folksonomies to

bootstrap formal ontologies, human computation approaches, …


Semantic Crowdsourcing [9] Many tasks in Semantic Web data management/curation can exploit Crowdsourcing

SSWS 2016 - Bertinoro, 22th July 2016Cognition for the Semantic Web 17Fact level

Schema level

Collection Creation CorrectionValidation Filtering Ranking Linking

Conceptual

modelling

Ontology populatio

nQuality

assessment

Ontology re-

engineering

Ontology pruning

Ontology elicitation

Knowledge

acquisition

Ontology repair

Knowledge base update

Data search/

selectionLink

generation

Ontology alignment

Ontology matching


Focus on Data Linking Creation of links in the form of RDF triples (subject, predicate, object) Within the same dataset (i.e. generating new connections between resources of

the same dataset) Across different datasets (i.e. creating RDF links, as named in the Linked Data

world)

Notes: Generated links can have an associated score expressing a sort of “confidence” in the truth

value or in the “relevance” of the triple In literature, data linking often means finding equivalent resources (similarly to record linkage

in database research), i.e. triples with correspondence/match predicate (e.g. owl:sameAs) in the following, data linking is intended in its broader meaning (i.e. links with any predicate)


Subject: {Milano cultural heritage assets} Predicate: foaf:depiction Goal: create <asset> foaf:depiction <photo>

Object: ? {photos}

Knowledge acquisition

SSWS 2016 - Bertinoro, 22th July 2016Cognition for the Semantic Web 19

Citizen science

campaign

http://bit.ly/picmiapp

Level badges as intrinsic

incentives (gamification)

Contributions with attribution/recognit

ion

http://bit.ly/picmiapp

Set of all links: <asset> foaf:depiction <photo> Goal: assign score to rank links on their “recognisability”/“representativeness”

Link ranking [10]

SSWS 2016 - Bertinoro, 22th July 2016Cognition for the Semantic Web 20

http://bit.ly/indomilando

Pure GWAP with hidden purpose

Points, badges, leaderboard as intrinsic reward

The score is a function of where is the no. of successes (=recogni-tions) and the no. of trials of the Bernoulli process (guess or not guess) realized by the game

Link ranking is a result of the “agreement”

between players





Link validation [11]


http://bit.ly/foss4game Set of links: <land-area> clc:hasLandCover <land-cover>Automatic classifications: <land-cover-assigned-by-DUSAF> ≠ <land-cover-assigned-by-GL30>

Goal: assign score to each link to discover the “right” land cover classPure GWAP with

not-so-hidden purpose (played by

“experts”)Points, badges, leaderboard as intrinsic reward

https://youtu.be/Q0ru1hhDM9Q

A player scores if he/she guess one of the two disagreeing

classifications

Score of each link is updated on the basis of players’ choices (incremented if link selected, decremented if link not selected)When the score of a link overcomes a threshold , the link is considered “true”

Link validation is a result of the “agreement”

between players

http://bit.ly/foss4game

http://bit.ly/foss4game




Caveat 1: Mice and Men (or: keep it simple) Crowdsourcing workers behave like mice [12] Mice prefer to use their motor skills (biologically cheap, e.g. pressing

a lever to get food) rather than their cognitive skills (biologically expensive, e.g. going through a labyrinth to get food)

Workers prefer/are better at simple tasks (e.g. those that can be solved at first sight) and discard/are worse at more complex tasks (e.g. those that require logics)

Crowdsourcing tasks should be carefully designed Tasks as simple as possible for the workers to solve Complex tasks together with other incentives (e.g. variety/novelty)



Suggestion 1: Divide et impera (or: Find-Fix-Verify)


Find-Fix-Verify crowd programming pattern [13] A long and “expensive” task… Summarize a text to shorten its total length …is decomposed in more atomic tasks… 1. find sentences that need to be shortened 2. fix a sentence by shortening it 3. verify which summarized sentence maintains original meaning …and the complex task is turned into a workflow of simple

tasks, and each step is outsourced to a crowd


Caveat 2: aggregation and disagreement Are all contributors “created equal”? Contributions/results on the same task are usually aggregated on

different workers (“wisdom of crowds”, “collective intelligence”) Update formula for score should weight contributions differently

by including some evaluation of contributors’ reliability (e.g. gold standard)

Is there always a “right answer”? Or is there a “crowd truth”? [14] Not always true/false, because of human subjectivity, ambiguity

and uncertainty Disagreement across contributors is not necessarily bad, but a

sign of: different opinions, interpretations, contexts, perspectives, …



Suggestion 2: compare and contrast From “wisdom of crowds” to “wisdom of the crowdsourcing methods”: different approaches to solve the same problem could be put in parallel to compare results

Which is the best crowdsourcing approachfor a specific use case?

Which is the most suitable crowd? Is crowdsourcing better/faster/cheaper

than automatic means (e.g. AI)? Examples: Detecting quality issues in DBpedia [15]: find-verify strategy

with different crowds (experts and workers) behind the find step Employing people for ontology alignment [16]: outcomes comparable to results of

OAEI systemsSSWS 2016 - Bertinoro, 22th July 2016

input task

output solutio

n

Citizen Science

Human Computation

Crowdsourcing

Automatic/machine

computation


Bibliography (1/2)


[1] James Surowiecki. The wisdom of crowds, Anchor, 2005. [2] Alan Irwin. Citizen science: A study of people, expertise and sustainable development. Psychology Press, 1995.

[3] Jeff Howe. Crowdsourcing: How the power of the crowd is driving the future of business. Random House, 2008.

[4] Edith Law and Luis von Ahn. Human computation. Synthesis Lectures on Artificial Intelligence and Machine Learning, 5(3):1–121, 2011.

[5] Jane Mc Gonigal. Reality is broken: Why games make us better and how they can change the world. Penguin, 2011.

[6] Kevin Werbach and Dan Hunter. For The Win: How Game Thinking Can Revolutionize Your Business. Wharton Digital Press, 2012.

[7] Luis Von Ahn. Games with a purpose. Computer, 39(6):92–94, 2006. [8] Luis Von Ahn and Laura Dabbish. Designing games with a purpose. Communications of the ACM, 51(8):58–67, 2008.


Bibliography (2/2)


[9] Cristina Sarasua, Elena Simperl, Natasha Noy, Abraham Bernstein, Jan Marco Leimeister. Crowdsourcing and the Semantic Web: A Research Manifesto, Human Computation 2 (1), 3-17, 2015.

[10] Irene Celino, Andrea Fiano, and Riccardo Fino. Analysis of a Cultural Heritage Game with a Purpose with an Educational Incentive, ICWE 2016 Proceedings, pp. 422-430, 2016.

[11] Maria Antonia Brovelli, Irene Celino, Monia Molinari, Vijay Charan Venkatachalam. A crowdsourcing-based game for land cover validation, ESA Living Planet Symposium 2016 Proceedings, 2016.

[12] Panos Ipeirotis. On Mice and Men: The Role of Biology in Crowdsourcing, Keynote talk at Collective Intelligence, 2012.

[13] M. Bernstein, G. Little, R. Miller, B. Hartmann, M. Ackerman, D. Karger, D. Crowell, K. Panovich. Soylent: A Word Processor with a Crowd Inside, UIST Proceedings, 2010.

[14] Lora Aroyo, Chris Welty. Truth is a Lie: 7 Myths about Human Annotation, AI Magazine 2014.

[15] Maribel Acosta, Amrapali Zaveri, Elena Simperl, Dimitris Kontokostas, Fabian Flöck, Jens Lehmann. Detecting Linked Data Quality Issues via Crowdsourcing: A DBpedia Study, Semantic Web Journal, 2016.

[16] Cristina Sarasua, Elena Simperl, Natasha Noy. Crowdmap: Crowdsourcing ontology alignment with microtasks, ISWC 2012 Proceedings, pp. 525-541, 2012.


Other relevant work (1/2)


Cultural heritage annotation: C. Wieser, F. Bry, A. Bérard and R. Lagrange. ARTigo: building an artwork

search engine with games and higher-order latent semantic analysis. In First AAAI Conference on Human Computation and Crowdsourcing, 2013.

Ranking triples for relevance: J. Hees, T. Roth-Berghofer, R. Biedert, B. Adrian and A. Dengel. BetterRelations:

using a game to rate linked data triples. In Annual Conference on Artificial Intelligence, 2011.

I. Celino, E. Della Valle and R. Gualandris. On the effectiveness of a Mobile Puzzle Game UI to Crowdsource Linked Data Management tasks. In CrowdUI workshop, 2014.

DBpedia data cleansing: J. Waitelonis, N. Ludwig, M. Knuth and H. Sack. WhoKnows? Evaluating Linked

Data Heuristics with a Quiz that cleans up DBpedia. Interactive Technology and Smart Education, 8(4), 2011.

Urban Games on spatial knowledge: I. Celino, S. Contessa, M. Corubolo, D. Dell'Aglio, E. Della Valle, S. Fumeo and T.

Krüger. Linking Smart Cities Datasets with Human Computation – the case of UrbanMatch, In ISWC 2012.

I. Celino, D. Cerizza, S. Contessa, M. Corubolo, D. Dell'Aglio, E. Della Valle and S. Fumeo. Urbanopoly – a Social and Location-based Game with a Purpose to Crowdsource your Urban Data, Proceedings of SoHuman 2012, 2012.


Other relevant work (2/2)


Crowdsourcing ontology engineering tasks or SPARQL querying: N. Noy, J. Mortensen, M. Musen and P. Alexander. Mechanical turk as an

ontology engineer?: using microtasks as a component of an ontology-engineering workflow. In WebSci 2013.

G. Wohlgenannt, M. Sabou and F. Hanika. Crowd-based ontology engineering with the uComp Protégé plugin. Semantic Web, 7(4), 2016.

M. Acosta, E. Simperl, F. Flöck and M.E. Vidal. HARE: A Hybrid SPARQL Engine to Enhance Query Answers via Crowdsourcing. K-CAP 2015.

Crowdsourced contributions and crowd quality: R. Kern, H. Thies, C. Zirpins and G. Satzger,. Dynamic and Goal-Based Quality

Management for Human-Based Electronic Services. Int. J. Cooperative Inf. Syst. 21, 1, 2012.

T. Schulze, S. Krug and M. Schader. Workers’ Task Choice in Crowdsourcing and Human Computation Markets. In ICIS Association for Information Systems, 2012.

D. Difallah, G. Demartini and P. Cudré-Mauroux. Pick-a-crowd: tell me what you like, and I'll tell you what to do. In WWW 2013.

M. Feldman and A. Bernstein. Cognition-based Task Routing: Towards Highly-Effective Task-Assignments in Crowdsourcing Settings. In ICIS 2014.

Mixed crowdsourcing approaches: M. S. Bernstein. Crowd-Powered Systems. KI 27, 1, 69–73, 2013.


Cognition for the Semantic WebResearch questionsIrene Celino + Tomi Kauppinen



Warning on group forming Today is the last day you form groups to investigate research questions

We suggest you to take the chance to work with people you haven’t grouped together yet… diversity is a resource!

Mini-project groups are not allowed!



Before getting too serious, let’s seriously play… You will be asked to present your research task results in 3 minutes (which is tough!)

We want to give a chance to win 2 additional minutes to one of the groups… To whom? To the group of the winner of this morning tutorial’s quiz game!!

1. Grab your mobile phone (or tablet or laptop) 2. Be sure you are connected to the internet 3. And finally go to:

KAHOOT.IT SSWS 2016 - Bertinoro, 22th July 2016


Research Question Why and how to make sense of linked spatio-temporal data? Can we make machines to be cognitive systems? What is the role of human cognitive capabilities? How can Linked Data / Semantic Web technologies help?

Possible approaches: crowdsourcing, information visualization, storytelling, gamification, sensor data fusion, affordances, …

Possible starting point paper: K. Janowicz, The role of space and time for knowledge organization on the Semantic Web. Semantic Web 1.1, 2 (2010): 25-32.



What you have to do Task: devise your own approach Provide a convincing future scenario (i.e. design fiction) as an

example Use your creativity and freedom! Come up with a proposal including motivation (why), approach

(how) and expected results (what); bonus point if you add ideas about evaluation

Put your group results in the shared Etherpad (no PowerPoint!): http://public.etherpad-mozilla.org/p/cognition-day


http://public.etherpad-mozilla.org/p/cognition-day

Tutorial Cognition - Irene

Technology

Transcript of Tutorial Cognition - Irene