NEFCIS: Neuro-fuzzy Concept Based Inference System for Specification Mining

Shankar, Arunprasath; Singh, Bhanu Pratap; Wolff, Francis; Papachristou, C.

doi:10.1109/ictai.2013.58

Cited by 2 publications

(4 citation statements)

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“…The priority index of a node represents the relative importance of the node (word) in design spec with respect to all specs in domain repository (corpus). The proximity score between nodes depicts the closeness between the nodes based word/sentence distance matrices and other deciding factors [19]. The ontology generation process is shown by step (d) of fig.…”

Section: Candidate Entity Extractionmentioning

confidence: 99%

“…2. The concept is weighted using two scores namely priority index, η and proximity score Ψ [19]. The priority index of a node represents the relative importance of the node (word) in design spec with respect to all specs in domain repository (corpus).…”

Section: Candidate Entity Extractionmentioning

confidence: 99%

“…Fuzzy sets are generated with the help of word bags, since every word can belong to multiple attributes depending on its attribute membership. The process is supplemented by a fuzzy clustering mechanism [19]. In fuzzy clustering, spec words can belong to more than one cluster, and associated with each word is a band of memberships, which indicate the strength of the association between that word and a particular cluster.…”

Section: Candidate Entity Extractionmentioning

confidence: 99%

“…For extracting candidate entities, we use a neuro-fuzzy concept-based inference system (NEFCIS) designed as a concept mining tool [19]. NEFCIS is capable of extracting keywords in spec documents as concept graphs.…”

Section: Candidate Entity Extractionmentioning

confidence: 99%

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Ontology-guided Conceptual Analysis of Design Specifications

Shankar

Singh

Wolff

et al. 2014

Proceedings of the 51st Annual Design Automation Conference

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The integration of reusable IP blocks/cores is a common process in system-on-chip design and involves manually comparing/mapping IP specifications against system requirements. The informal nature of specification limits its automatic analysis. Existing techniques fail to utilize the underlying conceptual information embedded in specifications. In this paper, we present a methodology for specification analysis, which involves concept mining of specifications to generate domain ontologies. We employ a semi-supervised expert system with semantic analysis capability to create a collaborative framework for cumulative knowledge acquisition. Our system then uses the generated ontologies to perform component retrieval, drop-in-replacement analysis and design vs. test-plan comparisons. We demonstrate our approach by evaluating several IP specifications.

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Section: Candidate Entity Extractionmentioning

confidence: 99%

Section: Candidate Entity Extractionmentioning

confidence: 99%

Section: Candidate Entity Extractionmentioning

confidence: 99%

Section: Candidate Entity Extractionmentioning

confidence: 99%

See 2 more Smart Citations