Towards the Automatic Detection and Correction of Errors in Automatically Constructed Ontologies

Gkaniatsou, Andriana; Bundy, Alan; McNeill, Fiona

doi:10.1109/sitis.2012.129

Cited by 3 publications

(3 citation statements)

References 7 publications

(10 reference statements)

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“…Concept Formation/Reformation: An important way in which new concepts are formed is by the evolution of existing concepts that have proved inadequate: Such inadequacies are often revealed by failures of inference using the old concepts. Researchers lately explored how these inadequacies can trigger conceptual change in different domains as, e.g., physics [17] or in ontologies [12].…”

Section: Structure and Methodsmentioning

confidence: 99%

Anchoring Knowledge in Interaction: Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition

Besold

Kühnberger

Garcez

et al. 2015

Artificial General Intelligence

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Section: Structure and Methodsmentioning

confidence: 99%

Anchoring Knowledge in Interaction: Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition

Besold

Kühnberger

Garcez

et al. 2015

Artificial General Intelligence

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“…By way of illustration, in a recent mini-project to detect errors in the automatically constructed KnowItAll ontology [Etzioni et al, 2011], we discovered triples for the capital of Japan with the following answers: Tokyo, Kyoto and Paris [Gkaniatsou et al, 2012]. "Tokyo" is the correct answer; "Paris" came from a tutor on logic and exemplified a false assertion; "Kyoto" used to be the capital of Japan but was curated to the present tense as a side effect of retrieval.…”

Section: Lesson 3: Curation Must Be Dynamicmentioning

confidence: 99%

Solving guesstimation problems using the Semantic Web: Four lessons from an application

2015

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We draw on our experience of implementing a semi-automated guesstimation application of the Semantic Web, gort, to draw four lessons, which we claim are of general applicability. These are: 1. Inference can unleash the Semantic Web: The full power of the web will only be realised when we can use it to infer new knowledge from old. 2. The Semantic Web does not constrain the inference mechanisms: Since we must anyway curate the knowledge we extract from the web, we can take the opportunity to translate it into what ever representational formalism is most appropriate for our application. This also enables the use of whatever inference mechanism is most appropriate. 3. Curation must be dynamic: Static curation is not only infeasible due to the size and growth rate of the Semantic Web, but curation must be application-specific. 4. Own up to uncertainty: Since the Semantic Web is, by design, uncontrolled, the accuracy of knowledge extracted from it cannot be guaranteed. The resulting uncertainty must not be hidden from the user, but must be made manifest. * The research reported in this paper was supported by ONR project N000140910467 and EPSRC project EP/J001058/1. We would like to thank two SWJ referees: Simon Scheider and an anonymous referee. 1 This is an estimate of the number of original English assertions that have been correctly formalised, not the accuracy of the original source information.

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“…Binary resolution needs to be supplemented with the factorization rule. 3 Factorization merges several literals in a clause into one by applying their most general unifier to the clause, so that these literals become identical. The combination of binary resolution and factorization is complete, meaning that if a conjecture is provable from some axioms, then these two rules will find a refutation, i.e.…”

Section: Definition 16 (Binary Resolution)mentioning

confidence: 99%

The interaction of representation and reasoning

Bundy

2013

Proc. R. Soc. A.

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Automated reasoning is an enabling technology for many applications of informatics. These applications include verifying that a computer program meets its specification; enabling a robot to form a plan to achieve a task and answering questions by combining information from diverse sources, e.g. on the Internet, etc. How is automated reasoning possible? Firstly, knowledge of a domain must be stored in a computer, usually in the form of logical formulae. This knowledge might, for instance, have been entered manually, retrieved from the Internet or perceived in the environment via sensors, such as cameras. Secondly, rules of inference are applied to old knowledge to derive new knowledge. Automated reasoning techniques have been adapted from logic, a branch of mathematics that was originally designed to formalize the reasoning of humans, especially mathematicians. My special interest is in the way that representation and reasoning interact. Successful reasoning is dependent on appropriate representation of both knowledge and successful methods of reasoning. Failures of reasoning can suggest changes of representation. This process of representational change can also be automated. We will illustrate the automation of representational change by drawing on recent work in my research group.

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Towards the Automatic Detection and Correction of Errors in Automatically Constructed Ontologies

Cited by 3 publications

References 7 publications

Anchoring Knowledge in Interaction: Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition

Anchoring Knowledge in Interaction: Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition

Solving guesstimation problems using the Semantic Web: Four lessons from an application

The interaction of representation and reasoning

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