Towards the Domain Agnostic Generation of Natural Language Explanations from Provenance Graphs for Casual Users
As more systems become PROV-enabled, there will be a corresponding increase in the need to communicate provenance data directly to users. Whilst there are a number of existing methods for doing this — formally, diagrammatically, and textually — there are
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Abstract. As more systems become PROV-enabled, there will be a corresponding increase in the need to communicate provenance data directly to users. Whilst there are a number of existing methods for doing this — formally, diagrammatically, and textually — there are currently no application-generic techniques for generating linguistic explanations of provenance. The principal reason for this is that a certain amount of linguistic information is required to transform a provenance graph — such as in PROV — into a textual explanation, and if this information is not available as an annotation, this transformation is presently not possible. In this paper, we describe how we have adapted the common ‘consensus’ architecture from the field of natural language generation to achieve this graph transformation, resulting in the novel PROVglish architecture. We then present an approach to garnering the necessary linguistic information from a PROV dataset, which involves exploiting the linguistic information informally encoded in the URIs denoting provenance resources. We finish by detailing an evaluation undertaken to assess the effectiveness of this approach to lexicalisation, demonstrating a significant improvement in terms of fluency, comprehensibility, and grammatical correctness.
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Introduction
As organisations begin to understand the value of storing and utilising PROV data [13], they will increasingly find scenarios where it is useful to show that data to their users. Where resources allow, the best interfaces to this data will likely be bespoke creations, tailored to the specific needs of the application. However, we speculate that in many cases the resources will not be made available to take this approach, motivating the search for an application-generic way of communicating provenance to casual users. In this vein, there are already a number of different ways for communicating PROV data to human users in formal [14], diagrammatic [5,17], and linguistic forms [16]. The utility of these various approaches depends on a number of factors but, perhaps, most importantly the user and their familiarity with the intricacies of both PROV and the application context. For example, whilst it is c Springer International Publishing Switzerland 2016 M. Mattoso and B. Glavic (Eds.): IPAW 2016, LNCS 9672, pp. 95–106, 2016. DOI: 10.1007/978-3-319-40593-3 8
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D.P. Richardson and L. Moreau
a very useful tool in a suitable context, it would not be appropriate to use the PROV-N notation to communicate with the vast majority of users. Likewise, the diagrammatic forms of representing PROV are also potentially inaccessible to many users who would perhaps have difficulty understanding mathematical graphs. A competent speaker of a particular language, on the other hand, is presumably far more likely to understand a well-worded provenance explanation, than understand a diagrammatic representation in a format that they have not previously encountered. Linguistic interfaces are of further use in contexts where a visual interface might be inappropria
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