Second International Workshop on New Generation Enterprise and Business Innovation NGEBIS 2013

1. Second International Workshop on New Generation Enterprise and Business InnovationNGEBIS 2013 Cross Domain CrawlingforInnovation PieruigiAssogna, Francesco Taglino CNR-IASI (Italy)

2. Outline • Motivations & Objectives • Methodologicalapproach • Technologicalapproach • Conclusions

3. Motivations and Objectives • In any kind of organization, creativity and innovation come from people • Tools aiming at supporting creativity need to be based on the most accredited theories related to how people use their knowledge to act on the environment, adapt to new situations, invent. The method proposed here aims at providing knowledge “raw material”, capable of triggering out-of-the-box ideas

4. Constructivism • According to Constructivism a person’s culture is an integrated network of concepts and models • This guides the person’s activity, and is consolidated, enriched, modified by each new experience • Apart from pathological situations (schizophrenia) each person’s structure is anyway connected

5. New Paths • The connections between concepts create paths that, with time, our mind travels more or less automatically • In new situations we have to “take the lead” and try new paths, possibly linking different and distant clusters • This is for instance what is favored by “lateral thinking” methods

6. Knowledge Base • In general a domain Knowledge Base (KB) is a tool for maintaining and enriching its users’ focused knowledge • In particular the KB’s ontology mimics their focused conceptual structure • When the users are confronted by new issues, a search on the KB or on the Net (on the base of the domain ontology) typically keeps them within this focused ground

7. The Methodology • We propose a way to extend a focused knowledge domain to support diversions from usual thinking paths • We use the domain ontology to search the Net for documents that address key topics of the domain together with topics belonging to different ones • These documents have good probability of containing considerations, theories, metaphors that link the person’s knowledge clusters with “exotic” ones, able to trigger ideas out-of-the-box

8. Semantics-based cross-domains crawling

9. Documental Resources Space where we search for interesting documents • websites (e.g., MIT website on innovations), RSS feeds, and public documents repositories (e.g., BBC news) • In our example we focus on Robotics and Machine Vision (R&MV) domain

10. Linked Data A set ofprinciplestoallow • Standard descriptionof data (RDF-based) • Standard way of accessing data (HTTP) • Linkingresources/data amongthem • Linking Open Data as a project forpublishingdatasets (e.g., Dbpedia) in a Linked Data fashion

11. The Linking Open Data cloud DBpedia

12. Referenceontology and bridgeto the LOD cloud • Within the BIVEE project wehavebuilt a glossaryof 600 concepts on R&MV • WeenrichedsuchconceptswithDBpediaentries (owl:sameAs) R&MVreference ontology DBpedia owl:sameAs Photodiode http://dbpedia.org/page/Photodiode Photodiodes owl:sameAs Camera http://dbpedia.org/page/Camera Camera

13. Termsextractionfromanalyzeddocument • Extracted terms/concepts are representative and somehow synthesize the document’s content • We analyzed different tools for extracting knowledge from documents • Zemanta, Alchemy, OpenCalais, FISE • AlchemyAPI: extract concepts from a text • relevance value • link to DBpedia and other LOD dataset

14. SemanticFilterover a doc Twosteps • Identify the extractedconceptsrelatedtoour domain of interest • Identifygood candidate and discardingnotinterestingdocuments

15. SemanticFilterover a doc: step 1 Identify the extractedconceptsrelatedtoour domain of interest (e.g., R&MV) • Givenanextractedconceptec, itexists at leastonereferenceconceptrc, suchthat r1 = r2 ReferenceOntology Concept(rc) (r2 = ref. toDbpedia entry) Extracted Concept(ec) (r1 = ref. toDbpedia entry) OR (r1dc:subject) r AND (r2dc:subjectr) where r is a resources

16. SemanticFilterover a doc: step 2 Let be S1 the set of extracted concepts related to our domain Let be S2 the set of extracted concepts NOT related to our domain A document is a good candidate if (a) t1<Sum(relVal(S1))<t2 AND t1=0.1, t2=0.4 (b) Sum(relVal(S2))>t3 t3=0.4 • (a) ensures that the analyzed document deals with our reference domain, but in a small manner, • (b) second constraint ensures that the analyzed document deals with other topics in a considerable measure.

17. Filtering: example 1 SUGGESTED AS INTERESTING Extracted Concepts and Relevance The document is about extracting energy from insects

18. Filtering: example 2 SUGGESTED AS INTERESTING Extracted Concepts and Relevance The document is about supporting shoppers get the right fit when buying clothes online

19. Filtering: example 3 NOT INTERESTING document Extracted Concepts and Relevance The document does not consider Robotics and Machine Vision at all

20. Filtering: example 4 NOT INTERESTING document Extracted Concepts and Relevance The document is too much Robotics oriented, so it can be surely useful for experts in the Robotics field, but it does not appear inspiring for lateral thinking

21. Conclusions and Outlook • Verypreliminary work on supportinglateralthinkingactivities • More experimentation • Using the LOD cloudasmuchaspossible

22. Questions&Answers