Semantic Web In Industry

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Semantic Web In Industry

• R. Guha

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Two Levels of the Semantic Web

• Deep Semantic Web
• Intelligent agents performing inference
• Semantic Web as distributed AI
• Small problem the AI problem is not yet solved
• Shallow Semantic Web using SW/Knowledge
  Representation techniques for
• Data integration
• Search
• Is starting to see traction in industry

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Integration The new buzzword in bussiness

• Huge explosion in the number of new databases,
  applications, documents, in the 90s
• Lots of redundancy, duplication gt high
  inefficiency
• Economic pressures forcing consolidation and
  efforts to reduce inefficiency
• Two aspects to integration Process Data
• Process integration depends on data integration

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Data Integration for Science

• Many experimental fields will generate more data
  in the next 2 years than exists today
• Large part of research consists of writing
  programs to analyze data, e.g., NASA
• Tools to normalize, share, integrate data stuck
  in the 80s (ftp, perl, )
• Semantic Web could create a web of data that
  changes all this.
• Example of the Internet Observatory

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Varieties of Data Integration Data Transformation

• Data Transformation Example
• Contact Information in SAP, Siebel, PeopleSoft,
• We want to reflect updates in one data source
  into another

XSLT, etc.
App. Server
Clarify
Siebel
PeopleSoft
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Varieties of Data Integration Data Aggregation

• Data Aggregation Example
• Clinical trial data at Stanford, UCSF, Mayo
• We want to give a Meta-analyst a uniform view of
  data from these different clinical trials
• Example of how this would have helped recent meta
  studies such as the estrogen study

Relational Views
DBMS
Meta-Analyst
UCSF
Stanford
Mayo
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Data Integration Layers

• Coping with software from different vendors
• Oracle vs. DB2 vs. SQL Server this is a solved
  problem
• Coping with different formats
• Relational vs. XML vs. ISAM this too is a solved
  problem
• Coping with different schemas
• Solved for the small case where one person
  understands all the schemas
• No products for the case where it is truly
  distributed
• We know how to do it in theory, but lots of
  practical problems
• Coping with data from unknown sources
• Wide open lots of unsolved problems

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Typical Data Integration Methodology

• Use a common namespace of terms for the concepts
  in the domain of the data sources being
  integrated, e.g., Employee, Customer, Patient,
  weight, height, bodyTemperature,
• Mappings relate data items in data sources to
  terms in namespace
• Transformation algorithms map queries in terms of
  common namespace into corresponding queries in
  terms of data source vocabularies
• Background knowledge about terms essential for
  transformations e.g., Employee subClassOf
  Person, 2 people with the same last name, first
  name and street address are likely to be the
  same, I.e., common namespace is really an
  Ontology
• Mappings and common namespace are the workhorse

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Role of Semantic Web in Data Integration

• The XML stack (XML, XSD, XPath, XQuery, ) does
  not have the concepts (objects, classes,
  properties, ) required for representing
  ontologies
• RDF/S does
• Neither of the them have a language for
  expressing mappings
• But RDF/S, being closer to logic, has more of the
  machinery that is required

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Kinds of Mappings

• Simple structural
• DB1.patient.weight corresponds to Patients
  weight
• Conditional structural
• If DB1.patient.type equals Outpatient then
  DB1.patient.foo corresponds to Patients visits
  duration
• Term mappings
• CA in DB1 corresponds to California in domain
  namespace
• Object with ssn 7687667 in database 1 corresponds
  to object with id aksdks in database 2

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Challenges and non-challenges in data integration

• Non-challenge algorithms for doing the
  transformations (ISI, MCC, SU ATT)
• Engineering Challenges
• Creating large, useful ontologies that are shared
  by many
• Creating mappings
• Research Challenges
• Semantic Drift
• Fuzzy terms, probabilistic mappings
• Trust

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Engineering Challenges

• Creating large, detailed ontologies is complex
  and expensive
• But it is happening CrossWorlds for business
  concepts, MAGE, etc. for medicine
• Danger some of them might turn out to be
  proprietary
• Creating mappings is tedious and time consuming
• Object mappings pose special challenges
• Mappings need to be dynamic and constantly updated

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Research Challenges with mappings

• Semantic Drift
• The meaning of terms as interpreted by different
  members of a community, over time could drift
• Cyc experience shows that Description Logic
  mechanisms are not adequate for either detecting
  or fixing these
• Fuzzy mappings
• E.g., walmarts concept of chair is similar to
  but not the same as MOMAs concept of chair
• Probabilistic mappings
• There is a 82 likelihood that Michael Jordan in
  database 1 is the same as Michael Jordan in
  database 2

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Other data web related challenges

• Trust How should the program know whether to
  trust some new data source?
• Without this, we will only have closed systems
• Options centralized approaches like UDDI or
  decentralized approaches like WOTs
• Inverse trust how can I trust you not to
  indiscriminately distribute my data? A big issue
  in fresh scientific data
• Systems challenges
• Caching
• Preventing accidental DOS attacks

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Forecast for SW and Data Integration

• We already have a number of data integration
  tools on the market
• We are seeing the first generation of ontology
  based data integration tools from small companies
• At least some of the big players will probably
  have some offerings for doing data integration
  based on Semantic Web concepts in the near future
• Whether they use Semantic Web formats and
  acronyms is an open question
• These common vocabularies will exhibit very
  strong network effects

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Semantic Web for Search Going beyond search as
Location Bar

• Keywords ? a particular page
• Typically a home page or well known hub page
• United airlines ? www.united.com
• Unix ? gnu.org, linux.org, freebsd.org
• Search as a smarter location bar
• Page rank is ideally suited for this
• This is largely a solved problem

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Varieties of Search Research searches

• User is searching for info about something
• Could be directed user is looking for a
  particular property
• Price of something, location of some event,
• Or undirected user is looking for some general
  class of properties
• Reviews/feedback on product, info on person or
  country
• If there is no hub page on the thing, existing
  search engines perform very poorly
• New focus is on this class of searches

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Semantic Web for Search

• Keyword based approaches havent made significant
  advances since PageRank
• Improvements may be gained by adding a modicum
  of understanding about the object denoted by
  the search query
• Improvements not just in search itself but also
  in the relevance of search related advertising

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Basic Issues

• Need database of potential objects user may be
  referring to, along with some properties of the
  object e.g., its type
• Too many objects to manually construct DB
• At least 300 million distinct object references
  on Web
• If it does know something more about the search
  terms denotation, (e.g., it denotes a musician),
  how can the search engine do better?

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Building the Web KB

• Many different automated approaches
• Simple natural language processing (Riloff, TAP,
  )
• Scrappers
• Machine Learning
• Most commercial efforts lead to proprietary KBs
• Huge opportunity for wider SW community
• Collaborate to actually create the KB

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Using the KB

• Word Sense Disambiguation., e.g., MSN Search,
  Teoma
• Incorporating data feeds into search results.
  E.g., MSN with popular musicians
• Incorporating object type specific actions. E.g.,
  Google with addresses and stock symbols
• Coming soon KB construction driven by ads

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Conclusions

• Please help Eric miller