=Paper=
{{Paper
|id=Vol-2994/paper58
|storemode=property
|title=NanoWeb: Search, Access and Explore Life Science Nanopublications on the Web (Discussion Paper)
|pdfUrl=https://ceur-ws.org/Vol-2994/paper58.pdf
|volume=Vol-2994
|authors=Fabio Giachelle,Dennis Dosso,Gianmaria Silvello
|dblpUrl=https://dblp.org/rec/conf/sebd/GiachelleDS21
}}
==NanoWeb: Search, Access and Explore Life Science Nanopublications on the Web (Discussion Paper)==
https://ceur-ws.org/Vol-2994/paper58.pdf
NanoWeb: Search, Access and Explore Life Science
Nanopublications on the Web∗
(Discussion Paper)
Fabio Giachelle1 , Dennis Dosso1 and Gianmaria Silvello1
1
Department of Information Engineering, University of Padua, Padua, Italy
Abstract
Nanopublications are scientific statements represented in the Resource Description Framework (RDF),
a brief machine-readable form representing data. Nanopublications consist of scientific facts extracted
from the literature and contextualized with provenance and attribution information.
Nanopublications are designed to enhance knowledge spreading, support the re-use of scientific
facts, and provide credit to the corresponding authors. Despite these promising features, nanopublica-
tions are not widely adopted, and their use is still quite limited to experts. We believe this is partly due
to the lack of services for searching, retrieving, and understanding nanopublications.
To mitigate this, we propose NanoWeb, a Web-based system designed to allow general users to
search, access, explore, and re-use nanopublications publicly available on the Web. Currently, NanoWeb
is tailored for the life science domain, where plenty of nanopublications are available.
Keywords
Nanopublications, Semantic Web, Data Citation
1. Introduction
In the last decades we assisted to a change of paradigm in the current world of research, more
precisely towards a more data-intensive approach, following the so-called fourth paradigm of
science [2]. As a result, data acquired a prominent role, becoming the center of scientific discov-
ery as well as of scholarship and scholarly communication [3]. Accordingly, data science has
experienced an unprecedented growth that involved also other linked research fields regarding
the search [4], provenance [5], citation [6], re-use [7], and exploration [8] of data.
Despite this premises, most of the huge volume of data available in the Web, is provided
in an unstructured format, which is human-readable but not machine-readable. Hence, the
heterogeneity of data and their representations lead to issues concerning interoperability; data
access, search and re-use; and domain-dependent requirements.
∗
This is an extended abstract of [1].
SEBD 2021: The 29th Italian Symposium on Advanced Database Systems, September 5-9, 2021, Pizzo Calabro (VV),
Italy
£ fabio.giachelle@unipd.it (F. Giachelle); dennis.dosso@unipd.it (D. Dosso); dennis.dosso@unipd.it (G. Silvello)
Å http://www.dei.unipd.it/~giachell/ (F. Giachelle); http://www.dei.unipd.it/~dosso/ (D. Dosso);
http://www.dei.unipd.it/~silvello/ (G. Silvello)
DZ 0000-0001-5015-5498 (F. Giachelle); 0000-0001-7307-4607 (D. Dosso); 0000-0003-4970-4554 (G. Silvello)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
Semantic Web technologies, such as the nanopublication model [9], aim to tackle some of
these issues according to the Findable, Accessible, Interoperable, Reusable (FAIR) guiding prin-
ciples [10]. Nanopublications represent statements (i.e. assertions) leveraging on the Linked
Open Data (LOD) principles [11] to convey scientific facts in an efficient, succinct, and machine-
readable form. The LOD features make nanopublications suitable to handle the connections
between related scientific facts, encoded in RDF. These facts are connected in a network that
can be explored, so to discover connections among them.
The nanopublications model is designed to make scientific claims accessible, so that the sci-
entific knowledge can automatically be explored by agents. Several scientific fields adopted the
nanopublications model, such as the Life Science domain, where more than ten million openly
available nanopublications [12] were created. There is important evidence in the biomedi-
cal domain about the benefits of using nanopublications for expanding scientific insight [13].
However, to the best of our knowledge, there is no tool to visualize nanopublications and ex-
plore their underlying network of scientific facts. BioKB [14] provides functionalities similar
to NanoWeb in terms of semantic search and graph visualization. In particular, it provides
access to the semantic content of biomedical articles through a SPARQL endpoint and a web
interface. Thus, allowing users to search for biomedical entities and visualize their graph of
relations. However, BioKB does not account for nanopublications and does not support a multi-
level exploration of the graph, enabling an in-depth exploration of the entities relation network.
Currently, searching for nanopublications is possible only through sparse SPARQL endpoints.
NanoWeb contributes to tackle these issues by providing: (a) a unified entry-point to access all
the publicly available nanopublications from the Life Science domain; (b) a user-friendly web
interface that enables users without a prior knowledge of SPARQL and related technologies to
search, access and explore millions of nanopublications in a human-readable form.
The paper is organized as follows: Section 2 presents some background, Section 3 provides
a brief overview of NanoWeb functionalities and describes one prominent feature, and Section
4 draws some final remarks.
2. Background
A nanopublication consists of a Resource Description Framework (RDF) graph based on an
assertion, that represents a scientific statement extracted, manually or automatically, from a
scientific publication. Nanopublications are representated using an extended RDF syntax sup-
porting quads in addition to triples, where an identifier (an IRI) is added. Hence, groups of
triples may be characterized as belonging to the same subgraph, i.e. to the same named graph
[15, 16], if they share the same extra URI. More specifically, the structure of nanopublications
consists of four named graphs: (a) the head, that connects the other three sub-graphs; (b) the
assertion graph (in blue in Figure 1) that reports the statement extracted from the scientific
publications as one RDF triple (subject-predicate-object), where the the two concepts of the as-
sertion (subject and object) are linked by a specific relationship defined by the predicate. The
predicate relationship may refer to external ontologies and scientific databases storing related
data. (c) The provenance graph (in orange in Figure 1) reports the assertion metadata such as
the assertion’s generation methods and its creators; and, (d) the publication info graph (yellow)
that contains the metadata about when the nanopublication was created, its authors and the
license terms for its reuse.
Figure 1: (A) RDF trig serialization of the nanopublication with assertion: ⟨activin A receptor type
2A - gene-disease biomarker association - Colorectal Cancer⟩; (B) network of gene-disease associations
created by five nanopublications.
Figure 1.A shows an example of nanopublication regarding the biomedical domain. The as-
sertion contains information about the gene-disease association ⟨activin A receptor type 2A –
gene-disease biomarker association – colorectal cancer⟩, where activin A receptor type 2A is the
subject, gene-disease biomarker association is the predicate and colorectal cancer is the object of
the triple. The assertion is extracted from a paper [17], which states that a drug – Mesalazine
– can reduce the mutations in a tumor suppressor gene – activin A receptor type 2A (ACVR2)
– related to colorectal cancer. This assertion is represented as a RDF nanopublication with the
TriG 1 serialization in Figure 1.A. TriG is a compact RDF syntax, characterized by the use of
prefixes to avoid the repetition of IRIs. Figure 1.B illustrates an example of a five nodes network
concerning gene-disease associations. If we consider for instance the colorectal cancer disease,
it is initially connected with the gene ACVR2, through the gene-disease biomarker association.
If we expand the colorectal cancer relation network, we discover two other genes 5XRCC6P5
and ABCA2. These genes are directly connected to the colorectal cancer disease and, after a
further expansion, we discover that they are linked also to the malignant neoplasm of breast
disease. These RDF triples are all inferred from five distinct papers distinguished by venue and
time of publication, that also do not cite each other. The triple in Figure 1.B contained in the
blue rectangle is also the one used as assertion in Figure 1.A. This shows how, by using RDF
graphs, triples connected among them, the LOD principles, and nanopublications about these
triples, it is possible to explore and discover new concepts and links among them not otherwise
explicitly visible through the traditional scientific literature. Despite these promising features,
the adoption of nanopublications by scientists is still quite limited to specific domains [18]. To
1
https://www.w3.org/TR/trig/
date, there is no easy way to find, re-use and cite them, as well as no service that allows users
to access them in a human-readable form, and search them using keywords or natural lan-
guage queries. Moreover, there is no tool designed to allow the progressive exploration of the
nanopublications relation network, which can be useful for the discovery of new meaningful
connections between nanopublications’ assertions and scientific facts.
SPARQL is a powerful but complex query language to access and interrogate RDF graphs [19].
Due to its complex syntax, SPARQL is not intuitive for end-users without a prior expertise and
knowledge of the underlying schema of database, and of the IRIs used in it. To address the
issues related to the use of SPARQL and enable users querying the RDF datasets via natural
language queries, the keyword search paradigm has been introduced. Keyword-based methods
are designed to ease the access to structured data [20, 21]. In contrast to SPARQL, keyword
search returns a ranking of answers, ordered according to their relevance for the user-provided
keyword query. Keyword query search systems over structured data consider both relational
databases (RDB) [21] and graph-like databases such as RDF datasets [22, 20]. Keyword-based
systems may be divided into three categories: (a) schema-based systems [23, 24] which ex-
ploit the information concerning the schema of the database to formulate structured-language
queries (e.g. SQL or SPARQL queries ) designed from the user-provided keyword query; (b)
graph-based systems [25, 26] which rely on the transformation of relational databases into
graphs; and (c) virtual-document based systems [27] which generate a virtual document [28] for
a given graph, where the lexical content is preserved. Hence, virtual-document based systems
can leverage on efficient state-of-the-art IR methods for indexing and ranking. However, there
is no keyword search system for nanopublications, which are always searched via SPARQL
endpoints. To allow end-users specify queries in natural language, NanoWeb exploits a very
recent advancement in virtual-document based systems [29], thus enabling fast and effective
keyword-search over RDF and nanopublications.
3. NanoWeb
NanoWeb2 is a public and open-source3 web application designed to provide intuitive search,
exploration, citation [30] and re-use of nanopublications.
As of now, NanoWeb is tailored for the life science domain, and it is conceived to support life
science experts in their research work. Nevertheless, NanoWeb can be applied, after domain-
specific customization, to other scientific domains.
NanoWeb provides a unified access to the world of nanopublications, even to users without
a specific expertise, which can search for nanopublications using natural language queries. Its
main features are:
1. A crawler collecting the public nanopublications available on the web;
2. Keyword search and advanced search. The latter provides a structured guided search
based on the boolean search paradigm;
3. A user-centric visual interface to search and consult nanopublications, enriched with
information gathered from external authoritative ontologies;
2
https://w3id.org/nanoweb/
3
NanoWeb source code is available at https://github.com/giachell/nanoweb
Figure 2: Graph exploration: users can inspect nanopublications and the information concerning en-
tity connections by clicking on the edges. As a result, an information window is shown reporting the
nanopublication’s content, which refers to the entities linked by the edge clicked. Inside the red box
(right side) is reported the information content of the nanopublication with assertion: ⟨mutS homolog
6 - gene-disease biomarker association - Carcinogenesis⟩.
4. A visual exploration of the nanopublications’ relation network built around the scientific
facts encoded in the assertions;
5. Data search capabilities providing direct connections to evidence papers and external
scientific curated databases.
One of the prominent NanoWeb features is the exploration of the nanopublications relation
network, i.e. the RDF graph composed by the triples extracted from the scientific evidence
papers, as described above. NanoWeb represents the nanopublications relation network as a
graph where the nodes are the subjects and the objects of the nanopublications’ triples. The
nodes are connected together with directed edges from subjects to objects.
Figure 2 shows a multi-level graph exploration of the relation network concerning the nanop-
ublication with title mutL homolog 1 - Colorectal Carcinoma, which expresses a gene-disease
association. The graph exploration functionality allows the user to understand the relation-
ships between different nanopublications. The graph exploration functionality is conceived
to let the user progressively expand the graph, focusing on both the nodes and connections
of interest, without any limit on the depth of the exploration, i.e., to the graph’s dimension
visualized. Hence, the user can potentially expand the whole graph, having all the nodes of the
relation network displayed. The exploration of the relation network is one of the biggest con-
tribution, since it provides a deeper understanding of the connections between entities, such
as gene-disease associations.
Figure 3: Advanced search: users are guided through the search process by means of sequential filters,
that progressively refine the query domain and restrict the retrieval output accordingly. The figure
shows an example of a topic-based search for nanopublications concerning genes and in particular the
mutL homolog 1 gene.
4. Conclusions
Nanopublications are concise, noise-free RDF resources designed to efficiently convey infor-
mation and concepts.
The full adoption of nanopublications is held back by the lack of services to search, access,
explore, and cite them. As of today, data search is possible using only SPARQL queries, that
are only within the capabilities of experienced users. There is no service to search over all
the publicly available nanopublications, using either keyword or natural language queries. To
target these issues we designed and developed NanoWeb.
NanoWeb provides unified access to Life Science nanopublications so that users can search,
access, explore, and re-use them on the Web. NanoWeb allows the users to (i) search for domain-
specific nanopublications using keyword queries; (ii) explore their relation network to discover
new nanopublications and meaningful connections; (iii) access and consult their information
content; (iv) access the evidence paper information (e.g. abstract) and the entry points to related
data record in external curated scientific databases; and, (v) easily cite nanopublications.
NanoWeb enables a serendipity-oriented perspective in the Life Science domain. Users can
benefit from this perspective through the exploration of nanopublication graphs, which could
lead to a deeper understanding of the connections between entities (e.g. genes and diseases)
and enrich the domain knowledge.
Acknowledgments
This work is supported by the ExaMode project, as part of the European Union Horizon 2020
program under Grant Agreement no. 825292.
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