Semantics and Web 3.0
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Semantics and Web 3.0

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This presentation provides a top down introduction to semantics and Web 3.0 ...

This presentation provides a top down introduction to semantics and Web 3.0
It is intended for the busy executive or developer who want to understand quickly why this new technological wave is relevant
For a “one slide presentation” see the first slide only
For a general introduction, see only the slides of the first section
Following slides about semantic technologies, architectures and applications

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  • Note A) aggiungere una slide di indice, distinguendo queste parto - 1) overview (qualche dettaglio sulle basi dati di conoscenza può essere spostato?)‏ - 2) overview tecnica (aggiungendo una slide che evidenzia che i tre assi possono essere usati anche per classificare una soluzione e che le combinazioni degli assi 2 a 2 forniscono delle informazioni interessanti: quali?)‏ - 3) tecnologia (da fare)‏ - 4) architettura (da rivere)‏ - 5) applicazioni (da integrare)‏ - 6) conclusioni e bilbliografia (da aggiungere, prendendo lo spunto dalle faq)‏ In generale, alcuni link internet possono essere omessi e aggiunti in nota. In definitiva questa rappresentazione può essere decomposta su due livelli: a) contenuto delle slides, da pubblicare (non dimentare la ultima slide, con i diritti di distribuzione di tipo creative commons b) contenuto delle slides con note estese, a circolazione interna o comunque controllata). In totale non superare le 50 slides B) includere la sezione relativa alle tecnologie, evidenziando a) gli aspetti evolutivi (maturazione degli standard bottom-upù articolazione differente degli standard rispetto all’ inizio) b) standard complementari e non rappresentrati c) tecnologie complementari e non rappresentate. Inoltre, una brevissima nota per ogni standard (se una slide può bastare per 3 standard, non più di 3 slides). In totale, per questa parte, non più di 8 slides C) nella sezione architettura evidenziare la tripartizione tool per basi dati di conoscenza, basi dari di conoscenza, piattaforma, con decomposizione in tre strari. Nelle basi dati di conoscenza, evidenzuare la disponibilità di basi dati in rete, anche federabil

Semantics and Web 3.0 Semantics and Web 3.0 Presentation Transcript

  • Semantics and Web 3.0 Alberto Ciaramella Intellisemantic http://www.intellisemantic.com may 2009
  • This presentation
    • This presentation provides a top down introduction to semantics and Web 3.0
    • It is intended for the busy executive or developer who want to understand quicly why this new technological wave is relevant
      • For a “one slide presentation” see the first slide only
      • For a general introduction, see only the slides of the first section
  • A overview Semantics in a slide Web 1.0, Web 2.0, Web 3.0 Architectures and Applications Knowledge Representations
  • Semantics in a slide
    • semantics adds meanings to the data, in such a way that they can be understood not only to people, but also by machine, which this way can better support people, hence the slogan:
      • “ let’s the machine do the most of the work”
    • to achieve that, semantic solutions integrates knowledge data bases to the usual information architectures, simulating some kind of intelligence, hence the slogan
      • “ a semantic application is a application with a common sense ”
    • even the simplest semantic extensions provide a well perceivable benefit to the final user, hence the slogan:
      • “ a little semantics goes a long way”
  • Short history
    • the “semantic web” vision was conceived since ’90s
    • The Scientific American paper in may 2001 by T. Berners-Lee, J. Hendler and O. Lassila disseminated the idea to a larger public, although the visions of this paper are in most part yet to achieve
    • W3C in these years carried out a significant standardization activity, so it is possible now to rely on a solid and agreed background
    • The first adoption cases were in enterprise applications
      • they are deployed in more controlled environments
      • the general public could not be fully aware of them
      • this is the most mature area today
    • Some interesting cases are now beginning to appear on the web, mostly as betas, and they are also named “Web 3.0”
      • This term suggests that semantic applications are more feature rich than web 2.0 solutions, whilst it obfuscates technical reasons why
  • Web 1.0, Web 2.0 and Web 3.0 (1)‏
    • Web 1.0 is the web as originated in ’90s, i.e. a infrastructure providing a unprecedent level of access to documents, of course suitably exposed to the network by information professionals
    • Web 2.0 is a collaborative web; Web 2.0 solutions facilitate the user in submitting new contents, through blogs and wikis, and to interact, through social networks; in summary Web 2.0 is a read-write web, whilst Web 1.0 is a read web
    • Web 2.0 applications appeared in ’00s, i.e. on the second decade of the web, hence the name
    • The counterpart of Web 2.0 for the enterprise is the Enterprise 2.0
  • Web 1.0, Web 2.0 and Web 3.0 (2)‏
    • Web 2.0 applications produce a increasing amount of documents and connections to deal with, and semantics as a technology can help in dealing more intelligently and efficiently with these issues
    • this new wave of connected and intelligent applications was recently named Web 3.0, i.e. a read-write-execute web
      • Remind the slogan “let’s the machine do the most of the work”
    • The technology behind this evolution is semantics, which started as a research dream, but seems to become now more pervasive
    • An interesting example of evolution from Web 2.0 to Web 3.0 is Wikipedia, which is developed in a Web 2.0 cooperarive environment and which is now available as data within the Linked Open Data (LOD) Web 3.0 initiative
  • Semantic solutions
    • A semantic architecture is a usual Web or enterprise architecture augmented with a knowledge representation.
    • Knowledge representations are a key point in semantic architectures and solutions
    • Knowledge representations (e.g. vocabularies, taxonomies) have a long tradition, which predates IT times
    • Semantics today formalized and generalized general concepts of knowledge representations, provided tools for improving development and standards for easing their integration into specific applications
  • Knowledge representations (1)‏
    • Knowledge representations can be represented in graphic form: nodes represent concepts or entities, and arcs represents relationships between nodes
    • They differ in the generality of the graphical representation and in the kind of relationships allowed , as detailed in the following
    Any Network Ontology Is-a, related-to Network Thesaurus Is-a Graph Taxonomy No Discommected dots Controlled vocabulary Relationships allowed Graphical representation Kind of knowledge representation
  • Knowledge representations (2)‏
    • A knowledge representation can be characterized by:
    • Kind of, as summarized in the previous slide, from controlled vocabularies to ontologies
    • its minor or major complexity (number of nodes, number of archs, number of allowed relationship)‏
    • Use the simplest representation for the domain to be represented and for the problem to be solved!
    • To make some examples:
    • a taxonomy representation is generally enough for Information Retrieval, although it is needed to verify if the knowledge granularity is adeguate for the application
    • a ontology representation is generally required for reasoning; in this case it is needed to verify if relationships defined are adequate
  • Knowledge representations (3)‏
    • Moreover a knowledge representation is characterized by:
    • its level of generalisation/detail, from upper ontologies, describing entities like “abstract”, “concrete”, to topic specific taxonomies, e.g. for scientific terms
      • The same application can integrate different kind of ontologies
    • the kind of entities it describe: these entities can be concepts, but can be also lexical terms (WordNet is a significant example of lexical knowledge representation)
      • A lexical based ontology is more difficult to generalize
    • The knowledge representation language: RDF and OWL are specifically designed for the semantic web, but knowledge representations developped independently from the semantic web have to be suitably converted
  • Semantic applications
    • Semantic applications are very diversified
    • Most of them are deployed in the enterprise: they are technically “simpler” and easier to justify as far as their costs are concerned
    • Large scale semantic web applications are tecnically more challenging and imply also the development of new business models and minds, but more recently:
      • The “open data” paradigm is becoming more and more popular
      • Semantics is becoming a “new entry” in web marketing
    • Semantic applications can be classified according to the problem solved and to the kind of the semantic technology used
    • we have to remind that “a little semantics goes a long way” since also the less complex semantic applications provide interesting advantages in comparison to the previous status of the art
  • “ A little semantics goes a long way”
    • “ A little semantics goes a long way” for these reasons:
      • It adds some meanings to your data
      • It adds a layer of relationships to your data and documents
      • It facilitate the reuse of third party knowledge in your application
    • “ A little semantics goes a long way” means also that:
      • Many useful semantic applications use semantics only for a specific semantic tecnology
      • Almost all semantic applications relay also on other technologies in order to provide the whole solution, e.g. natural language processing
  • Examples of Semantic applications (1)
    • Unstructured data access and integration:
      • classify documents by most significant topics described in a knowledge base
      • extract metadata from documents and web pages in order to simplify their search in the following
        • and this evolution can also spark a new “semantic wave” in on line advertisements
      • extract facts and relationships in documents, in order to produce structured data
  • Semantic applications (2)
    • Structured data access and integration:
      • For providing more agility to the schema evolutions
      • For simplifying the integration of different data bases
      • For inferring new information from the existing one,g including reasoning
    • Structured and unstructured data
      • For unifying their access, e.g. in Business Intelligence
    • Social and interest network
      • For aggregating more intelligently people and interests
    • Application integration:
      • for supporting the dynamic location of web services
      • for supporting the dynamic construction of business processes
  • Semantics and other IT disciplines
    • Semantics is a evolution of different IT disciplines, bringing new paradigms for providing agility, scalability and robustness; between these we can mention:
      • For data bases: semantics follows a paradigm different from the usual RDB, i.e. it implements a graph data base in order to provide better agility
      • For artificial intelligence: semantics follows a new paradigm, i-e. the open word assumption in order to provide scalability and robustness
      • For application integration: semantics extends the first generation SOA in order to provide better agility
    • Semantics allows also to combine different kind of approaches till now separate, as for example searching both in structured and in unstructured sources
  • The dark site of semantics
    • Do not overlook “the dark side of semantics”!
      • This expression means that the problem could be not so complicated, but the sourrounding environment could add unexpected implementation difficulties
    • Semantics in fact is not a magic bullet to address garbage data, inconsistent knowledge bases, although it can help in discovering these issues
    • Semantic applications have also to cope with IT ordinary problems like different document formats and so, and have to solve them efficiently before adding semantics
    • Solutions to these “interface” problems in many cases imply the use of other IT technologies like entity extraction, parsification and so
  • Some final remarks
    • Web 2.0 solutions do not replace Web 1.0; in any case they constitute a new opportunity and the faster growth area; it is likely that in next years the same will happen between Web 3.0 and Web 2.0 solutions
    • Semantics is a familly of technologies, Web 3.0 is a family of applications
    • Semantics predates Web 3.0, present achievements in semantics (i.e. “some semantics”) are the most significant technology enablers for Web 3.0
    • Semantic technologies have been deployed first in the enterprise; now the web time is coming
    • Semantics technologies will became pervasive in IT, and a IT manager or professional has to be aware of the different technology choices and business opportunities
  • A technical overview The dimensions of semantics The technical facet The architectural facet The application facet
  • The dimensions of semantics
    • To set a framework, we distinguish three indepent dimensions:
    • technology
    • application
    • architecture
    • Any of those dimensions includes differerent aspects
    tecnology application architecture Intellisemantic
  • Technology
    • different layers of standards
      • those on the bottom are well established, other are emerging or planned
    • any level of standard can be correlated to applications of increasing complexity
      • Information retrieval
      • Information extraction
      • Question answering
      • Reasoning
      • Web of trust
    Intellisemantic
  • Architecture
    • A semantic architecture includes a knowledge data base.
    • Semantic architectures hence are characterized by:
    • What kind of knowledge data base (KDB) is used
    • where is this KDB used, i.e. in the presentation, in the business logic, in the service integration layer or in the data base layer?
    Presentation Business Logic Data base Knowledge data base Knowledge development tools Service integration
  • The application facets
    • The environment facet: semantics is splitting between
      • semantic web
      • semantic enterprise (intranet, extranet, internet)‏
    • The application goal facet:
      • for better use of unstructured information
      • for structured data integration
      • for application integration
    • The industry facet: in different industries
      • different reasons for adopting semantics
      • different levels of awareness
      • different adoption cases
    Intellisemantic
  • Technologies and recommendatioms Overview Main recommendations
  • Overview
    • W3C set initially a general framework composed by a ordered set of layers, suitable for semantic tasks of increasing complexity, from describing facts to reasoning about these facts, till verifying their thrust
    • W3C carried out its activites of course starting form the bottom layer. Till now the W3C activities:
      • produced significant recommendations for lower layers and this is a significant achievement for semantics, which relies on solid grounds
      • added some more flexibility to the original one, since it was found that
        • not all the layers were needed for all the cases
        • some layers could have different implementations
    Intellisemantic
  • Some remarks
    • The layered framework of W3C recommendations is a significant achievement, but beware that, in a real implementation:
      • not all the layers are needed in order to define it as “semantic”,
      • semantic layers are only a part of the whole solution, which can require also other complementary technologies, as natural language processing, statical methods and so
      • The recommendation used must be complemented with other views, as presented in the slide “the three dimensions of semantics”
    • The following slides provide a overview of major reccommendarions; the W3C web site provides of coursev more detailed information
  • Most significant recommendations (1)‏
    • RDF (Resource Description Framework) for coding triples.
      • A triple describes a statement, which is formed by a thing, a corresponding property and a corresponding value, i.e. this presentation (thing) has a title (property) which is “Semantics and Web 3.0” (value).
      • A triple can also be visualized as a arch (property) connecting a source node (thing) to a destination node (value).
      • Any knowledge data base can be represented as a set of triples, and of course it can be coded in RDF
      • RDF is not the only representation for triples, but it was the first formalized by W3C and it is widely used as a interchamge format
    • RDFS is the schema language for RDF
      • It adds information about the meaning of data coded in RDF
  • Most significant recommendations (2)‏
    • SPARQL (Simple Protocol and RDF Query Language) is a query language for a RDF coded data base
    • OWL (Web Ontology Language) builds on RDF and RDFS and extends them, with notions like classes, just to mention one, in order to enable more powerful reasoning than with RDF + RDFS. Different kind of OWL language extensions have been defined, which, in the increasing order of allowed expressions, are:
      • OWL Lite
      • OWL DL (Description Logic), with some restrictions in comparison to OWL Full, in order to ensure decidibility
      • OWL Full, which is the the unrestricted OWL dialect. Decidibility is not assured in this case, however
  • Other significant recommendations
    • Microformats are a collection of formats for embedding metadata within XHTML and HTML web pages
    • RDFa (i.e. RDF attributes) is a proposed set of extensions to XML, including XHTML of course, in order to allow the inclusion of metadata (e.g. the author, the date) in documents
    • eRFD (i.e. embedded RDF) is similar to RDFa, but it is meant only for XHTML documents
    • GRDLL (Gleaning Resource Descriptions from Dialects of Languages) is a W3C recommendarion for extracting RDF out of XHTML using XSLT
    • All these recommendations emerged in the last 2 years, to enable applications with “some semantics” inside
  • Architectures Definition, Motivations,Structure knowledge data base development Triple stores Semantic middleware Semantic services
  • Definition and motivations
    • A semantic architecture includes a knowledge base as a clearly identified layer
    • Motivations:
    • to increase the business agility
    • to facilitate the integration of different applications and data
    • to provide improved user interfaces
    • to provide new functions
    Intellisemantic
  • Structure A semantic architecture can be described as the usual four layered architecture (presentation, business logic, service integration, data) to which a knowledge layer is superimposed The knowledge layer itself can affect only the presentation, or the business logic or the service integration, or the data logic, producing a first classification of architectures Intellisemantic, Politecnico di Torino
  • Structure presentation Business logic Service integration Data Knowledge Development tool Knowledge data base
  • Developing application knowledge base
    • Understand application requirements
      • which is the application domain?
      • which is the typical document structure?
      • how it is possible to benefit from metadata?
    • Indentify the most appropriate resources
      • many good quality resources are available
      • a application requiring to develop a totally new knowledge base is not so common
    • Modify or merge them if appropriate
    • Validate and test the final solution
    Intellisemantic
  • How to look for knowledge data bases
    • Identify them from directories, as http://www.schemaweb.info , http://www.daml.org/ontologies
    • Identify them from aggregators (in such a case they are generally not free), as http://www.taxonomywarehouse.com
    • Identify them from “paper directories”, as the book “Ontology Engineering”, by Corcho and alii
    • Identify some of them from specific sites, as http://www.eclass-online.com )
    • Identify them through suitable search engines, as Swoogle
    Intellisemantic
  • Most common tools and building blocks in semantics
    • knowledge base development environments: some of them have been developed by the academic community and are free, as Protegè, others are commercial OTS
    • semantic stores, i.e. a RDF triple store manager: some of them was developed directly for semantics, other are semantic extensions of the usual relational data bases, as Oracle
    • reasoners, as for example Pellet
    • semantic middleware and web services
    • Semantic search engines, to be distinghied further into diferent cathegories, as a) searching for knowledge data bases b) improving the search and the navigation for sites and documents
    Intellisemantic
  • Applications Enterprise applications Web applications
  • Enterprise semantics motivations: the user point of view
    • (+) less costs related to the reduction of the time overhead
      • for document retrieving, information extraction
      • more in general, for accomplishing routinary tasks
    • (+) better quality of results and more revenues
      • in intelligence to improve the relevance of documents found
      • in eCommerce to increase the % of items found and sold
    • (+) more agile enterprise i.e. improved time to market
      • a well identified and maintained knowledge layer can speed up IT system updates motivated by internal procedural changes or by external rules compliance
    • (+) new functions and business opportunities as well
      • most of them are related to the “long tail” and Web 2.0
    Intellisemantic
  • Enterprise semantic systems: the developer and integrator point of view
    • (+) are easier to upgrade
      • by simply updating the knowledge layer
    • (+) are easier to maintain ,
      • by focusing on the knowledge layer
    • (-) require some more inital design efforts
      • since they imply a more structured design
    Intellisemantic
  • Enterprise semantic systems: the manager and analyst point of view
    • (-/+) require to train your staff about a new paradigm
      • but this can be repaid by the following projects
    • (-/+) require a careful analysis, since the novelty of the field
      • in order to identify what can be considered developmen t, by today technology, and what has yet to considered a research challenge
    • (-/+) require the identification and solution of the additional technical challenges in the application
      • e.g. properly handling different kinds of documents formats can affect a semantic application, although it is not semantics
    • (-/+) requires the identification and solution of the additional managerial challenges in the application
      • e.g. some organisations have difficulties in accepting even the best knowledge management systems,for cultural reasons
    Intellisemantic
  • Enterprise semantic applications (1)‏
    • Extend the usual enterprise solutions, as:
    • Content Management Systems (CMS)‏
    • Knowledge Managent Systems (KMS)‏
    • Customer Relationship Management (CRM)‏
    • Business Intelligence Sustems (BI)
    • Human Resource Management Systems
    • IT and enterprise governance , including security For improving fuctionalities in:
    Intellisemantic
  • Enterprise semantic applications (2)‏
    • For improving aforementioned solutions in specific fuctionalities, as:
    • Unstructured data (document classification and search, document analysis, facts extraction)‏
    • Structured data (merging different data bases, reasoning)‏
    • Applicarion integration
    • Solutions and semantically extended functionalties can be combined in a matrix, showing also the best
    Intellisemantic
  • Semantic web applications
    • Semantic web applications were the original “dream”, which is a far reaching effort for reasons as:
      • The scale of information available on the web
      • New business models to developd for the use of “better” information on the web
    • Something in any case is moving since 2008, although we have to distinguish between
      • “ My Semantic web” solutions, i.e. a semantically enhanced portal or service
      • Semantic “web on the large” solutions, including tools and initiatives for this evolution
      • This distinction is not always so sharp indeed.
  • “ My Semantic web” applications
    • Extension of Web 2.0 solutions, as:
      • Semantic social networks, as Twine
      • Semantic mashup, as Tripit
      • Semantic wikis, as Metaweb
      • Semantic blogs, as Zemanta
    • Depending on the case, they aggregate more information in comparison to usual Web 2.0, increasing the user stickness, or do more automatic work for the user benefit
    • Semantic technology enabled portals, as provided by Elsevier, BBC, harper
    • Typically delivered by media companies, they classify and reaggregate the portal information in order to improve the findability of their free or better of their for sale documents
  • Semantic Web on the large main driving forces today (1)‏
      • Open world:
        • Open data:
          • Expose and interconnect semanticised web resources, in order to obtain a web of open data: this is the direction followed by the Linked Objet Data (LOD) project, whose interconnected resources are continously increasing and between others include DBpedia, which is the converted RDF from Wilipedia
        • open semantic web service, as http://www.opencalais.com
  • Semantic Web on the large main driving forces today (2)
    • Semantic markerting
      • Semantic positioning
        • Support web masters in producing resources provided by semantic metadata in such a way to mprove their findability: such a kind of initiative is carried aiur by Search Monkey, by Yahoo, and will affect also web marketing
      • Semantic contextual advertisement
        • solutions, using semantics for associating advertisements to the most suitable web pages, as for example http://www.peer39.com and http://www.adpepper.com
  • Semantic web on the large: other directions
    • Semantic search engines, to be further distinguished into:
      • Vertical specialized engines, as Hakia
      • Searchers for tripicized resources, as those available with the Linked Open Data (LOD) Initiative
      • Engined supporting natural language, as Powerset
    • Semantic browsers
  • References
    • http://www.w3.org/2001/sw the W3C Semantic Web Activity site, with access to specifications, activities, tutorials and best implementation cases
    • http://www.semanticuniverse.com a portal for semantics
    • “ Ontology Engineering”, by A. Gomez-Perez, M. Fernando Lopez, O. Corcho, Springer, 2004
    • “ Adaptive Information”, by J.Pollock, R. Hodgson, Wiley 2004
    • “ Semantics in Business Systems”, by D. McComb, Morgan Kaufnann Puvlishers, 2004
    • “ A Semantic Web Primer” by G. Antoniou, F. van Harmelen, MIT Press, 2008
    • “ Semantic Web for the Working Ontologisr”, by D. Allemang, J. Hendler, Morgan Kaufann Publishers, 2008
    • “ Semantic Web for Dummies” by J. Pollock, Wiley 2009
  • Acknowledgments
    • I acknowledge my colleagues of IntelliSemantic for their useful feedbacks, discussions and insights.
    • I acknowledge also managers and professionals who attended IntelliSemantic one day workshop in semantics, whose key points are summarized in the second part of this presenration, and colleagues who attended my recent talks about semantics at MilanIn and BAIA events, whose key points are detailed in the first part of this presentation: all the feedabacks I received contributed to improve the quality of this presentation.
    • For further comments, please do not esitate to contact me at [email_address]
  • Licence
    • This work is licenced under Creative Commons Attribution-NonCommercial-Share A like 3.0 Unported Licence
    • To view a copy of this licence visit:
    • http://creativecommons.org/licenses/by-nc-sa/3.0/