Inconsistent Data on the Semantic Web A Theoretical Approach Brian Goodrich

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Inconsistent Data on the Semantic WebA
Theoretical ApproachBrian Goodrich
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The Problem

• An computer application has a set of input and a
  set of output based upon the set of input and its
  internal logic.
• If an application is given data as input which
  causes a conflicted state in deciding its output,
  it will crash without some kind of logic by which
  to decide that conflict.
• The Semantic Web is based being able to parse
  human intent from structured, semi-structured,
  and unstructured data on the Web.
• Human intent is frequently conflicting.

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• Conflicting Data Sources
• Malicious - (deceptive or rerouting attempts) or
  just ignorantly incorrect information
• Incomplete Information having insufficient
  context or simply unfinished data
• Humor especially sarcasm, satire and
  exaggeration (e.g. political cartoons)
• Time what once was one thing is now another
  (e.g. quality of service, price, etc.)
• Ontological Deficiency when extraction ontology
  lacks sufficient vividness to separate data
  appropriately.

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Solution
Fast Maintain current speed of the Web.
Accurate Correct decisions of data reliance.
Dynamic Keeps pace with change on the Web.
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Thesis

• To propose a method for simplifying the task of
  dealing with conflicting data on the Semantic Web
  in a fast, accurate and dynamic way by supplying
  each web source with a derived indicator of its
  communal usage called a Consensual Reliability
  Score. (CRS)

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Methods
(az) (by) (cx) (dw) CRS(f)

• Formula for deriving CRS from inputs a, b, c,
  d.
• With weighted constants z, y, x, w.

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Site Type Mining (a z)

• Five types of Web Pages
• Head Pages
• Navigation Pages
• Content Pages
• Look up Pages
• Personal Pages

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Incoming Index (b y)

• Distributed web crawler that counts hyperlinks
  then traverses the unique hyperlink paths,
  looking for additional links.
• Link counts are stored in a hash indexed by the
  destination of the hyperlinks.
• Provides a dynamic count of how often the
  internet as a whole is pointing to a given web
  source. Therefore an indication of how often
  people use the given web source.
• Excludes orphan sites (mostly personal sites and
  spam pop-ups)
• Based on the success of the Google search engine

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Usage Mining (c x)

• Most straight forward approach of testing how
  often people use a web source. Query sites of
  hits or how many people have seen this site?
• Problem Unlike Incoming Index method, does not
  exclude orphan sites.
• Further experimentation needed to determine xs
  weight.

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Direct Survey (d w)

• Most reliable method of determining reliability.
  Manually query users directly.
• Too slow and costly to be consider a whole
  solution but can assist in CRS derivation.
  Hopefully offset frequently visited sites with no
  true info (onion.com, humor, etc.)
• More experimentation needed to determine ws
  weight.

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Review
(az) (by) (cx) (dw) CRS(f)
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Classical content data mining is not applicable
in this case (CRS derivation) because it is the
content of the web sources that is in
question. -Brian Goodrich
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Storage

• Global Index
• Fast access
• Centralized storage for CRSBot.
• Centralized vulnerability.
• Vital non-distributed resource in a distributed
  system.
• Local Storage
• Non-centralized vulnerability
• Non-unified derivation formula (disrupts trust
  algorithm)
• Local Derivation
• Too slow to be useful (problem size too large)

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Related Work

• Tim Berners-Lee
• There is a choice here, and I am not sure right
  now which appeals to me most. One is to say
  precicely,
• "whatever any document says of the form xxxx is a
  member of W3C so long as it is signed with key
  32457934759432".
• The other is to say,
• "whatever is of form xxxx and can be inferred
  from information signed with key 32457934759432
• Problems with both choices, but both use static
  references in a dynamic environment (the web)

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Contributions

• CRS provides a fast and accurate measure of
  community consensus on the web.
• Allows reliable decision about deciding between
  conflicting data on the web, fine-tuning the
  results from the Semantic Web.

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Limitations

• Totally reliant on usage patterns of the
  internet, which may not always reflect which data
  is more correct.
• Reflects only consensus to a data source, not the
  actual data contained in it.
• Cannot express complex or compound relationships
  or extract partial truths.

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Questions?