An experiment in document retrieval using conceptual graphs

Genest, David; Chein, Michel

doi:10.1007/bfb0027893

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…In [12], the authors propose an indexing approach based on conceptual graphs for information retrieval. They mention the support of an ontology and define a valuation function between two conceptual graphs in order to calculate their semantic proximity.…”

Section: Discussion and Related Workmentioning

confidence: 99%

Ontology-supported and ontology-driven conceptual navigation on the World Wide Web

Crampes¹,

Ranwez²

2000

Proceedings of the Eleventh ACM on Hypertext and Hypermedia

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This paper presents the principles of ontology-supported and ontology-driven conceptual navigation. Conceptual navigation realizes the independence between resources and links to facilitate interoperability and reusability. An engine builds dynamic links, assembles resources under an argumentative scheme and allows optimization with a possible constraint, such as the user's available time. Among several strategies, two are discussed in detail with examples of applications. On the one hand, conceptual specifications for linking and assembling are embedded in the resource meta-description with the support of the ontology of the domain to facilitate meta-communication.Resources are like agents looking for conceptual acquaintances with intention. On the other hand, the domain ontology and an argumentative ontology drive the linking and assembling strategies.

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Section: Discussion and Related Workmentioning

confidence: 99%

Ontology-supported and ontology-driven conceptual navigation on the World Wide Web

Crampes¹,

Ranwez²

2000

Proceedings of the Eleventh ACM on Hypertext and Hypermedia

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“…Most semantic matchers [7,10,11,15,20] use only taxonomic knowledge -i.e. the subsumption relationships among concepts and relations.…”

Section: Semantic Matchingmentioning

confidence: 99%

Matching utterances to rich knowledge structures to acquire a model of the speaker's goal

Yeh

Porter

Barker

2005

Proceedings of the 3rd International Conference on Knowledge Capture

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An ultimate goal of AI is to build end-to-end systems that interpret natural language, reason over the resulting logical forms, and perform actions based on that reasoning. This requires systems from separate fields be brought together, but often this exposes representational gaps between them. The logical forms from a language interpreter may mirror the surface forms of utterances too closely to be usable as-is, given a reasoner's requirements for knowledge representations. What is needed is a system that can match logical forms to background knowledge flexibly to acquire a rich semantic model of the speaker's goal. In this paper, we present such a "matcher" that uses semantic transformations to overcome structural differences between the two representations. We evaluate this matcher in a MUC-like template-filling task and compare its performance to that of two similar systems.

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“…Structural subsumption is used as the matching mechanism. Works by [12,14,20,30] also use taxonomic knowledge to aid in matching. The query and resource are represented as conceptual graphs and allow for more expressiveness.…”

Section: Related Workmentioning

confidence: 99%

Using transformations to improve semantic matching

Yeh¹,

Porter²,

Barker³

2003

Proceedings of the International Conference on Knowledge Capture - K-Cap '03

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Many AI tasks require determining whether two knowledge representations encode the same knowledge. Solving this matching problem is hard because representations may encode the same content but differ substantially in form. Previous approaches to this problem have used either syntactic measures, such as graph edit distance, or semantic knowledge to determine the "distance" between two representations. Although semantic approaches outperform syntactic ones, previous research has focused primarily on the use of taxonomic knowledge. We show that this is not enough because mismatches between representations go largely unaddressed. In this paper, we describe how transformations can augment existing semantic approaches to further improve matching. We also describe the application of our approach to the task of critiquing military Courses of Action and compare its performance to other leading algorithms.

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An experiment in document retrieval using conceptual graphs

Cited by 14 publications

References 10 publications

Ontology-supported and ontology-driven conceptual navigation on the World Wide Web

Ontology-supported and ontology-driven conceptual navigation on the World Wide Web

Matching utterances to rich knowledge structures to acquire a model of the speaker's goal

Using transformations to improve semantic matching

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