Structured Knowledge Source Integration and its applications to information fusion

Masters, James S.

doi:10.1109/icif.2002.1020968

Cited by 14 publications

(7 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Whilst the latter have already given up on being universally applicable, Cyc's vision is to explicitly 'assimilate' various data sources and thus to "extend the flexibility and power of the Cyc product to serve as the universal ontology and knowledge repository in any application requiring knowledge based reasoning" [135]. The belief that such an approach can work might be summarised by a quote attributed to Lenat: "Intelligence is ten million rules.…”

Section: From Universal Common Sense To Domain Ontologymentioning

confidence: 98%

Carnap, Goguen, and the Hyperontologies: Logical Pluralism and Heterogeneous Structuring in Ontology Design

2010

View full text Add to dashboard Cite

This paper addresses questions of universality related to ontological engineering, namely aims at substantiating (negative) answers to the following three basic questions: (i) Is there a 'universal ontology' ?, (ii) Is there a 'universal formal ontology language' ?, and (iii) Is there a universally applicable 'mode of reasoning' for formal ontologies? To support our answers in a principled way, we present a general framework for the design of formal ontologies resting on two main principles: firstly, we endorse Rudolf Carnap's principle of logical tolerance by giving central stage to the concept of logical heterogeneity, i.e. the use of a plurality of logical languages within one ontology design. Secondly, to structure and combine heterogeneous ontologies in a semantically wellfounded way, we base our work on abstract model theory in the form of institutional semantics, as forcefully put forward by Joseph Goguen and Rod Burstall. In particular, we employ the structuring mechanisms of the heterogeneous algebraic specification language HetCasl for defining a general concept of heterogeneous, distributed, highly modular and structured ontologies, called hyperontologies. Moreover, we distinguish, on a structural and semantic level, several different kinds of combining and aligning heterogeneous ontologies, namely integration, connection, and refinement. We show how the notion of heterogeneous refinement can be used to provide both a general notion of sub-ontology as well as a notion of heterogeneous equivalence of ontologies, and finally sketch how different modes of reasoning over ontologies are related to these different structuring aspects.Mathematics Subject Classification (2010). Primary 68T30; Secondary 03C95.

show abstract

Section: From Universal Common Sense To Domain Ontologymentioning

confidence: 98%

Carnap, Goguen, and the Hyperontologies: Logical Pluralism and Heterogeneous Structuring in Ontology Design

2010

View full text Add to dashboard Cite

show abstract

“…Although it contains some linguistic and extensional information, this information is limited. We believe it can be extended with new linguistic information (for example, with WordNet) [23,24], and new extensional information, such as using the web to populate the ontology [25]. Figure 2 shows some of the most important topics of ACTION and the number of subtypes they have.…”

Section: Action: the Actions Ontologymentioning

confidence: 99%

Ontology Driven Requirements Query

Conesa

Kim

2007

2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07)

View full text Add to dashboard Cite

Use cases are commonly used to represent customers' requirements during systems development. In a large software development environment, finding a relevant use case from a large use case library created in the past or related projects is a complex, error-prone and expensive task. Based on the semantic web approach, we propose an ontological methodology to support this task. We use an ontology to augment use cases with semantic information. This ontology is derived from ResearchCyc ontology. We also propose the augmentation of queries used to retrieve use cases with this ontology. We present this approach to better capture, reuse and query use cases .

show abstract

“…Knowledge fusion technology supports intelligent query-answering systems aiming at providing the users with nonrecurring, consistent, and unambiguous answers to their query under redundancy/lack of information (e.g., [23][24][25][26][27]. Some research efforts deal with knowledge fusion of multiple knowledge sources to build a new knowledge base [28][29][30][31]. Several knowledge fusion efforts are devoted to knowledge integration with the problem-solving purposes [27,[32][33][34][35].…”

mentioning

confidence: 99%

Context-Aware Knowledge Fusion for Decision Support

Smirnov

Levashova

Shilov

2016

Context-Enhanced Information Fusion

View full text Add to dashboard Cite

The purpose of this chapter is to investigate knowledge fusion processes with reference to context-aware decision support. Various knowledge fusion processes and their possible outcomes are analyzed. A context-aware decision support system for emergency management serves as a possible application in which knowledge fusion processes go on. This system provides fused outputs from different knowledge sources. It relies upon context model, which is the key to fuse information/knowledge and to generate useful decisions. The discussion is complemented by examples from a fire response scenario.Keywords Context-aware decision support Á Constraint-based ontology Á Information fusion model Á Knowledge fusion Á Emergency management Á Fire response IntroductionInformation fusion deals with various forms of information integration (aggregation, union, merging, etc.) from multiple sources. Some time ago, sources of data and information (databases, sensors, etc.) [1] due to observed shortcomings. Currently, this model is the most popular among other information fusion models. Initially proposed for the military applications, now it is widely used in civil domains as well, such as business or medicine. The levels with the JDL/DFG model are: source pre-processing/subject assessment (level 0), object assessment (level 1), situation assessment (level 2), impact assessment/threat refinement (level 3), process refinement (level 4), and user refinement/cognitive refinement (level 5). Through its different levels, the model divides the fusion processes according to the different levels of abstraction of the data fused and the different problems the data fusion is applicable to (e.g., characteristic estimation vs. situation recognition and analysis). The model does not prescribe a strict ordering of the processes and the fusion levels, and the levels are not always discrete and may overlap. The JDL/DFG model is useful for visualizing the data fusion process, facilitating discussion and common understanding, and important for system-level information fusion design [2].Recently, research on information fusion extended the set of input sources with ontologies, text documents, the web, etc. That is, the focus of data and information fusion has changed to knowledge fusion.Information fusion and knowledge fusion are tightly related [3]. Referring to various perspectives on information fusion (e.g., [2,[4][5][6][7][8]), this technology is aimed at facilitation of situation awareness and improvement of decision making. The main result of information fusion is a new meaningful piece of information that is beneficial to decision making in less uncertainty, more preciseness, and/or more comprehensibility then the contributing parts [9][10][11].The objective of knowledge fusion is to integrate information and knowledge from multiple sources into some common knowledge that may be used for decision making and problem solving or may provide a better insight and understanding of the situation under consideration [12][13][14][15]. Knowledge f...

show abstract

Structured Knowledge Source Integration and its applications to information fusion

Cited by 14 publications

References 3 publications

Carnap, Goguen, and the Hyperontologies: Logical Pluralism and Heterogeneous Structuring in Ontology Design

Carnap, Goguen, and the Hyperontologies: Logical Pluralism and Heterogeneous Structuring in Ontology Design

Ontology Driven Requirements Query

Context-Aware Knowledge Fusion for Decision Support

Contact Info

Product

Resources

About