Integrating Know-How into the Linked Data Cloud

Pareti, Paolo; Testu, Benoit; Ichise, Ryutaro; Klein, Ewan; Barker, Adam

doi:10.1007/978-3-319-13704-9_30

Cited by 12 publications

(16 citation statements)

References 11 publications

(11 reference statements)

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“…We have evaluated the quality of our automatic integration against existing integration efforts and showed its superiority across all the dimensions considered, such as the quantity and the precision of the links. For a more detailed account of this framework see the forthcoming paper by P. Pareti et al [4]. …”

Section: Resultsmentioning

confidence: 99%

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A Linked Data Approach to Know-How

Pareti

Testu

Ichise

et al. 2015

Lecture Notes in Computer Science

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Abstract. The Web is one of the major repositories of human generated know-how, such as step-by-step videos and instructions. This knowledge can be potentially reused in a wide variety of applications, but it currently suffers from a lack of structure and isolation from related knowledge. To overcome these challenges we have developed a Linked Data framework which can automate the extraction of know-how from existing Web resources and generate links to related knowledge on the Linked Data Cloud. We have implemented our framework and used it to extract a Linked Data representation of two of the largest know-how repositories on the Web. We demonstrate two possible uses of the resulting dataset of real-world know-how. Firstly, we use this dataset within a Web application to offer an integrated visualization of distributed knowhow resources. Lastly, we show the potential of this dataset for inferring common sense knowledge about tasks.

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Section: Resultsmentioning

confidence: 99%

“…Similarly to the DBpedia project [1], we focus our extraction on semi-structured resources. Semi-structured know-how resources, such as the articles available on WikiHow, 4 can be analyzed reliably, as they are explicitly divided into a number of steps, methods and requirements.…”

Section: Methodsmentioning

confidence: 99%

A Linked Data Approach to Know-How

Pareti

Testu

Ichise

et al. 2015

Lecture Notes in Computer Science

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“…Further support for our hypothesis is provided by the existence of large instructional websites that contain instructions with this level of structure. Evidence of this has been provided by a large scale conversion of over 200,000 instructions from the wikiHow and Snapguide 7 websites into an rdf format using the prohow vocabulary [7]. This existing structure can be extracted and represented in rdf.…”

Section: Know-how Acquisitionmentioning

confidence: 99%

“…This discovery process can be performed in different ways. In a previous experiment, we automated the creation of links between different sets of instructions from wikiHow (in the prohow format) using Natural Language Processing and Machine Learning [7]. This showed that creating links between sets of prohow instructions can be achieved with high accuracy.…”

Section: Know-how Linkingmentioning

confidence: 99%

Linking Data, Services and Human Know-How

Pareti

Klein

Barker

2016

The Semantic Web. Latest Advances and New Domains

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Abstract. An increasing number of everyday tasks involve a mixture of human actions and machine computation. This paper presents the first framework that allows non-programmer users to create and execute workflows where each task can be completed by a human or a machine. In this framework, humans and machines interact through a shared knowledge base which is both human and machine understandable. This knowledge base is based on the prohow Linked Data vocabulary that can represent human instructions and link them to machine functionalities. Our hypothesis is that non-programmer users can describe how to achieve certain tasks at a level of abstraction which is both human and machine understandable. This paper presents the prohow vocabulary and describes its usage within the proposed framework. We substantiate our claim with a concrete implementation of our framework and by experimental evidence.

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“…The above shortcoming is evident even in conceptually simpler KBs. For instance, the Web of Know-How dataset 5 [21] contains activities and instructions collected from Wi-kiHow and Snapguide. Although the vocabulary used to represent this knowledge is relatively simple (PROHOW 6 ), the generated instantiations encapsulate rich axiomatizations.…”

Section: Mereology Patternmentioning

confidence: 99%

Question Answering over Pattern-Based User Models

Meditskos

Dasiopoulou²,

Vrochidis

et al. 2016

Proceedings of the 12th International Conference on Semantic Systems

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In this paper we present an ontology-driven framework for natural language question analysis and answering over user models (e.g. preferences, habits and health problems of individuals) that are formally captured using ontology design patterns. Pattern-based modelling is extremely useful for capturing n-ary relations in a well-defined and axiomatised manner, but it introduces additional challenges in building NL interfaces for accessing the underlying content. This is mainly due to the encapsulation of domain semantics inside conceptual layers of abstraction (e.g. using reification or container classes) that demand flexible, context-aware approaches for query analysis and interpretation. We describe the coupling of a frame-based formalisation of natural language user utterances with a context-aware query interpretation towards question answering over pattern-based RDF knowledge bases. The proposed framework is part of a human-like socially communicative agent that acts as an intermediate between elderly migrants and care personnel, assisting the latter to solicit personal information about care recipients (e.g. medical history, care needs, preferences, routines, habits, etc.).

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Integrating Know-How into the Linked Data Cloud

Cited by 12 publications

References 11 publications

A Linked Data Approach to Know-How

A Linked Data Approach to Know-How

Linking Data, Services and Human Know-How

Question Answering over Pattern-Based User Models

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