Showing posts with label Data Fusion. Show all posts
Showing posts with label Data Fusion. Show all posts
Wednesday, September 18, 2013
Sunday, April 25, 2010
Paper Summary - Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications
"Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications", A.
Bellenger1 and S. Gatepaille, Defence and Security Information Processing, Control and Cognition department, France
This paper is relevant to my work because they use DS to represent uncertainty in ontologies. They do this by creating an upper ontology that contains the DS measures calculated i.e mass, belief, plausibility, etc.
The paper starts with a background in data fusion and gives some examples of how uncertainty is captured in ontologies and why it is important to represent uncertainty. They define uncertainty as "incomplete knowledge, including incompleteness, vagueness, ambiguity, and others". In addition to the natural occurrence of uncertainty in data, it is also a product of fusing data which may be acquired from different sources.
This is an interesting statement: " If the user/application is not able to decide in favor of a single alternative (due to insufficient trust in the respective information sources), the aggregated statement resulting from the fusion of multiple
statements is typically uncertain. The result needs to reflect and weight the different information inputs appropriately, which typically leads to uncertainty."
This is the common in military applications and in general knowledge bases because one attempts to acquire supporting data for entities in the knowledge base from various sources that can be unreliable.
They briefly discuss the shortcomings in current traditional methods to handle uncertainty in ontologies. They state that since ontologies are designed to contain concepts and relations only that describe asserted facts about the world, that they are not designed to handle uncertainty. The facts asserted are assumed to be 'true'. Therefore even information that is not certain to be 'true' are stored and lead to errors or inaccurate information. There is not a standard way to handle uncertainty currently (can read more on this).
They discuss how probability is used as a way to represent uncertainty in ontologies. They discuss some existing work in this area including (BayesOWL). The problem with this approach in particular is that it does not account for OWL properties, instances of the ontologies or the data types. There are also extensions to DL (Pronto is one of them), however performance is a problem. There are also Fuzzy approaches that exist.
They then discuss using DS. DS is presented as a generalized probability theory, however books related to this topic are not exactly is agreement with this representation. Masses are calculated and the sum of these masses make up the beliefs. It is also noted that is supports combining evidence from different sources which makes it especially useful for fusing data from different sources. They note work that actually use DS to handle the inconsistencies produced by mapping ontologies. However it also highlights a relevant paper that translates an OWL taxonomy into a directed evidential network.
The rest of the paper discusses their approach and how they use DS for modeling and reasoning. The point they make about uncertainty and why probabilistic methods can't represent it accurately is p-methods do not represent the absence of information very well. One needs to specify prior and conditional probabilities. They argue this leads to a minimax error due to is nature of symmetry prior probability assignment (.5) when information is not available. With DS missing information is not applied unless obtained indirectly. It allows one to specify a degree of ignorance (some define this as an upper and lower bound). They find this property to be appealing.
Probabilistic approaches use singletons only where DS allows one to use composites in addition to singletons. This is powerful. With probability theory there is a relationship between an event and its negation, DS does not imply a relationship between an event and its negation, it only models beliefs associated with a class.
They mention an additional point that I find makes this approach appealing. DS provides a way to combine evidence from different sources. This makes it especially useful for fusion.
They state, "the evidence theory is much more flexible than the probability theory". This is a strong statement and I'm not sure if it is completely true based on other papers that show how both Bayesian and DS can produce similar results.
Ok the paper ends with their approach. They discuss their proposed model which is an upper ontology representing the uncertainty. A DS_Concept which is a subclass of OWL:Thing has a DS_Mass, DS_Belief, DS_Plausibility, and a DS_Source. The Uncertain_Concept represents a concept that is part of the set. There is an object property is_either which has a range of owl:Thing so that all instances can be used.
This paper isn't cited by anyone else but I think there are good ideas proposed here and I am using this paper in my 601 work.
Bellenger1 and S. Gatepaille, Defence and Security Information Processing, Control and Cognition department, France
This paper is relevant to my work because they use DS to represent uncertainty in ontologies. They do this by creating an upper ontology that contains the DS measures calculated i.e mass, belief, plausibility, etc.
The paper starts with a background in data fusion and gives some examples of how uncertainty is captured in ontologies and why it is important to represent uncertainty. They define uncertainty as "incomplete knowledge, including incompleteness, vagueness, ambiguity, and others". In addition to the natural occurrence of uncertainty in data, it is also a product of fusing data which may be acquired from different sources.
This is an interesting statement: " If the user/application is not able to decide in favor of a single alternative (due to insufficient trust in the respective information sources), the aggregated statement resulting from the fusion of multiple
statements is typically uncertain. The result needs to reflect and weight the different information inputs appropriately, which typically leads to uncertainty."
This is the common in military applications and in general knowledge bases because one attempts to acquire supporting data for entities in the knowledge base from various sources that can be unreliable.
They briefly discuss the shortcomings in current traditional methods to handle uncertainty in ontologies. They state that since ontologies are designed to contain concepts and relations only that describe asserted facts about the world, that they are not designed to handle uncertainty. The facts asserted are assumed to be 'true'. Therefore even information that is not certain to be 'true' are stored and lead to errors or inaccurate information. There is not a standard way to handle uncertainty currently (can read more on this).
They discuss how probability is used as a way to represent uncertainty in ontologies. They discuss some existing work in this area including (BayesOWL). The problem with this approach in particular is that it does not account for OWL properties, instances of the ontologies or the data types. There are also extensions to DL (Pronto is one of them), however performance is a problem. There are also Fuzzy approaches that exist.
They then discuss using DS. DS is presented as a generalized probability theory, however books related to this topic are not exactly is agreement with this representation. Masses are calculated and the sum of these masses make up the beliefs. It is also noted that is supports combining evidence from different sources which makes it especially useful for fusing data from different sources. They note work that actually use DS to handle the inconsistencies produced by mapping ontologies. However it also highlights a relevant paper that translates an OWL taxonomy into a directed evidential network.
The rest of the paper discusses their approach and how they use DS for modeling and reasoning. The point they make about uncertainty and why probabilistic methods can't represent it accurately is p-methods do not represent the absence of information very well. One needs to specify prior and conditional probabilities. They argue this leads to a minimax error due to is nature of symmetry prior probability assignment (.5) when information is not available. With DS missing information is not applied unless obtained indirectly. It allows one to specify a degree of ignorance (some define this as an upper and lower bound). They find this property to be appealing.
Probabilistic approaches use singletons only where DS allows one to use composites in addition to singletons. This is powerful. With probability theory there is a relationship between an event and its negation, DS does not imply a relationship between an event and its negation, it only models beliefs associated with a class.
They mention an additional point that I find makes this approach appealing. DS provides a way to combine evidence from different sources. This makes it especially useful for fusion.
They state, "the evidence theory is much more flexible than the probability theory". This is a strong statement and I'm not sure if it is completely true based on other papers that show how both Bayesian and DS can produce similar results.
Ok the paper ends with their approach. They discuss their proposed model which is an upper ontology representing the uncertainty. A DS_Concept which is a subclass of OWL:Thing has a DS_Mass, DS_Belief, DS_Plausibility, and a DS_Source. The Uncertain_Concept represents a concept that is part of the set. There is an object property is_either which has a range of owl:Thing so that all instances can be used.
This paper isn't cited by anyone else but I think there are good ideas proposed here and I am using this paper in my 601 work.
Saturday, April 24, 2010
Research Paper Summary - A General Data Fusion Architecture
H Carvalho, W Heinzelman, A Murphy, and C Coelho. A
general data fusion architecture. In Int. Conf. on Info. Fusion,
pages 1465–1472, 2003.
This is a short paper that describes an architecture for data fusion. What they are proposing is a taxonomy that defines 3 types of fusion: data oriented, variable oriented, and a mixture of the two. They are making a clear distinction between data as a measurement of the environment and variable as determined by feature extraction.
They describe examples of sensor data and state that the data needs to be pre-processed before fused. The pre-processing can involve conversions of a signal or filtering or handling noise. After pre-processing the data can be fused and they are proposing a 3-level data fusion framework. They begin by classifying the data as defined by the taxonomy. Basically when the fusion occurs defines what type of fusion we are dealing with (data, variable or mixture).
They go into a few examples of using this architecture. In general, the paper is not detailed enough to understand if the approach is viable. It is high level and short. It does provide additional information about the formalities of data fusion which is useful.
general data fusion architecture. In Int. Conf. on Info. Fusion,
pages 1465–1472, 2003.
This is a short paper that describes an architecture for data fusion. What they are proposing is a taxonomy that defines 3 types of fusion: data oriented, variable oriented, and a mixture of the two. They are making a clear distinction between data as a measurement of the environment and variable as determined by feature extraction.
They describe examples of sensor data and state that the data needs to be pre-processed before fused. The pre-processing can involve conversions of a signal or filtering or handling noise. After pre-processing the data can be fused and they are proposing a 3-level data fusion framework. They begin by classifying the data as defined by the taxonomy. Basically when the fusion occurs defines what type of fusion we are dealing with (data, variable or mixture).
They go into a few examples of using this architecture. In general, the paper is not detailed enough to understand if the approach is viable. It is high level and short. It does provide additional information about the formalities of data fusion which is useful.
Paper Summary - An Introduction to Multisensor Data Fusion
D. L. Hall and J. Llinas, editors. Handbook of Multisensor
Data Fusion. CRC Press, 2001.
This paper is based on the book and gives a general background in multisensor data fusion. It gives basic definitions, a bit of history and highlights types of applications that use multisensor data fusion techniques. This is most prevalent in military applications but commercial applications are also making use of fusing data from multiple sources. There are advantages in using a 'multi-sensor' approach, improved accuracy and estimates are better and in general there is a statistical advantage.
It goes on to provide some basic definitions and discusses examples of sensors (more related to military domain). What is interesting is the following:
"The most fundamental characterization of data fusion involves a hierarchical transformation between observed energy or parameters (provided by multiple sources as input) and a decision or inference (produced by fusion estimation and/or inference processes) regarding the location, characteristics, and identity of an entity, and an interpretation of the observed entity in the context of a
surrounding environment and relationships to other entities....
The transformation between observed energy or parameters and a decision or
inference proceeds from an observed signal to progressively more abstract concepts."
They go on to discuss methods which are to make identity estimations including Dempster-Shafer and Bayesian.
"Observational data may be combined, or fused, at a variety of levels from the raw data (or observation) level to a state vector level, or at the decision level."
It then talks in details about examples in military and non-military applications and then about the Joint Data Fusion Process Model which was established in 1986.
The rest of the paper goes into detail about the architecture.
Why is this important to my work?
There are aspects about true multi-sensor data fusion that can be adapted and used in fusing semantic web data. There are very similar issues involved. We get data about entities from different sources. This data can be complementary, certain sources can offer facts that other sources are not aware of and fusing this information together presents a more comprehensive picture of an entity. This is applicable which smushing FOAF instances (part of earlier work) and this is applicable which simply merging fact retrieved from different sources. One example in particular is new sources. Different facts can be exposed from different news sources. When you bring these facts together you get a more complete story.
This brings us to another issue with data fusion and that is conflict resolution. When we are combining sources sometimes the information can be in conflict with each other. This is an interesting problem.
This paper is a great way to get a good background in multi-sensor data fusion and one can use definitions, techniques, architectures and apply them to fusing Semantic Web data.
Data Fusion. CRC Press, 2001.
This paper is based on the book and gives a general background in multisensor data fusion. It gives basic definitions, a bit of history and highlights types of applications that use multisensor data fusion techniques. This is most prevalent in military applications but commercial applications are also making use of fusing data from multiple sources. There are advantages in using a 'multi-sensor' approach, improved accuracy and estimates are better and in general there is a statistical advantage.
It goes on to provide some basic definitions and discusses examples of sensors (more related to military domain). What is interesting is the following:
"The most fundamental characterization of data fusion involves a hierarchical transformation between observed energy or parameters (provided by multiple sources as input) and a decision or inference (produced by fusion estimation and/or inference processes) regarding the location, characteristics, and identity of an entity, and an interpretation of the observed entity in the context of a
surrounding environment and relationships to other entities....
The transformation between observed energy or parameters and a decision or
inference proceeds from an observed signal to progressively more abstract concepts."
They go on to discuss methods which are to make identity estimations including Dempster-Shafer and Bayesian.
"Observational data may be combined, or fused, at a variety of levels from the raw data (or observation) level to a state vector level, or at the decision level."
It then talks in details about examples in military and non-military applications and then about the Joint Data Fusion Process Model which was established in 1986.
The rest of the paper goes into detail about the architecture.
Why is this important to my work?
There are aspects about true multi-sensor data fusion that can be adapted and used in fusing semantic web data. There are very similar issues involved. We get data about entities from different sources. This data can be complementary, certain sources can offer facts that other sources are not aware of and fusing this information together presents a more comprehensive picture of an entity. This is applicable which smushing FOAF instances (part of earlier work) and this is applicable which simply merging fact retrieved from different sources. One example in particular is new sources. Different facts can be exposed from different news sources. When you bring these facts together you get a more complete story.
This brings us to another issue with data fusion and that is conflict resolution. When we are combining sources sometimes the information can be in conflict with each other. This is an interesting problem.
This paper is a great way to get a good background in multi-sensor data fusion and one can use definitions, techniques, architectures and apply them to fusing Semantic Web data.
Saturday, April 17, 2010
Paper Summary - An Introduction to Bayesian and Dempster-Shafer Data Fusion
D. Koks and S. Challa, DSTO Systems Sciences Laboratory, November 2005
http://www.dsto.defence.gov.au/publications/2563/DSTO-TR-1436.pdf
This paper is about data fusion and using techniques such as Bayesian and DS. It starts out with an introduction about data fusion and how it is defined in multiple domains. It then highlights work by others that implemented different methods to perform data fusion. It then gives nice detailed examples of using Bayesian and Dempster-Shafer to perform data fusion. It ends with a comparison summary of these two techniques.
The paper is very good. It shows all of the equations step by step and gives clear examples. It very clearly shows shortcoming of both methods.
http://www.dsto.defence.gov.au/publications/2563/DSTO-TR-1436.pdf
This paper is about data fusion and using techniques such as Bayesian and DS. It starts out with an introduction about data fusion and how it is defined in multiple domains. It then highlights work by others that implemented different methods to perform data fusion. It then gives nice detailed examples of using Bayesian and Dempster-Shafer to perform data fusion. It ends with a comparison summary of these two techniques.
The paper is very good. It shows all of the equations step by step and gives clear examples. It very clearly shows shortcoming of both methods.
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