Keyword Search over RDF Using Document-Centric Information Retrieval Systems

Abstract

For ordinary users, the task of accessing knowledge graphs through structured query languages like SPARQL is rather demanding. As a result, various approaches exploit the simpler and widely used keyword-based search paradigm, either by translating keyword queries to structured queries, or by adopting classical information retrieval (IR) techniques. In this paper, we study and adapt Elasticsearch, an out-ofthe-box document-centric IR system, for supporting keyword search over RDF datasets. Contrary to other works that mainly retrieve entities, we opt for retrieving triples, due to their expressiveness and informativeness. We specify the set of functional requirements and study the emerging questions related to the selection and weighting of the triple data to index, and the structuring and ranking of the retrieved results. Finally, we perform an extensive evaluation of the different factors that affect the IR performance for four different query types. The reported results are promising and offer useful insights on how different Elasticsearch configurations affect the retrieval effectiveness and efficiency.

Search, access, and explore life science nanopublications on the Web

“…Stemming from this last class of systems, the last category is the one of the virtualdocument based systems Kadilierakis et al (2020). First described in Lopez-Veyna, Sosa & López-Arévalo ( 2012), this approach relies on the concept of virtual document of a graph.…”

Keyword Search over RDF: Is a Single Perspective Enough?

“…The work in [21] introduced the TSA+VDP keyword search system, which first builds offline an index of documents over a set of subgraphs via a breadth-first search method, and at query-time, it returns a ranked list of these documents based on a BM25 model. Regarding the retrieval unit, most works return either URIs or subgraphs, except [8,22] that follow a triple-centered approach.…”

“…Elasticsearch has been also used for indexing and querying Linked Bibliographic Data in JSON-LD format [23]. Finally, Kadilierakis et al [8] adapts Elasticsearch for supporting keyword search over arbitrary RDF datasets. Through an extensive evaluation, the authors studied questions related to the selection of the triple data to index, the weighting of the indexed fields, and the structuring and ranking of the retrieved results.…”

“…Through an extensive evaluation, the authors studied questions related to the selection of the triple data to index, the weighting of the indexed fields, and the structuring and ranking of the retrieved results. In our work, we make use of the approach proposed in [8] because it is schema-agnostic and returns ranked lists of triples, which offers us the flexibility to provide different visualizations of the search results.…”

“…To grasp the idea, Figure 2 shows the search results for the query "El Greco museum", as presented in each of the five currently supported tabs. As basic keyword-search retrieval method, we assume the triple-centered approach proposed in [8] (which in turn relies on Elasticsearch) because it is schema-agnostic (and thus general-purpose), and it offers efficient and scalable retrieval services with effectiveness comparable (as evaluated using DBpedia-Entity test collection for entity search [9]) to the effectiveness of dedicated systems for keyword search over RDF (more in [8]). Over this basic service, in this paper we motivate the provision of certain fundamental perspectives, we showcase the benefits from each one, and we evaluate what users can achieve if they have all of them at their disposal.…”