02 ai-one - content analytics business cases

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use cases of ai-one content analysis in text and language

use cases of ai-one content analysis in text and language

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  • 1. Business cases for contentanalytics with ai-onebiologically inspired intelligence© ai-oneinc. 2012 ai-one™
  • 2. Biologically Inspired Intelligence logic creativity© ai-oneinc. 2012
  • 3. The secret of content analytics… with only a few ai-one commands it is easyto build and use semantics in language !Successionaly we show some sampleconcepts how to use the ai-one approach(commands) in daily business cases.© ai-oneinc. 2012
  • 4. Make sense of a texti.e. in: E-Mail, Article, Feed, Tweet, Story etc…Recognize the sense and meaningwithin a text corpus means to identifythe semantically most importantwords which build the content Use of the : KeyWordCommand© ai-oneinc. 2012
  • 5. Make sense of a texti.e. in: E-Mail, Article, Feed, Tweet, Story etc…Recognize the sense and meaning within a text corpus means to identifythe semantically most important words which build the contentExtract the most important words in a textcorpus and form the Light WeightOntology (LWO) and from a digest of themeaning. That is the condensed summaryof the sense and meaning of a text.Now ai-one or other matcher can classifyand sort the text. This is the ai-Fingerprint. With the for example 7 words this text is define in its sense Use of the : KeyWordCommand© ai-oneinc. 2012
  • 6. Find associative, semantic relationsi.e. in: E-Mail, Article, Feed, Tweet, Story etc…Its like brainstorming, what has to-do with what, or which word issemantically connected with which word. Find the associative andsemantic relations trough a whole big text or whole data base. Findpatterns we did not know they exist! WORDThis commands starts with one or multiple words WORDand searches for the semantically relations. WORD Find patterns of relations in whole text corpus. WORD WORD Validate the importance of a connection WORD WORD between two or multiple wordsDetect association bridges between two words. Display of semantic chains.! WORD WORD WORD Use of the : AssoAnalysCommand© ai-oneinc. 2012
  • 7. Find syntax patternsi.e. E-Mail, Article, Feed, Tweet, Story etc…One additional challenge is the spelling. Users very often miss spellwords. Therefore we also search for syntax patterns in order to verifythe words. The Phonetic pattern recognition on syntax is very helpfully to identify similar • Maier • Meyer words, spell errors and artificially re- • Mair • Peyer designed words. • Paier • Peier • Meier • … Find word pattern, where also the first character may be wrong! Use of the : PhoneticCommand© ai-oneinc. 2012
  • 8. Query chains, combinationsIn certain cases it may help to chain the differentcommands into a small workflow.Depending the project, its best KeyWordCommandto combine the ai-onecommands. In the beginning on ResultSet 1has to switch commandsstructures and conventional Check-Asso Check-Phoneticthinking, but then programmersare in our world very fast. ResultSet / Edit Match/Classify ResultSet 2 Use of the : combine the commands© ai-oneinc. 2012
  • 9. Summary: just a few commandsKeyWordCommandAssoAnalyseCommantPhoneticCommandLearn & Tighten CommandsFocus & others…… explain the entire semantic world!© ai-oneinc. 2012
  • 10. ai-one gives Better results!current linguistics and semantic solutionsworks only if they are feed with accurate anddetailed language dependent models, and thereis NO incrementally updating/learning possible!ai-one solved this challenge, ai-one’s approachworks incrementally, shows the inherent(intrinsic) semantic in any language without preprogramming or compelling use of ontologiesand thesauri. ai-one© ai-one inc. 2011inc. 2010
  • 11. Intelligent Language HandlingLWO: Dynamic and self detection ontologies Prof. Dr. habil. Ulrich Reimer, University of Applied Sciences St. Gallen, "Learning a Lightweight Ontology for Semantic Retrieval in Patient-Center Information Systems". One direct benefit and resulting application, explained also in the paper of Prof Dr. habil Ulrich Reimer is, a trend barometer that uses the ai-one core technology to observes and analyze for example the Internet (news platforms, online news, RSS feeds, blogs etc.) The trend barometer finds in context and topics discussed the current keywords, that is the semantic trends, and builds a dynamic ontology on a daily basis. Similarly, ai- one can be applied as trend barometer or analysis tool on documents or databases. This opens up the possibility to compare documents, even databases, as regards content. The number of possible applications are almost infinite. ai-one© ai-one inc. 2011inc. 2010
  • 12. Intrinsic semantic (LWO) vs.: Full-fledged ontologies [Supervised learning] - Works only with detailed models - Language dependent, - no incrementally updating Sharing / reuse of ontologies [limited possibilities] - Based on models and reservations about the quality - Language dependent - no incrementally updating Folksonomies [WEB 2.0 / semantic WEB] - No controlled quality or validation - Often incomplete or not existent, Language dependent - no incrementally updating ai-one© ai-one inc. 2011inc. 2010
  • 13. ai-one is language independent prolitterisdorerchristianvaldaandreasblick20020129seite5nummer23swissairboss12mi I0I00I0II0I0III0I0I00II0II0I00II0I0II0I000II0I0II0I00II0I00II0I0II0I000I0II0I ofür5jahreimvorauskassiertderdruckwächstcortiindeckungvonchristiandorerundandre 0II0I0I0II0II0I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I000II00II0II0I00II0I0I0I0 asvaldazürichesistvölligunüblichdassderlohnfürfünfjahreimvorausbezahltwirdsagenre nommierteheadhunterfdppräsidentgeroldbührer53verlangtjetztschonungslostranspare 0III000I0I0III0II00I0II00II0I0I00II0I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I000 nznochimmersagenmariocortiundseinefreundeausdemaltenverwaltungsratnocommen II00II0II0I00II0I0I0I00III000I0I0III0II00I0I0I0I0II0I00I0I0I0I0I00I0IIII0I0I0 tcortiweiltegesterninpolenundwolltenochimmernichtsagenoberaufeinenteilseines5jahr I0II00I0II0I000II00II0II0I00II0I0I0I00III000I0I0III0II00II0I0I00II0I00I0I0I0 eslohnesverzichtenwirdinschweigenhüllensichauchdieehemaligenverwaltungsrätediei I0I00I0IIII0I0I0I0II00I0II0I000II00II0II0I00II0I0I0I00III000I0I0III0II00I0II0 mmärz2001denvergoldetencortivertragaufgesetzthattenindersalärkommissionsassda malszementkönigthomasschmidheiny56herrschmidheinymöchtesichnichtäussernsola I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I000II00II0II0I00II0I0I0I00III000I0I0III ngedieuntersuchungdessachwaltersläuftlässterausrichtengleichtöntesbeimzweitenko 0II00I0II0I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I000II00II0II0I00II0I0I0I00III mmissionsmitgliedgaudenzstaehelinichhabeverständnisfürdasinteresseaberesliegtan 000I0I0III0II00I0II0I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I000II00II0II0I00II0 derswissairzuinformierennichtanmirauchdieübrigenmitgliedervonvrenispoerrybislukas I0I0I00III000I0I0III0II00I0II0I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I000II00II mühlemannverkriechensichdabeiwarenauchsiebestensimbildsolcheverträgewurdenim merimplenumbesprochensagteinehemaligesvrmitglieddochcreditsuissechefmühlema 0II0I00II0I0I0I00III000I0I0III0II00I0II0I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I nn51bequemtsichgenausowenigzueineroffeneninformationwiebankierbénédicthentsc 000II00II0II0I00II0I0I0I00III000I0I0III0II00I0II0I00I0I0I0I0I00I0IIII0I0I0I0 h53amtelefonsagterentnervtichhabedasrechtnichtszusagenwarumwollensienichtssag II00I0II0I000II00II0II0I00II0I0I0I00III000I0I0III0II00I0II0I00I0I0I0I0I00I0I The CORE works on binary level enherrhentschfragensienichtweiterichgebekeinenkommentarschönentagaufwiederse henaufgehängtjetztregtsichpolitischerwiderstandwennderbetragohneauflagenüberziel III0I0I0I0II00I0II0I000II00II0II0I00II0I0I0I00III000I0I0III0II00I0II0I00I0I0I eausbezahltwurdedannistdasinakzeptabelsagtfdppräsidentgeroldbührerüberfdpmitgli 0I0I00I0IIII0I0I0I0II00I0II0I000II00II0II0I00II0I0I0I00III000I0I0III0II00I0II edcortijetztbrauchtesschnelltransparenzdenkopfschütteltauchcvppräsidentphilippstäh 0I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I000II00II0II0I00II0I0I0I00III000I0I0II elin57ichhabemühemiteinemsohohenlohndasführtzuriesigeneinkommensunterschied I0II00I0II0I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I000II00II0II0I00II0I0I0I00III enimvolkdasdarfnichtseinerhaltezwarvielvonleistungslohndochdenkannmannichtimvor 000I0I0III0II00I0II0I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I000II00II0II0I00II0 ausbekommenvorauszahlungensindunüblichsagtheadhuntersandrovgianellaundfredy islervonspencerstuartichhabenochnievoneinemsolchenfallgehörtaberniemandwollted I0I0I00III000I0I0III0II00I0II0I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I000II00II enswissairjobdiesesversprechenwarwohleinlockvogeldamitcortiseinensicherennestlép 0II0I00II0I0I0I00III000I0I0III0II00I0II0I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I ostenaufgabmariocortilukasmühlemannthomasschmidheinybénédicthentschkonzerns 000II00II0II0I00II0I0I0I00III000I0I0III0II00I0II0I00I0I0I0I0I00I0IIII0I0I0I0 anierermussvorgerichtzürichdervorkassevertragcortisistkeinepremiereimfallderkonkur II00I0II0I000II00II0II0I00II0I0I0I00III000I0I0III0II00I0II0I00I0I0I0I0I00I0I sitenbiberholdingliesssichkonzernsaniererchristianspeiserbildseinenjobmit28millionen frankengarantielohnvergoldenermusssichjetztvorgerichtverantwortenwiecortikassierte III0I0I0I0II00I0II0I000II00II0II0I00II0I0I0I00III000I0I0III0II00I0II0I00I0I0I auchspeiserfrühzeitigdiegesamtesalärpauschaleerliesssichdiemillioneneinjahrvorderf 0I0I00I0IIII0I0I0I0II00I0II0I000II00II0II0I00II0I0I0I00III000I0I0III0II00I0II älligkeitauszahlendermanagerwurdeverdächtigtamzahltagbereitsvomdrohendenkonk 0I00I0I0I0I0I00I0IIII0I0I0I0II00I0II0I000II00II0II0I00II0l0l0l00l0l0l00l0l0l ursgewusstzuhabenspeisermussteseineraffgierallerdingsteuerbezahleneinewocheuh 0l0l0l0lll0l000ll0l00l0l0ll0l00l0ll0l000ll0l0l0l0l0ll000l0l0l0l0ll0l0l0l0l0l0l0l afteinestrafklagevormzürcherbezirksgerichteinezivilklagedurchdensachwalter200000f rankenmussteerzurückzahlenseinekarrierewurdejähbeendetimhe 00l0ll0l0l0l00l0l0lll0l0l0l0l0l0ll0l0l0lll0lll0l0l0l0l00l0ll0l0l0l0l00ll0l0ll0ll0l ai-one© ai-one inc. 2011inc. 2010
  • 14. ai-one plus NLP for perfect results Combine ai-one with NLP and ontology for best possible output conditioning. Categorization Find connections (NLP) (LWO) Sense, • Autonomous display of • Categorize content Meaning any kind of data based on rules Decisions • Unstructured approach • Structured approach • Recognition of all • Trained; Manually connections between updated and developed words Better decisions because ai-one! ai-one© ai-one inc. 2011inc. 2010
  • 15. A few ai-one commands solveand support:• Sentiment analyses• Social media analyses• Trend studies• Automatic classifying• Autonomic sense making• Detect unknown patterns• Answers unknown questions• Autonomic decision making .. and much more© ai-oneinc. 2012
  • 16. Only a few commands are need to:Explore and then Explain the world oflanguage with basically two maincommands and a few complementarycommands:…that’s the ai-one LIB & API!© ai-oneinc. 2012
  • 17. Thank You!ai-one inc. ai-one ag ai-one gmbh5711 La Jolla Blvd., Flughofstrasse 55, Koenigsallee 35a,Bird Rock Zürich-Kloten GrunewaldLa Jolla, CA 92037 8152 Glattbrugg 14193 Berlininfo@ai-one.comwww.ai-one.com© ai-oneinc. 2012