The Web of Know-How
A Linked Data framework for human tasks and procedures

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Know-how is a fundamental aspect of human knowledge, and a large amount of it is represented as Web resources, such as step-by-step instructions and procedures. Despite their importance, those resources are difficult to access and reuse, due to their lack of structure and isolation from other related resources. To overcome these limitations, we extend to know-how the benefits that Linked Data has already brought to representing, retrieving and reusing declarative knowledge.

Our framework can represent generic know-how as Linked Data and automatically acquire this representation from existing resources on the Web. This system also allows the automatic generation of links between different know-how resources, and between those resources and other online knowledge bases, such as DBpedia.


The Human Activities Dataset (Base+Multilingual)

The Human Activities Dataset we have created describes 211,696 human activities from many different domains. These activities are decomposed into 2,609,236 entities (each with an English textual label). These entities represent over two million actions and half a million pre-requisites. Actions are interconnected both according to their dependencies (temporal/logical orders between actions) and decompositions (decomposition of complex actions into simpler ones). This dataset has been integrated with DBpedia (259,568 links). More information about this dataset is available in the publications listed below. This dataset is available on Datahub and GitHub. A multilingual version of the dataset, containing 800K+ sets of instructions in 16 different languages is available on Kaggle.

PROHOW Linked Data Vocabulary

Our Linked Data vocabulary to represent know-how and the execution of tasks is available online. It is dereferenceable, and it can be retrieved both in human readable and RDF format (Turtle and RDF/XML).

PROHOW Instruction Parser

The PROHOW Instruction Parser allows you to parse semi-structured natural language instructions into RDF data following the PROHOW vocabulary and data model. This editor currently supports only simple lists of steps, methods and requirements and cannot parse more complex PROHOW workflows.


HowLinks offers an integrated visualization of instructions coming from distributed repositories. Related entities and instructions are linked together, and they can be easily explored expanding the nodes of a tree-like structure. The HowLinks application has been developed in collaboration with the National Institute of Informatics (NII), Tokyo.

Selected Publications

  • Paolo Pareti, Ewan Klein and Adam Barker. Linking Data, Services and Human Know-How. The Semantic Web. Latest Advances and New Domains. ()
    PDF BibTex ESWC 2016
    DOI: 10.1007/978-3-319-34129-3_31

  • Paolo Pareti, Benoit Testu, Ryutaro Ichise, Ewan Klein and Adam Barker. Integrating Know-How into the Linked Data Cloud. Knowledge Engineering and Knowledge Management, volume 8876 of Lecture Notes in Computer Science ()
    PDF BibTex EKAW 2014
    DOI: 10.1007/978-3-319-13704-9_30

  • Paolo Pareti. Distributed Linked Data as a Framework for Human-Machine Collaboration. Proceedings of the 7th International Workshop on Consuming Linked Data (COLD). ()
    PDF BibTex COLD 2016

  • Paolo Pareti, Ewan Klein and Adam Barker. A Semantic Web of Know-How: Linked Data for Community-Centric Tasks. Proceedings of the companion publication of the 23rd International Conference on World Wide Web ()
    PDF BibTex WI&C
    DOI: 10.1145/2567948.2578846