LOTUS: Adaptive Text Search for Big Linked Data

Ilievski, Filip; Beek, Wouter; Erp, Marieke van; Rietveld, Laurens; Schlobach, Stefan

doi:10.1007/978-3-319-34129-3_29

Cited by 10 publications

(14 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.…”

Section: Abstractearch Over Rdf Datasetsmentioning

confidence: 99%

Keyword Search over RDF: Is a Single Perspective Enough?

Nikas

Kadilierakis

Fafalios

et al. 2020

BDCC

View full text Add to dashboard Cite

Since the task of accessing RDF datasets through structured query languages like SPARQL is rather demanding for ordinary users, there are various approaches that attempt to exploit the simpler and widely used keyword-based search paradigm. However this task is challenging since there is no clear unit of retrieval and presentation, the user information needs are in most cases not clearly formulated, the underlying RDF datasets are in most cases incomplete, and there is not a single presentation method appropriate for all kinds of information needs. As a means to alleviate these problems, in this paper we investigate an interaction approach that offers multiple presentation methods of the search results (multiple-perspectives), allowing the user to easily switch between these perspectives and thus exploit the added value that each such perspective offers. We focus on a set of fundamental perspectives, we discuss the benefits from each one, we compare this approach with related existing systems and report the results of a task-based evaluation with users. The key finding of the task-based evaluation is that users not familiar with RDF (a) managed to complete the information-seeking tasks (with performance very close to that of the experienced users), and (b) they rated positively the approach.

show abstract

Section: Abstractearch Over Rdf Datasetsmentioning

confidence: 99%

Keyword Search over RDF: Is a Single Perspective Enough?

Nikas

Kadilierakis

Fafalios

et al. 2020

BDCC

View full text Add to dashboard Cite

show abstract

“…LODLaundromat [12] (http://lodlaundromat.org) is a service that fetches several datasets and transforms them to a common format. By using this service, one can search for specific URIs, while it provides a keyword search engine (called Lotus [31]), where one can find all the triples and URIs where a specific keyword occurs. Comparing to our approach, we take into consideration the equivalence relationships between the datasets, therefore, one can find all the equivalent URIs for a given one, while we offer connectivity measurements for any set of datasets.…”

Section: (Via Sparql)mentioning

confidence: 99%

High Performance Methods for Linked Open Data Connectivity Analytics

Mountantonakis

Tzitzikas

2018

Information

View full text Add to dashboard Cite

The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets. Connectivity among two or more datasets can be achieved through common Entities, Triples, Literals, and Schema Elements, while more connections can occur due to equivalence relationships between URIs, such as owl:sameAs, owl:equivalentProperty and owl:equivalentClass, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets. However, there are not available connectivity measurements (and indexes) involving more than two datasets, that cover the whole content (e.g., entities, schema, triples) or "slices" (e.g., triples for a specific entity) of datasets, although they can be of primary importance for several real world tasks, such as Information Enrichment, Dataset Discovery and others. Generally, it is not an easy task to find the connections among the datasets, since there exists a big number of LOD datasets and the transitive and symmetric closure of equivalence relationships should be computed for not missing connections. For this reason, we introduce scalable methods and algorithms, (a) for performing the computation of transitive and symmetric closure for equivalence relationships (since they can produce more connections between the datasets); (b) for constructing dedicated global semantics-aware indexes that cover the whole content of datasets; and (c) for measuring the connectivity among two or more datasets. Finally, we evaluate the speedup of the proposed approach, while we report comparative results for over two billion triples.

show abstract

“…Tools for semantic text search over Linked Data such as LOTUS [18] allow literals, and statements in which they appear, to be retrieved based on textual relevance criteria. To enable users to obtain relevant information for their use case, these tools use retrieval metrics that are calculated based on structured data and meta-information.…”

Section: Semantic Text Searchmentioning

confidence: 99%

“…Frank 18 is a command-line tool that allows data from the LOD Laundromat data collection to be streamed at the level of triple pattern fragments [3]. This tool is used for the analysis of the quality of language-tagged strings (Section 6.2) and for the analysis of the quality of data documents (Section 6.3).…”

Section: Toolchainmentioning

confidence: 99%

See 1 more Smart Citation

Literally better: Analyzing and improving the quality of literals

Beek

Ilievski

Debattista

et al. 2017

Self Cite

View full text Add to dashboard Cite

Quality is a complicated and multifarious topic in contemporary Linked Data research. The aspect of literal quality in particular has not yet been rigorously studied. Nevertheless, analyzing and improving the quality of literals is important since literals form a substantial (one in seven statements) and crucial part of the Semantic Web. Specifically, literals allow infinite value spaces to be expressed and they provide the linguistic entry point to the LOD Cloud. We present a toolchain that builds on the LOD Laundromat data cleaning and republishing infrastructure and that allows us to analyze the quality of literals on a very large scale, using a collection of quality criteria we specify in a systematic way. We illustrate the viability of our approach by lifting out two particular aspects in which the current LOD Cloud can be immediately improved by automated means: value canonization and language tagging. Since not all quality aspects can be addressed algorithmically, we also give an overview of other problems that can be used to guide future endeavors in tooling, training, and best practice formulation.

show abstract

LOTUS: Adaptive Text Search for Big Linked Data

Cited by 10 publications

References 15 publications

Keyword Search over RDF: Is a Single Perspective Enough?

Keyword Search over RDF: Is a Single Perspective Enough?

High Performance Methods for Linked Open Data Connectivity Analytics

Literally better: Analyzing and improving the quality of literals

Contact Info

Product

Resources

About