ontos solutions for semantic web: text mining, navigation ... · ais-adm-07, st. petersburg,...

The Second International WorkshopAIS-ADM-07

June 3-5, 2007, St. Petersburg, Russia

Ontos Solutions for Semantic Web:Text Mining, Navigation and Analytics

Vladimir Khoroshevsky, Computer Center RAS, 40 Vavilov str,

GSP-1 Moscow, Russia

Irina Efimenko, Grigory Drobyazko, Polina Kananykina, Victor Klintsov,Dmitry Lisitsin, Viacheslav Seledkin, Anatoli Starostin, Vyacheslav Vorobyov

Ontos AG, 84/2 Vernadskogo Av., 119606 Moscow, Russia

AIS-ADM-07, St. Petersburg, Russia, June 3 - 5, 2007

AgendaAgenda

IntroductionSemantic Technologies Umbrella

Ontos Solutions for Semantic WebGeneral View

MAS Based Information-To-Knowledge TransformationOntology Driven Text Mining & Web Mining

Semantic Navigation and Analytics

Ontos Semantic Services DemosPortal MedTrustSemantic RSS and Semantic Navigation

ConclusionChallenges and Future Trends


Introduction.Semantic Technologies Umbrella

SemanticWeb

MAS

“The Semantic Web will globalize KR, just as the WWW globalisedhypertext”

Tim Berners-Lee


Introduction.Semantic Web and MAS


Ontos Solutions for Semantic Web.General View

Daniel HladkyCEO

Ontos International AGMittelstrasse 24, 2560 [email protected]: +41 79 353 50 43Tel.: +41 32 332 82 70Fax: +41 32 332 92 52

Ontology Driven Text Mining & Web Mining

MAS Based Information-to-Knowledge Transformation

RDF-storage Development and Implementation

Semantic Navigation & Analytics

Main R&D in Domain


Ontos Solutions for Semantic Web.Ontology Driven Text Mining & Web Mining

ObjectivesCombining of AI & IT experience within the NLP domainR&D in knowledge management

ApproachesUsage of IE technologies in NL texts processingEnrichment of IE techniques with NLP on the basis of special linguistic modelsRepresentation of the NL texts meaning in the form of cognitive maps

ResultsNew generation of MIE-systemsPractical usage of the results within the commercial & non-commercial organizations



Basic Principles

Processing those constructions, that can be processed correctly, and NON-processing those ones, that still can not be processed correctly

Development of reusable components for multi-platform implementation

Providing domain ontology-driven analysis



Main Requirements

Work with multilingual document collection (English, German and Russian texts)

Work with monothematic document collection (first of all, the so-called “Business Duties” domain)

An adequate processing of relevant objects and relations, according to the concrete ontology

Representation of processing results in a form of a cognitive map, that is a kind of semantic network

Multi-platform implementation of all systems of the family



Domain Ontology “Business Duties”



Domain Ontology “Business Duties” (cont.)



Domain Ontology “MedTrust”



Web doc, xls, pdf

Crawler filtersplain text

plain text

OntosMiner™

RDF-Store

•Oracle RDF Store•MS SQL Server 2005•InMemory DB•IBM DB2

General Sheme of NL-texts Processing


ApplicationsDigester, Summarizer, LightOntos, Report Generator, Semantic Navigator, Semantic RSS

Dix (Dictionary SDK for developers),minDix (user-oriented Dictionary SDK),Cross-lingua XML-generator

Plugins

Additional Technologcal Components

Tokenizer, SentSplitter, Morph-tagger, POS-tagger, Gazetteer, Morph Gazetteer, NP-chunker, VP-chunker, Anaphora Resolver, NE-transducer, OrthoMatcher (rule-based), UnknownMather(rule-based), Minimizer, Semantic Tagger (on text), Semantic Tagger (by sent), XML-generator (model-driven)

Tokenizer, SentSplitter, POS-tagger, Gazetteer, NE-transducer, Coreferencer, OrthoMatcher(hardcoded)

Main Modules

Domain-based ChainCreole-based Chain

Architecture

Ontos SolutionGATE Solution



Extracted Named Entities Types

People;Organizations – Various types (commercial, educational, etc.);ElectionsMass Media and Media holdingsPartiesGovernment StructuresTitles and JobTitles;Scientific degrees;Several kinds of Addresses;Money;Percent;URL, e-mail, phone (international style);Locations;Dates and Periods of Time;Medicines, diseases, treatment methods, etc.Etc.



Affiliate;Buy-Sell; Employ;Found;Graduate;Invest;JointVenture; Own;Rival;LocatedIn;EarnDegree

Reside;Belong;Investigate;Lobby;Participate;BeFriend;BeRelative;BePartner;Support;Medical Rels (be_indicated, etc.)Etc.

Extracted Semantic Relations Types



Google management Team




Russian Text Processing Results


Ontos Solutions for Semantic Web.MAS Based Information-To-Knowledge Transformation

ResourcesCrawling

TextMining

Objects & RelationsIdentification & Merging

Data & MetadataStoring

Semantic Navigation&

Intelligent Analytics

Information Processing Technological Cycle



Internet

RDF-STORE

NLP SERVICE

IDENTIFY & MERGE AGENTs

OntosMiner

OntosMiner

OntosMiner

CRAWLERs

MERGE AGENTs

APPL SERVECEs

Ontos SOA General View



Doc Storage

InternetWeb Crawler

Web Crawler

Web Crawler

Content Extraction Broker

Broker of Doc Processing

Broker of Doc Preprocessing

Broker of Doc Postprocessing

Agent Fragmentator

Agent Tokenizer

Agent Tokenizer Agent

Tokenizer

Morph TaggerAgent

Morph TaggerAgent

Morph TaggerAgent

Vocab AgentVocab Agent

Vocab Agent

Vocabs

Broker of NE Transducing

VP Chunk AgentVP Chunk

AgentVP Chunk Agent

Broker of Syntax

NP Chunk Agent

NP Chunk Agent

NP Chunk Agent

NE Agent

NE AgentNE Agent

OWL Generator

Agent

Broker of Semantic Tagger

SR Agent

SR Agent SR

Agent

Ortho Matcher Agent

CorefAgent

Knowledge Base

Store Agent

Broker of Merging

Merge AgentMerge

AgentMerge Agent

MAS Architecture



Objects & Relationships Merging



Semantic Indexing


Ontos Solutions for Semantic Web.Semantic Navigation and Analytics

Semantic Navigation

On Fly Digesting of Document Collections

On Fly Summarization of Documents & Document Collections in a Specified Target Language

Documents & Document Collections Meaning Visual Representation & on Fly Reporting by Demands


Ontos Solutions for Semantic Web.Semantic Navigation

Backward to Previous point of semantic navigation

Hide/Visualize object attributes

Change rank threshold

Look through references

State-1 State-3State-2 State-4

Choose an object of interest

Open Object Navigation Card


Open Relation Navigation Card

Open Object Navigation Card


Return to Start point

Forward to Next point

Forward to Next point

Return to Start point

Show navigation history, Generate Digest

Generate summary

Generate summary

Show navigation history, Generate Digest

Work Flow of Semantic Web Client


Ontos Solutions for Semantic Web.Semantic Navigation

Objectcard

Object relations(How interacts With,

Indicated To, Contraindicated To,...)

RelevantDocs


Ontos Solutions for Semantic Web.Cross Lingua Digesting & Summarization

Google Management

Google Management

English

Russian

RDF StorageOntosMiner

Language IndependedInternal Representation

SemanticDigester

SemanticSummarizer

General Work Flow


Ontos Solutions for Semantic Web.Semantic Digesting

Surf for Digesting


Ontos Solutions for Semantic Web.Semantic Digesting

On Fly Digesting of Document Collections


Ontos Solutions for Semantic Web.Semantic Summarization

“Report” summarization mode


Ontos Solutions for Semantic Web.Semantic Summarization

Summary of Documents Collection by Demand


Ontos Semantic Services Demos.Portal MedTrust

Within the medtrust portal you can order directly from a connected partner (drug store). Mashups function build into the semantic navigation card.


Ontos Semantic Services Demos.Portal MedTrust


Ontos Semantic Services Demos.Semantic RSS and Semantic Navigation


Conclusion.Main Challenges

Challenges of “Multi”Multi-source (Adoption of the Semantic Web paradigm (e.g. blogs, wikis, etc.) because of their inherent multi-source nature)Multi-lingua (Collection under processing consists of the texts in different languages)Multilingual (Text under processing consists of the fragments in different languages)

Challenges of “Mono”Native-Language-Only Users (need in information presented in different languages but understand only native language)

Challenges of “Customizing”Domains (users needs related to the different domains)Ontologies (different domains and users need in related ontologies)Adaptation (based on extension of ontologies and/or NLPs)

Challenges of “Back-End”Results representation (different domains and users need in different representation of results)User friendly interface

Challenges of “Dimension”Volume (Terabytes of documents should be processed)Performance (NLP processing is time-consuming task)


Conclusion.Future Trends

Solutions in “Multi” and ”Mono”Multi-source, Multilingual Content ExtractionGenre-Driven Content ExtractionCross-lingua Digesting and Summarization

Solutions in “Customizing”Ontologies Merging and AlignmentUser-Driven Acquisition and Management of Lexicons & Processing RulesComputer-Aided Language Processing

Solutions in“Back-End” and “Dimension”Representation of Results as Cognitive MapsKnowledge-Driven Semantic NavigationMulti-agent Architecture for Language Processing & Knowledge ManagementGrid Platform Usage


Conclusion.Outlook

Semantic Navigation with Analytics is available for a specified domain ontology

Pilots with first customers show that the frequency can be improved by such new technologies

Volume handling and performanceAt the moment rather difficult to imagine a “world ontology” to cover all possible topics and being supported by the super hardware infrastructure

Adding functions to the navigation card

Social book markingMashups

Semantic SearchExtending domain ontology's to cover wider range of people and areas of interest

Social book marking / manual annotation

Mashups: e.g. call pictures for a named entity

Any Questions?

Thank You!