Gathering lexical linked data and knowledge patterns from FrameNet

Nuzzolese, Andrea Giovanni; Gangemi, Aldo; Presutti, Valentina

doi:10.1145/1999676.1999685

Cited by 40 publications

(37 citation statements)

References 6 publications

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“…These KPs are close to EKPs, but although some user-study proved that their use is beneficial in ontology design [3], yet they miss some of the aspects that we study here: evaluation of their cognitive soundness, and adequacy to provide access to large-scale linked data. [14] presents a resource of KPs derived from a lexical resource i.e., FrameNet [2]. In future work, we plan a compared analysis between EKPs and other KPs.…”

Section: Related Workmentioning

confidence: 99%

Encyclopedic Knowledge Patterns from Wikipedia Links

Nuzzolese

Gangemi

Presutti

et al. 2011

The Semantic Web – ISWC 2011

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Abstract.What is the most intuitive way of organizing concepts for describing things? What are the most relevant types of things that people use for describing other things? Wikipedia and Linked Data offer knowledge engineering researchers a chance to empirically identifying invariances in conceptual organization of knowledge i.e. knowledge patterns. In this paper, we present a resource of Encyclopedic Knowledge Patterns that have been discovered by analyizing the Wikipedia page links dataset, describe their evaluation with a user study, and discuss why it enables a number of research directions contributing to the realization of a meaningful Semantic Web.

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Section: Related Workmentioning

confidence: 99%

Encyclopedic Knowledge Patterns from Wikipedia Links

Nuzzolese

Gangemi

Presutti

et al. 2011

The Semantic Web – ISWC 2011

Self Cite

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“…This modeling approach and its founding theories are extensively described in [15,39,32]. However, an example may be useful to convey the intuition behind the theory.…”

Section: Definition 2 (Frame-based Graph)mentioning

confidence: 99%

“…The core component of the system is FRED [39], a Semantic Web machine reader able to produce a RDF/OWL frame-based representation of a text. It integrates the output of several NLP tools, enriches and transform it by reusing Linguistic Frames [32], Ontology Design Patterns [20], open data, and various vocabularies. FRED detects events, roles, and n-ary relations and represent in a RDF/OWL graph.…”

Section: Legalo Pipeline and Componentsmentioning

confidence: 99%

“…The confidence measure is provided by CrowdFlower, it measures the inter-rater agreement between workers weighted by their trust values, hence indicating both agreement and quality of judgements at the same time. It is computed as described in Definition 5 32 , and an example is given in Example 5.1: t k the sum of trust scores of raters giving judgements on u, and trust(j i ) the sum of t k values of raters that choose judgement j i , the confidence score conf idence(j i , u) for judgement j i on the task unit u is computed as follows:…”

Section: Hypothesis 2 (Usable Predicate Generation)mentioning

confidence: 99%

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From hyperlinks to Semantic Web properties using Open Knowledge Extraction

Presutti

Nuzzolese

Consoli

et al. 2016

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Abstract. Open information extraction approaches are useful but insufficient alone for populating the Web with machine readable information as their results are not directly linkable to, and immediately reusable from, other Linked Data sources. This work proposes a novel paradigm, named Open Knowledge Extraction, and its implementation (Legalo) that performs unsupervised, open domain, and abstractive knowledge extraction from text for producing directly usable machine readable information. The implemented method is based on the hypothesis that hyperlinks (either created by humans or knowledge extraction tools) provide a pragmatic trace of semantic relations between two entities, and that such semantic relations, their subjects and objects, can be revealed by processing their linguistic traces (i.e. the sentences that embed the hyperlinks) and formalised as Semantic Web triples and ontology axioms. Experimental evaluations conducted on validated text extracted from Wikipedia pages, with the help of crowdsourcing, confirm this hypothesis showing high performances. A demo is available at http://wit.istc.cnr.it/ stlab-tools/legalo.

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“…Publishing Lexica as Linked Data The need to publish lexical resources as linked data has been recognized recently [16]. Although principles for publishing RDF as Linked Data are already well established [1], the choice of identifiers and first-class objects is crucial for any linking approach.…”

Section: Open Research Questionsmentioning

confidence: 99%

Leveraging the Crowdsourcing of Lexical Resources for Bootstrapping a Linguistic Data Cloud

Hellmann

Brekle

Auer

2013

Semantic Technology

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Abstract. We present a declarative approach implemented in a comprehensive open-source framework based on DBpedia to extract lexicalsemantic resources -an ontology about language use -from Wiktionary. The data currently includes language, part of speech, senses, definitions, synonyms, translations and taxonomies (hyponyms, hyperonyms, synonyms, antonyms) for each lexical word. Main focus is on flexibility to the loose schema and configurability towards differing language-editions of Wiktionary. This is achieved by a declarative mediator/wrapper approach. The goal is to allow the addition of languages just by configuration without the need of programming, thus enabling the swift and resource-conserving adaption of wrappers by domain experts. The extracted data is as fine granular as the source data in Wiktionary and additionally follows the lemon model. It enables use cases like disambiguation or machine translation. By offering a linked data service, we hope to extend DBpedia's central role in the LOD infrastructure to the world of Open Linguistics.

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Gathering lexical linked data and knowledge patterns from FrameNet

Cited by 40 publications

References 6 publications

Encyclopedic Knowledge Patterns from Wikipedia Links

Encyclopedic Knowledge Patterns from Wikipedia Links

From hyperlinks to Semantic Web properties using Open Knowledge Extraction

Leveraging the Crowdsourcing of Lexical Resources for Bootstrapping a Linguistic Data Cloud

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