ai-fingerprints: finding the intrinsic patterns in language
DESCRIPTION
How to generate a knowledge representation model for unstructured text.TRANSCRIPT
© ai-one inc. 2012
biologically inspired intelligence
ai-one™© ai-one inc. 2012
ai-Fingerprints: Finding theintrinsic patterns in language
© ai-one inc. 2012
Biologically Inspired Intelligence
creativitylogic
© ai-one inc. 2012
© ai-one inc. 2012
Detects the meaning of a document by identifying the most semantically important words in the text.
Automatically generates a graph representation of knowledge.
Think: Feature detection of vertices and edges (keywords and associations) in any document, in any language.
Works without human intervention or models.
Make sense of any* text
© ai-one inc. 2012
* as long as there is sense in the unstructured sentences…
© ai-one inc. 2012
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ai-one
© ai-one inc. 2012
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© ai-one inc. 2012
ANY Language!
© ai-one inc. 2012
ANY Language!
© ai-one inc. 2012
prolitterisdorerchristianvaldaandreasblick20020129seite5nummer23swissairboss12miofür5jahreimvorauskassiertderdruckwächstcortiindeckungvonchristiandorerundandreasvaldazürichesistvölligunüblichdassderlohnfürfünfjahreimvorausbezahltwirdsagenrenommierteheadhunterfdppräsidentgeroldbührer53verlangtjetztschonungslostransparenznochimmersagenmariocortiundseinefreundeausdemaltenverwaltungsratnocommentcortiweiltegesterninpolenundwolltenochimmernichtsagenoberaufeinenteilseines5jahreslohnesverzichtenwirdinschweigenhüllensichauchdieehemaligenverwaltungsrätedieimmärz2001denvergoldetencortivertragaufgesetzthattenindersalärkommissionsassdamalszementkönigthomasschmidheiny56herrschmidheinymöchtesichnichtäussernsolangedieuntersuchungdessachwaltersläuftlässterausrichtengleichtöntesbeimzweitenkommissionsmitgliedgaudenzstaehelinichhabeverständnisfürdasinteresseaberesliegtanderswissairzuinformierennichtanmirauchdieübrigenmitgliedervonvrenispoerrybislukasmühlemannverkriechensichdabeiwarenauchsiebestensimbildsolcheverträgewurdenimmerimplenumbesprochensagteinehemaligesvrmitglieddochcreditsuissechefmühlemann51bequemtsichgenausowenigzueineroffeneninformationwiebankierbénédicthentsch53amtelefonsagterentnervtichhabedasrechtnichtszusagenwarumwollensienichtssagenherrhentschfragensienichtweiterichgebekeinenkommentarschönentagaufwiedersehenaufgehängtjetztregtsichpolitischerwiderstandwennderbetragohneauflagenüberzieleausbezahltwurdedannistdasinakzeptabelsagtfdppräsidentgeroldbührerüberfdpmitgliedcortijetztbrauchtesschnelltransparenzdenkopfschütteltauchcvppräsidentphilippstähelin57ichhabemühemiteinemsohohenlohndasführtzuriesigeneinkommensunterschiedenimvolkdasdarfnichtseinerhaltezwarvielvonleistungslohndochdenkannmannichtimvorausbekommenvorauszahlungensindunüblichsagtheadhuntersandrovgianellaundfredyislervonspencerstuartichhabenochnievoneinemsolchenfallgehörtaberniemandwolltedenswissairjobdiesesversprechenwarwohleinlockvogeldamitcortiseinensicherennestlépostenaufgabmariocortilukasmühlemannthomasschmidheinybénédicthentschkonzernsanierermussvorgerichtzürichdervorkassevertragcortisistkeinepremiereimfallderkonkursitenbiberholdingliesssichkonzernsaniererchristianspeiserbildseinenjobmit28millionenfrankengarantielohnvergoldenermusssichjetztvorgerichtverantwortenwiecorti
ANY Language!
© ai-one inc. 2012
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ANY Language!
© ai-one inc. 2012
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ANY Language!
© ai-one inc. 2012
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Light Weight Ontology (LWO)!
© ai-one inc. 2012© ai-one inc. 2010
obesity
lifestyle
nutrition
physical exercise
stress
smoking
0.9
0.90.60.75
0.9
Our technology detects the strength of all associations between all words (n:n).
The LWO captures the intrinsic semantic value of every word, sentence, etc.
ai-one is self-learning, self-organizing and incrementally self-updating.
Lightweight Ontology (LWO)Association Strength
© ai-one inc. 2012
Sender
Subjective interpretation & understanding
subjective meaning and semantic
Every human interprets semantics differently because of experience, intellectual level, cognitive biases, etc.
Be careful: The semantic trap!
Receiver
ai-one has the neutral observer position to detect the inherent meaning
ai-one shows the LWOregardless if right or wrong
© ai-one inc. 2012
From LWO to one ai-fingerprintConvert the LWO-pattern in a simple visualization with graphs that show, store & match fingerprints
© ai-one inc. 2012
• jobs -> c.v.• c.v. -> jobs
c.v.
job
job
job
c.v.
c.v.
job
job
• data in forms -> free text• free text -> forms
• Dynamic• Intelligent
• similar text • similar meaning
Use case: ai-fingerprint to match job posting with resumes for HR
© ai-one inc. 2012
The fingerprint is defined by the the most important words of a text corpus – called keywords. Keywords words are classified in importance according to a value determined by ai-one.
Keywords can be used to search with Yahoo, Google, Wolfram Alpha, Bing and any other search approach. Since the keywords are the optimal search words, users will get the best answer by traditional search engine.
Spicing with the ai-fingerprint semantic for better results.
ai-fingerprint as bridge to traditional approaches
© ai-one inc. 2012
We solve the problem of identifying multiple themes in a single document. Very often, there are multiple items, issues or themes on the same page.
For example in a news magazine, there are short article about different subjects on the same page. This is very challenging for traditional semantic text analysis processes.
ai-one’s LWO automatically segments topics into related clusters.
ai-fingerprint solves an other problem in traditional text analytics
© ai-one inc. 2012© ai-one inc. 2010
ai-Fingerprint Typical ai-fingerprint of a text corpus very focused on one issue/theme.
© ai-one inc. 2012© ai-one inc. 2010
ai-Fingerprint Typical ai-fingerprint of a text corpus where tree different issues/themes are on the same page
11
22
3
3
© ai-one inc. 2012© ai-one inc. 2010
ai-Fingerprint Typical ai-fingerprint of a “nonsense”-text corpus where no real sentences are giving an story, randomly sentences
© ai-one inc. 2012© ai-one inc. 2010
ai-Fingerprint Enables better decision making by providing a way to compare semantic content by using graphs.
© ai-one inc. 2012
… much better than any traditionally method!
Current linguistics and semantic solutions work only if they are feed accurate and detailed language models.
These models are expensive and specific to a single dialect.
Worse, they do NOT incrementally update or learn!
© ai-one inc. 2010
…ai-one –
© ai-one inc. 2012
… recognizing the content… understanding the meaning and
generalizing its application… deciding about its importance… knowing what to do with this
learned information
© ai-one inc. 2010
…ai-one – the next evolution in software… intelligent agents?
© ai-one inc. 2012
Thank You!
© ai-one inc. 2012
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