• Use Cases from the Charter

Use Cases motivating refinement of Uncertainty Ontology

/Discussion

Purpose/Goals

We follow Charter http://www.w3.org/2005/Incubator/urw3/charter. The objectives of the URW3-XG are twofold:

• To identify and describe situations on the scale of the World Wide Web for which uncertainty reasoning would significantly increase the potential for extracting useful information;
• To identify methodologies that can be applied to these situations and the fundamentals of a standardized representation that could serve as the basis for information exchange necessary for these methodologies to be effectively used.

To motivate this, the charter mentions several use cases. In this use case we briefly link these „charter use cases“ to several more detailed use cases

• The main method to achieve goals is to enrich web documents with Uncertainty Annotations [UncAnn], hence increase the potential for extracting useful information.

Issues and Relevance to Uncertainty (Summarize how relevant to uncertainty reasoning and representation?)

In automated Web data processing we often face situations when Boolean truth values are unknown, unknowable, or inapplicable. The nightmare caused by proprietary uncertainty representations makes impossible to use these for further processing. We briefly mention several use cases originally mentioned in the URW3 charter. Moreover we include here Fine grained version of Uncertainty Ontology a finer grained version of Uncertainty Ontology to show a possible evolution of upper level UncertaintyOntology and emphasize uncertainty issues connected to machine processing (lot of situations is perfectly certain when considering human consumption of web resources). We focus especially on finer classification of Machine Agents (UncAnn Agent:MachineAgent) and uncertainty caused by lack of knowledge of a machine agent (UncAnn UncertaintyNature:Epistemic:MachineEpistemic).

• Information extracted from large information networks such as the World Wide Web is typically incomplete (UncAnn UncertaintyType:Incompleteness). The ability to exploit partial information is very useful for identifying sources of service or information. For example, that an online service deals with greeting cards may be evidence that it also sells stationery. It is clear that search effectiveness could be improved by appropriate use of technologies for handling uncertainty.

• Much information on the World Wide Web is likely to be uncertain. In some cases this is an inherent property of the world (UncAnn UncertaintyNature:Aleatory). Nevertheless, it is a difference, whether this world is the physical world, or living, society or business. Examples include weather forecasts (UncAnn UncertaintyNature:Aleatory:PhysicalWorldAleatory), gambling odds or stock exchange (UncAnn UncertaintyNature:Aleatory:BusinessWorldAleatory). Canonical methods for representing and integrating such information are necessary for communicating it in a seamless fashion.

• Web information is also often incorrect or only partially correct, raising issues related to trust or credibility (UncAnn UncertaintyNature: Trust, UncertaintyType:Lie). Uncertainty representation and reasoning helps to resolve tension amongst information sources having different confidence and trust levels (this is one of few cases where even a human agent has problems with processing information).

• The Semantic Web vision implies that numerous distinct but conceptually overlapping ontologies will co-exist and interoperate (UncAnn UncertaintyNature:Epistemic:MachineEpistemic). It is likely that in such scenarios ontology mapping will benefit from the ability to represent degrees of membership and/or likelihoods of membership in categories of a target ontology, given information about class membership in the source ontology.

• Dynamic composability of Web Services will require runtime identification of processing and data resources and resolution of policy objectives. Uncertainty reasoning techniques may be necessary to resolve situations in which existing information is not definitive (UncAnn UncertaintyNature:Epistemic:MachineEpistemic).

Assumptions/Preconditions (List the preconditions necessary for this use case to occur, including description of necessary context)

• We need a unified model of uncertainty annotation of web resources
• Due to the size of the web this should be done automatically (maybe after some human training for particular domain)

Required resources (List resources, such as data sources, ontologies, needed for scenario)

1. Uncertainty Ontology, deductive engines using uncertainty information
2. in case of third party uncertainty annotation we need a storage for these annotated resources

Associate methodologies that could help (Describe / point to papers ...)

All models and respective methods for handling different types of uncertainty (UncAnn UncertaintyModel)

recommend those aspects that are considered most important to be included in a standard representation of vagueness and uncertainty (this the main task of our XG)

Extensions of UncertaintyOntology which prove to be useful in annotation of web resources in order to improve their machine processing

Successful End (Describe what happens if this use case is successful)

Automatic processing of web resources will be more accurate

Failed End (Describe what happens if this use case fails)

• Nothing happens, the web will be as of today

Main Scenario (List the sequence of events for the basic course (numbered))

1. Development of more detailed uncertainty ontology
2. get acquaintance within some specific domain
3. experiments with processing of resources annotated with such ontology
4. life cycle of development continues until models and methods bring improvement

Additional background information or references

Some acquaintance with uncertainty processing is in the community of Information Retrieval, see e.g. Parsons, S., '' Current approaches to handling imperfect information in data and knowledge bases ''

(Summarize/provide references to information to further describe or characterize use case)

Variations (List the alternatives that will not be further decomposed at this time)

Open Issues