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PoolParty Solutions


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10 enterprise solutions based on linked data and semantic technologies

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PoolParty Solutions

  1. 1.     Smart Customer SupportPoolParty Solutions Systems How can semantic technologies help to make customer support systems more intelligent inAndreas Blumauer order to understand customers needs better?a.blumauer@semantic-web.at25.02.2013 Addressed problem Customer support systems frequently cause disorientation due to the technical terms used and a lack of transparent and easily comprehensible navigation structures. Providers of products and services from various sectors (telecommunications, public administration, law, etc.) use different languages and differing categories than consumers (or citizens) do. Thus, in many cases clients have to deal with frustrating translation work which leads to misunderstandings and increasing costs in theContents call center.Smart Customer Support Systems ......................... 1Enterprise Linked Data Integration ......................... 2 Our solution approachVocabulary Management ........................................ 3 Users benefit from a guidance system which helpsSemantic Content Management ............................. 4 to achieve orientation at any point of the supportText Mining of Business News ............................... 5 system. The guidance system consists of semanticVertical Search Solutions ....................................... 6 search facilities like search filters (faceted search),Knowledge Bases ................................................... 7 search refinements, similarity search (see also:Linked Open Data ................................................... 8 recommender system) and integrated fact boxesRecommender Systems ......................................... 9 which display further details about the search termSemantic Search .................................................. 10 which might refer to a product, for example. As a prerequisite for these improvements, a knowledge model consisting of concepts (e.g. products, technologies, services, etc.) its relations and 1/11  
  2. 2.    differing names (including synonyms) has to becreated. This model is based on an open W3C Enterprise Linked Datastandard called SKOS which makes the effortfuture-proof. In some cases it is advisable to split Integrationup the thesaurus into (at least) two modules. Asemantic layer of this kind helps to translate How can linked data be used as a more agilebetween the two worlds (supplier/vendor vs. and flexible methodology for enterprise dataclient/customer). While the supplier’s thesaurus still integration?links its concepts to the corresponding parts of theclient’s thesaurus, the thesauri can be managedseparately from each other.Results • Semantic index of content base of the support system • User-friendly digital guidance system • Facilities to refine search queries to find answers to specific questions more easily • Help users to learn quickly: combine search results with facts from other knowledge bases automaticallyUsed methods, technologies and Addressed problemstandards Putting all the information in one place which describes a business object like a product, a • PoolParty knowledge modelling approach customer or a certain technology can ease the life of many people significantly. Unfortunately, the • Simple Knowledge Organization System automatic integration of data from various sources (SKOS) can cause tremendous efforts. Data in enterprises is organised such that data remains locked up in its • PoolParty Thesaurus Server database. Knowledge workers are forced to collect information from a series of data silos manually to • PoolParty Extractor put those pieces together like a puzzle in order to create the basis for a decision making process. • PoolParty Search Data integration projects most often are built upon yet another inflexible data structure. Numerous amendments or additions made to the structure or to the semantics of an information component cannot be reflected properly by the integration layer. The result is a landscape consisting of data silos which are scarcely connected to each other. Intelligent linkages happen only in the course of ad hoc processes which are not readily comprehensible. 2/11  
  3. 3.    Our solution approach Vocabulary ManagementWeb data, but also data in enterprises arecharacterized by a great structural diversity as well How can controlled vocabularies become anas frequent changes. This poses a great challenge easily accessible source of knowledge to linkfor applications based on that data. We address information sources more efficiently?this problem by using a flexible data model thatsupports the integration of heterogenous andvolatile data. We make use of linked datatechnologies for data integration purposes whichrelies on graph-based models. This allows toincrementally extend the schema by variousproperties and constraints. Linked data is based onopen standards which makes the effort future-proof.Results • 360o views on specific business objects (topic pages) like products, companies, technologies etc. • Reports based on sometimes complex queries which can only be answered if data is used from various sources Addressed problem • Mashups of unstructured (e.g.: business Benefits from creating and using vocabularies still news, social media, etc.) and structured seem to be below the invested effort. Whereas data (e.g.: statistics, legacy data, etc.) controlled vocabularies can build the basis for a richer metadata management system, it remains • Mashups of data from the web (e.g.: open still unclear how thesauri or ontologies can also be government data) and internal data sources used as a valuable information source on its own. Vocabulary management can help to overcome the Babylonian language confusion. A thesaurus canUsed methods, technologies and be used by knowledge workers as an encyclopediastandards to better understand unclear, unintelligible or ambiguous terms and phrases which occur in a • Linked data stack large proportion of the documents, mails or protocols they have to deal with on a daily basis. • Semantic web standards (RDF, SKOS, SPARQL etc.) Our solution approach • Linked data alignment In order to get (enterprise) vocabularies widely • Linked data manager accepted the costs for the creation and development of such thesauri and vocabularies • PoolParty Semantic Integrator have to stay as low as possible. This can be achieved if thesaurus managers get support by • PoolParty Extractor appropriate methods and software tools to produce high-quality semantic metadata built upon open • Large scale RDF triple stores (e.g.: standards. In case the enterprise (or domain- Virtuoso) specific) thesaurus is built upon W3Cs Simple Knowledge Organization System (SKOS) it can also build the core of an organizations knowledge 3/11  
  4. 4.    graph to be reused by many other applications. Inaddition, built-in text analytics, several importers Semantic Contentand linked data enrichment tools help to extend theenterprise vocabulary further and further while Managementkeeping the efforts as low as possible. Acomprehensive library of quality- and validity How can linked data help to establish achecks makes sure that the outcome will meet the metadata layer across systems to link contenthighest demands for quality. Putting an enterprise from multiple sources?vocabulary to the right place means, that it shouldbe reused by other applications as often aspossible. Several standard APIs allow quickintegration as well as complex queries over theresulting knowledge graph.Results • Enterprise vocabularies fully compatible with W3Cs semantic web standards (SPARQL, RDF, SKOS) • Ready to be used within a linked data enterprise architecture • Highly comfortable thesaurus editor, fully web-based with hundreds of features • Importers for legacy data sources Addressed problem • Integrations with frequently used enterprise Managing content in a CMS is a cost-intensive systems like Sharepoint, Confluence or task. To take care of metadata as an integral part of Drupal professional content management is likely to be neglected. Using referencable metadata on top of • Facilities to enrich thesauri with terms from our content is key to increase the value of such document collections and linked open data cost-intensive assets.Used methods, technologies and Our solution approachstandards Text analytics based upon controlled vocabularies • PoolParty Thesaurus Server can help to keep the cost of managing metadata in a CMS as low as possible. Annotating and • Simple Knowledge Organization System categorizing content by using thesauri also makes (SKOS) sure that a highly-expressive semantic index of our content repositories can be built later on. Automatic • PoolParty Knowledge Modeling Approach text analytics in combination with comfortable user- dialogues for semi-automatic content tagging can • Linked Data enrichment be used to link, categorize and annotate content. Our solution approach is aiming to establish a • Data importers and text analytics metadata layer outside the actual content management system to make an integration with • Thesaurus Quality and Validity Checker other content repositories as easy as possible. (qSKOS) 4/11  
  5. 5.    Results Text Mining of Business • Automatic document annotation and categorization (XML documents, plain text) News • Semi-automatic tagging dialogues based on How can semantic technologies help to filter tag recommender out news items and to put them in a specific context automatically? • Rule-based named entity recognition • Sentiment analysis • Connectors to enterprise linked data repositoryUsed methods, technologies andstandards • Concept-based annotation • Simple Knowledge Organization System (SKOS) • Natural language processing • PoolParty Extractor Addressed problem Working as an analyst, researcher, product manager or as a journalist means that one has to skim through hundreds of news articles per day. On the one hand the usage of social networks and attached reputation systems can help to narrow down the number of relevant sources, on the other hand an ever increasing amount of information has ended up on our desktops since we have become active members of Twitter, Linkedin or other social media channels. Unstructured information makes up the largest portion of frequently quoted big data. Being able to deal with unstructured information in combination with structured data like statistics or relational databases has become a key ability to succeed in a variety of knowledge intensive industries. Our solution approach Domain-specific text mining becomes more precise when built upon controlled vocabularies. The analysis of large amounts of mainly short documents like business news requires highly 5/11  
  6. 6.    performant algorithms built on top of specificknowledge graphs. The outcome of text mining in Vertical Search Solutionsthe context of linked data is rather a web of linkedentities than simply a semantic document index. How can semantic knowledge modelsBy using linked data based knowledge models in its contribute to a highly efficient topical searchcore, PoolParty platform is able to combine text engine?mining with graph databases.Results • Precise and highly performant text mining for specific domains • Extraction of highly structured knowledge graphs from semi-structured and unstructured information • Basis for integrated views over heterogeneous information sourcesUsed methods, technologies andstandards • PoolParty Extractor Addressed problem • Natural Language Processing Common paradigms of search engine development • SKOS not necessarily reach the optimal results when specialized information put into a specific context or process has to be retrieved. A vertical search engine, in contrast to a general web or enterprise search engine, focuses on a specific knowledge domain. To bring such a topical search engine to its full potential the underlying index has to be built upon a specific knowledge model. A vertical search engine, as distinct from a general search tool, makes also use of an individual user interface and domain-specific navigational elements. But most importantly, in case the search engine shall cover a clearly defined scope, the use of semantic knowledge models achieves a very good cost- benefit ratio. Our solution approach Structured information as well as unstructured text can build the basis for vertical search solutions. By reflecting the knowledge about the search domain with means of a thesaurus, a more precise semantic document index can be built. Using linked data based knowledge graphs for document 6/11  
  7. 7.    indexing instead of pure term-based vocabulariesallows to further enrich the document basis by facts Knowledge Basesfrom other knowledge models. Users benefit fromricher search results not only consisting of How can semantic technologies help to makedocuments but also of facts and figures related to collaborative knowledge bases betterthe actual information needs. Since a vertical accessible for employees?search solution is built around a well-defined scope,it is also advisable to generate and provide specificsearch assistants like facets or search refinementtools.Results • Smart search assistants (faceted search etc.) • Precise search results • Search application and interfaces customized to the needs of the subject matter experts • Integrated views on structured and unstructured information alike Addressed problem Transforming a simple document server into aUsed methods, technologies and collaborative knowledge base which serves as astandards valuable source for knowledge workers in their daily work is not as simple as it seems to be. On the one • PoolParty Thesaurus Server hand collaboration platforms like enterprise wikis most often are the right choice to encourage people • PoolParty Search Server to collect ideas for new content or to make knowledge about products and services better accessible. On the other hand knowledge bases tend to get tattered over time. Our solution approach In order to make specific knowledge about business processes, methods or technologies available for as many employees as possible, we combine the best of three worlds: enterprise collaboration software, text mining and controlled vocabularies. This results in solutions which fulfill the demand for highly dynamic and flexible knowledge bases, still stable (technical and content-wise) enough to be used in professional environments. Since the knowledge base is generated around a controlled vocabulary acting as a meta-layer, traditional navigation structures like trees no longer act as a rigid corset which makes traversing of graph-like structures impossible. 7/11  
  8. 8.    Semi-automatic tools for linking, categorizing andcontent indexing is key to overcome this problem. Linked Open DataPutting a controlled vocabulary in place whichgrows in parallel to the content base demands new How can open semantic web standardsand more agile patterns of taxonomy or thesaurus stimulate new ways to distribute and reuse datamanagement than traditional approaches would and information across intraorganisational andprovide. extraorganisational boundaries?Results • Linked knowledge objects on top of enterprise collaboration platforms like Confluence or Sharepoint • Semantic search over knowledge bases • Automatic content enrichmentUsed methods, technologies andstandards • Atlassian Confluence • Microsoft Sharepoint Addressed problem • Drupal For many organizations the efficient distribution of its data has become a main task. For example, • PoolParty PowerTagging NPOs or NGOs which want to stimulate specific markets can free up their information, make it • Semantic Sharepoint available and accessible to allow new entrants. Publishers have recognized that opening up (parts • Semantic Confluence of) their databases can stimulate the demand for even more information inducing finally the act of purchase. Open semantic web standards play a key role in this distribution policy since they allow a high degree of reusability and linking. Our solution approach The strict usage of semantic web standards, not only as an export format but as the way to represent data internally allows us to bring linked data to its full potential. Initial phases of a knowledge graph project might start with the creation of a SKOS thesaurus further enriched by facts or ontological statements from other linked data sources. The publication of linked data inside corporate boundaries or of linked open data on the (semantic) web is technically spoken the same task. In both cases data can be accessed programmatically by the usage of standard APIs 8/11  
  9. 9.    like SPARQL. Data becomes a self-describingdigital asset to build semantically enhanced Recommender Systemsapplications or mashups. How can semantic technologies help to enrich a DMS or CMS with intelligent functions likeResults recommender systems? • Linked Data Server as part of the PoolParty Thesaurus Server • Linked Open Data Portals • Linked Data Manager to retrieve, extract and transform open data automatically and periodically • Linked Data alignment toolsUsed methods, technologies andstandards • PoolParty Thesaurus Server • Linked Data Manager Addressed problem • Semantic Web Standards (SKOS, RDF Given the plethora of information in large document Schema, SPARQL) collections or content repositories, the provision of digital assistants can become essential to survive. • Drupal Who else has been working on a similar document or a related issue I am working on right now? Is • Large scale RDF triple stores (e.g.: there a corresponding slide deck available which Virtuoso) deals with the same questions like the paper I am writing just now? Typical document or content management systems are still more focussed on workflow management or archiving solutions than on functionalities which help to put content into the context of the actual work step. Our solution approach Recommender engines work on top of semantic fingerprints. Each business object (resource) is represented by its semantic metadata which is a fragment of the overall enterprise knowledge graph. This meta information is used to detect hidden links between objects like persons or documents. Controlled vocabularies based on SKOS and linked data build the backbone to express the semantic fingerprint of each resource. Algorithms which calculate the similarity between such graph fragments are used as core elements for the recommendation engine. 9/11  
  10. 10.    Results Semantic Search • Content management workflows free of interruptions and media breaks How can semantic search (which goes beyond search over documents only) be realized in the • Avoid unnecessary overlapping and context of enterprise information systems? duplications of work • Support and stimulate cross-reading in knowledge bases or cross-selling in shop systems • Enable serendipity effectsUsed methods, technologies andstandards • Semantic fingerprints • Similarity algorithms and machine learning • SPARQL • PoolParty Search Server Addressed problem • Large scale triple stores (e.g.: Virtuoso) Search has become a more and more important functionality in most information management systems. Learning from web search engines, most intranet searches have already introduced some useful assistance functions like auto-complete. Semantic search can go far beyond those rather simple features and can help to reduce search times to a minimum while user experience will improve noticeably. Looking at digital assistants like Apples Siri, it becomes obvious that the role of search systems will become more and more important for the next generations of knowledge bases. Semantic search and search in general is still very focused on the idea of retrieving a list of relevant documents whilst in reality knowledge workers have to find and link information from a huge variety of sources including statistical databases, videos or personnel databases. Our solution approach Semantic search in the context of linked data means to search over a knowledge graph including document search. This approach makes complex queries possible, e.g.: show me all business news which mention at least one of our suppliers of components used in product ABC. The basis for 10/11  
  11. 11.    such complex queries is made up by an enterpriselinked data store containing a semantic index ofvarious legacy data sources combined with theknowledge graph plus enrichments from otherlinked data sources, taxonomies and ontologies.Results • search engine which provides means for complex queries • queries over various kinds of information (documents, relational databases, taxonomies, etc.) • personalized searchUsed methods, technologies andstandards • PoolParty Search Server • SPARQL • Large scale triple stores (eg.: Virtuoso)This work is licensed under aCreative Commons Attribution-NoDerivs 3.0Unported License. 11/11