Converting Semantic Meta-knowledge into Inductive Bias

Cabral, John; Kahlert, Robert C.; Matuszek, Cynthia; Witbrock, Michael; Summers, Brett

doi:10.1007/11536314_3

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…In the longer term, the prospects for increasingly automated knowledge acquisition seem bright. We have been working on automated rule learning over large conceptual and relational vocabularies (Cabral et al 2005, Curtis et al 2009), and are participating in the DARPA Machine Reading Program, in support of this goal.…”

Section: Conclusion and Next Stepsmentioning

confidence: 99%

Harnessing Cyc to Answer Clinical Researchers' Ad Hoc Queries

et al. 2010

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By extending Cyc’s ontology and KB approximately 2%, Cycorp and Cleveland Clinic Foundation (CCF) have built a system to answer clinical researchers’ ad hoc queries. The query may be long and complex, hence only partially understood at first, parsed into a set of CycL (higher-order logic) fragments with open variables. But, surprisingly often, after applying various constraints (medical domain knowledge, common sense, discourse pragmatics, syntax), there is only one single way to fit those fragments together, one semantically meaningful formal query P. The system, SRA (for Semantic Research Assistant), dispatches a series of database calls and then combines, logically and arithmetically, their results into answers to P. Seeing the first few answers stream back, the user may realize that they need to abort, modify, and re-ask their query. Even before they push ASK, just knowing approximately how many answers would be returned can spark such editing. Besides real-time ad hoc query-answering, queries can be bundled and persist over time. One bundle of 275 queries is rerun quarterly by CCF to produce the procedures and outcomes data it needs to report to STS (Society of Thoracic Surgeons, an external hospital accreditation and ranking body); another bundle covers ACC (American College of Cardiology) reporting. Until full articulation/answering of precise, analytical queries becomes as straight-forward and ubiquitous as text search, even partial understanding of a query empowers semantic search over semi-structured data (ontology-tagged text), avoiding many of the false positives and false negatives that standard text searching suffers from.

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Section: Conclusion and Next Stepsmentioning

confidence: 99%

Harnessing Cyc to Answer Clinical Researchers' Ad Hoc Queries

et al. 2010

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“…Rules from the RIF perspective would allow the integration, transformation and derivation of data from numerous sources in a distributed, scalable, and transparent manner. Because of the great variety in rule languages and rule engine technologies, RIF consists of a core language 3 to be used along with a set of standard and non-standard extensions. These extensions need not all be combinable into a single unified language.…”

Section: Introductionmentioning

confidence: 99%

“…These proposals try to overcome the difficulties of accommodating ontologies in Relational Learning. The work of [3] on using semantic meta-knowledge from Cyc as inductive bias in an ILP system is another attempt at solving this problem though more empirically. Conversely, we promote an extension of Relational Learning, called Onto-Relational Learning (ORL), which accounts for ontologies in a clear, elegant and well-founded manner by resorting to onto-relational rule languages.…”

Section: Introductionmentioning

confidence: 99%

Learning Onto-Relational Rules with Inductive Logic Programming

Lisi

2012

Preprint

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Rules complement and extend ontologies on the Semantic Web. We refer to these rules as onto-relational since they combine DL-based ontology languages and Knowledge Representation formalisms supporting the relational data model within the tradition of Logic Programming and Deductive Databases. Rule authoring is a very demanding Knowledge Engineering task which can be automated though partially by applying Machine Learning algorithms. In this chapter we show how Inductive Logic Programming (ILP), born at the intersection of Machine Learning and Logic Programming and considered as a major approach to Relational Learning, can be adapted to Onto-Relational Learning. For the sake of illustration, we provide details of a specific Onto-Relational Learning solution to the problem of learning rule-based definitions of DL concepts and roles with ILP.

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“…According to the World Wide Web Consortium (W3C), in the future, the Internet may become a vast and complex global knowledge base known as the Semantic Web (Antoniou & van Harmelen, 2008;Davies, Studer, & Warren, 2006;Cabral, Kahlert, & Matuszek, 2005).…”

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confidence: 99%

Knowledge Bases Over Algebraic Models

Knyazhansky

Plotkin

2012

International Journal of Knowledge Management

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This paper discusses the informational equivalence problem for knowledge bases. The authors show that using some mathematical approach it is possible to attack this problem and end up with an implementable algorithm. An essential part of the paper is devoted to the explanation of the mathematical idea which stands behind this algorithm. The authors try to do that in common terms or, at least, in less formal terms. In the second part of the paper mathematical methods are applied to study the properties of automorphic equivalence of knowledge bases (multi-models) and show that this notion is much wider than the total isomorphism (identity) of knowledge bases. In order to make the paper self-contained, the reader is provided with the formal definition of a knowledge base. Further development of the theoretical approach presented in the paper can lead to practical applications. For example, it can be used for preventing duplication of information in knowledge bases and in other tasks of improving knowledge management.

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Converting Semantic Meta-knowledge into Inductive Bias

Cited by 3 publications

References 4 publications

Harnessing Cyc to Answer Clinical Researchers' Ad Hoc Queries

Harnessing Cyc to Answer Clinical Researchers' Ad Hoc Queries

Learning Onto-Relational Rules with Inductive Logic Programming

Knowledge Bases Over Algebraic Models

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