# Towards Interactive Analytics over RDF Graphs
## 1 Introduction  
The amount of data available on the Web today is increasing rapidly due to successful initiatives, such as the Linked Open Data movement ([[http://lod-cloud.net/](http://lod-cloud.net/)]([http://lod-cloud.net/](http://lod-cloud.net/))). More and more data sources are being exported or produced using the Resource Description Framework ([[https://www.w3.org/RDF/](https://www.w3.org/RDF/)]([https://www.w3.org/RDF/](https://www.w3.org/RDF/))) (or RDF, for short) standardized by the W3C. There are thousands of published RDF datasets (see [1] for a recent survey), including cross-domain knowledge bases (KBs) (e.g., DBpedia [2] and Wikidata [3]), domain specific repositories (e.g., DrugBank [4], GRSF [5], ORKG [6], WarSampo [7], and recently COVID-19 related datasets [8, 9, 10] as well as Markup data through schema.org. Figure 1 shows the general picture of access services over RDF. Apart from Structured Query Languages, we have Keyword Search systems over RDF (like [11]) that allow users to search for information using the familiar method they use for Web searching. We can also identify the category _Interactive Information Access_ that refers to access methods that are beyond the simple \"query-and-response\" interaction, i.e., methods that offer more interaction options to the user and also exploit the _interaction session_. In this category, there are methods for RDF Browsing, methods for Faceted Search over RDF [12], as well as methods for Assistive (SPARQL) Query Building (e.g., [13]). Our work falls in this category, specifically we aim at providing an interactive method for analytics over RDF. Finally, in the category _natural language interfaces_ there are methods for question answering, dialogue systems, and conversational interfaces. As regards structured query languages, RDF data are mainly queried through structured query languages, i.e., SPARQL ([[https://www.w3.org/TR/rdf-sparql-query/](https://www.w3.org/TR/rdf-sparql-query/)]([https://www.w3.org/TR/rdf-sparql-query/](https://www.w3.org/TR/rdf-sparql-query/))), which is the standard query language for RDF data. SPARQL supports complex querying using regular path expressions, grouping, aggregation, etc., but the application of analytics to RDF data and especially to large RDF graphs is not so straightforward. The structure ofsuch graphs tends to be complex due to several factors: (i) different resources may have different sets of properties, (ii) properties can be multi-valued (i.e., there can be triples where the subject and predicate are the same but the objects are different) and (iii) resources may or may not have types. On the other hand, the regular methods of analytics are not capable of analyzing RDF graphs effectively, as they (i) focus on relational data, (ii) can only work with a single homogeneous data set, (iii) neither support multiple central concepts, nor RDF semantics, (iv) do not offer flexible choices of dimension, measure, and aggregation and (v) demand deep knowledge of specific query languages depending on data's structure. In view of the above challenges, there is a need for a simple conceptual model able to guide data analysis over one or more linked data sets that demands no programming skill. Motivated from this need, we are investigating an approach based on a high-level query language, called HIFUN [14], for applying analytics to RDF graphs. We study how that language can be applied to RDF data by clarifying how the concept of analysis context can be defined, what kind of transformations are required and how HIFUN queries are translated to SPARQL. Please note that with the translation approach that we focus on, we can apply analytics to RDF sources, without having to transform the RDF data to relational ones, nor to copy them. The idea was first introduced in [15].