-The International Technology Alliance in Distributed Analytics and Information Science (DAIS-ITA) is a research program conducting fundamental research across a consortium of organizations from academia, industry and government in the US and UK. A key output of the program are the academic publications, and from these a rich social and topical network between authors and organizations emerges. To capture and convey this we have created the publicly available Science Library as a user-centric, interactive portal. The Science Library is a Controlled Natural Language (CNL) driven research gateway, which allows the community to explore and query the publications and networks through an open, webbased application. The data is represented through interactive visualizations along with the ability for users to query the model using a natural language conversational interface. The CNL based approach models the data through concepts, properties and relationships which are defined using CNL and are therefore both human readable and directly machine processable. This captures complex semantics in a simple format and enables nontechnical users to participate in the continuous improvement of the data model behind the Science Library application. This paper presents the features, implementation and design considerations of the Science Library and the underlying CNL implementation.
When publishing a broad body of scientific research, we
believe that it is important to retain some of the contextual
information about the relationships between the papers, authors
and topics. This helps the reader to understand the “bigger
picture” behind each paper, beyond the traditional information
captured in the references and citations. The International
Technology Alliance in Distributed Analytics and Information
Science (DAIS-ITA) [
In this paper we describe the Science Library1 application,
1The live Science Library for the DAIS ITA research program can be
accessed at http://sl.dais-ita.org/science-library
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
and give examples of the various interactive visualizations
that enable the visitor to explore the rich knowledge graph of
research publication information. We believe that the approach
we have taken supports the idea of Cognitively Mediated
Research Discovery [
To achieve this in a realistic timeframe we used the advantages available when developing such semantic knowledge graphs using a Controlled Natural Language (CNL), and we describe this CNL-based implementation of the Science Library in this paper. We also described the high-level architecture of the Science Library system, and the technique we use to solicit publication information from the DAIS ITA community for publishing.
The Science Library solution is built using technology from our earlier research (Controlled English) but the Science Library solution itself has not been reported in detail in any of our publications to date. The previously published work introduces the Science Library at a high level while this paper explores how this solution provides flexibility through the knowledge graph and the ability to explore complex relationships. Hence this paper describes the implementation of the Science Library and the potential power of the approach when using a Controlled English based solution. The Science Library currently handles 9,000 nodes, with approximately 40,000 links and 60,000 property values. It contains over 250 papers, approximately 300 authors and over 60 organizations. The site is publicly available and gets up to 50 active users per day.
In Section II we explore the various ways to interact with the complex knowledge graph through visualizations accompanied by the motivations behind them and in Section III we explain the CNL basis for the application, and the conceptual model schema that underpins the work. In Section IV we discuss existing work related to using CNL to drive Science Gateways and in Section V we talk about our future plans for the environment as the DAIS ITA program continues. Section VI concludes the paper.
Like many research groups or programs, one key output are the academic publications created as a result of the research. From these a rich social and topical network between authors and organizations emerges, and since the DAIS ITA is focused in particular on fostering a deep collaborative mind-set it is important for us to understand and explore this aspect of our work. To capture and convey this body of published research, for example as shown in the Author narrative timeline in Figure 1, we have created the publicly available Science Library2 as a user-centric, interactive portal. The idea for the Science Library originally came from the earlier NIS ITA research program, and was originally developed and deployed3 for that purpose, but has been reused and extended for this later DAIS ITA research program. The Science Library is a Controlled Natural Language (CNL) driven research gateway, which allows the community to explore and query the publications and networks through an open, web-based application built on a complex semantic knowledge graph. The data is represented through interactive visualizations along with the ability for users to query the model using a natural language query interface. The CNL based approach models the data through concepts, properties and relationships which are defined using CNL and are therefore both human readable and directly machine processable. This captures complex semantics in a simple format and enables non-technical users to 2Science Library for DAIS ITA (Distributed Analytics and Information Science International Technology Alliance), http://sl.dais-ita.org/science-library/ 3Science Library for NIS ITA (Network and Information Science International Technology Alliance, http://nis-ita.org/science-library/ participate in the continuous improvement of the data model behind the Science Library application. This paper presents the features, implementation and design considerations of the Science Library and the underlying CNL implementation.
Fig. 2. List of publications
One primary purpose of the Science Library is the exploration of publications from a given research program, such as the DAIS ITA. Figure 2 shows a simple list of publications that can be sorted by various criteria including: age, name and citation count. Additional information is available on this page, such as an overall summary of the publications for the program, broken down into publication type (journal, external conference, internal conference, technical report or patent). Each publication has a colored icon to indicate the paper type, and any paper variants are indicated by stacked icons. For example, it is often the case that an original journal or conference publication will be summarized or reworked into an internal conference publication for presentation to the DAIS ITA research community at the annual meeting of the alliance.
Throughout the Science Library application all entities can be clicked on to take the user to a page that shows the details about that particular entity. In Figure 3 we show the publication details page which provides a large thumbnail image of the paper and the ability to download all relevant information regarding that publication (e.g. the paper PDF, any presentation slides, poster, or other supporting material). We also display the author information, the title and abstract and any venue information, including the location at which the paper was presented (for conference papers). All of this information is available in the semantic knowledge graph that underpins the system and is available for searching or exploration of the graph.
Figure 1 shows a narrative timeline for the publications of any author. The visualization shows the development of research ideas and topics over time, along with co-authorship sub-groups. Users can read this visualization from left to right, with each node on the network being a particular publication at a point in time and the links between them showing coauthorship flows between the papers. This can be particularly useful to determine when new threads or topics form partway through the research, and how they manifest in terms of publications over time.
Building on this is the more traditional co-authorship chart shown in Figure 4. The nodes represent authors, indicated by their initial (e.g. “DB” for Dave Braines), and links to other authors who they have co-authored with. The weight of the links indicate the number of times that co-authorship has occurred, and this also drives the proximity of the nodes. In the interactive visualization, users can hover over particular links to have them highlighted, and have information about the author in question appear over the node. This is particularly useful for highlighting common co-authorship subgroups, which often indicate particular topics or threads of work that subsets of authors regularly publish. The colors of the nodes indicate whether an author is from academia, industry, or government.
Sometimes it is useful to see the specific publications for a pair of co-authors, i.e. only those papers for which both authors are involved. This is shown in Figure 5 and is represented as a stacked bar chart over time, with the bars representing the papers of a particular type. Hovering over any bar shows the individual papers for that period, enabling the user to click through to see their details. This is a very similar visualization to the one used for displaying the publications for any given organization and seems to give good insight for this particular perspective. It is worth noting however that the visualizations chosen for each view are purely subjective based on our informal opinion as to what “works best” for the given situation and is not according to any scientific basis.
In Figure 6 we show the visual aspects of a particular event mainly to showcase that we have the geo-spatial data, rather than it being a particularly useful view. We show the event venue as a large yellow circle, and the authors of the publications at this event (color coded according to their organization type) are shown in the geographic location associated with their organization.
Each page also has a simple search bar at the bottom of the screen. This will accept any search terms from the user and performs a reasonably complex semantic search into the semantic knowledge graph. If the terms that are searched for result in matches to specific named instances then the result shown to the user is the relevant page on which this information can be located. For example, the search term “Dave Braines co-authors” will return a result which is the author page for Dave Braines in the co-author graph mode. In the event that multiple matches are made, or matches are made to which there is no pre-defined visualization type, then a simple list of clickable links (to papers, authors, organizations, topics etc) are shown. The search is based on a combination of fuzzy keyword searching (to match title and abstract terms) as well as semantic graph searching. This capability is underpinned by the “Hudson” natural language search APIs4 which provide a rich JSON response indicating which concepts, properties or instances were matched in the semantic knowledge graph based on the query terms used. There is a lot of additional power that can be gained from using this approach which is not yet implemented into the system. For example the ability to ask questions such as “list authors who have written papers with someone from industry on the topic of IoT before 2018”. Today the interpreter API will match all of the terms in this question to the corresponding features in the semantic graph, but we have not yet developed a suitable “answerer” capability to take this interpretation and show the result to the user in a meaningful way.
In all of these visualizations we have designed a very simple user experience and have tried to showcase a variety of visual techniques to communicate certain aspects of the information in the underlying semantic knowledge graph. There is far more information available in the graph than we are making use of (See Figure 7), and it would be possible in the future to create a much richer set of visualizations if time permits. See Section V for some discussion about our plans for possible future extensions to this work.
All of the visualizations and interaction features described in
the preceding section are implemented using traditional web
development technologies using HTML, CSS and Javascript
(client and server side),and they could be integrated with any
suitable back-end database-like system. The real novelty in
our approach comes in the technology used to implement the
semantic knowledge graph, which drive the visualizations and
allow users to easily contribute to, and extend, the model.
To achieve this, we use a Controlled Natural Language (CNL)
technology named Controlled English (CE), which we defined
and developed in previous research [
The following CE examples taken from the Science Library illustrate the structure and readability of the sentences used to rapidly build up the concepts and relationships in the knowledge graph: there is a person named 'dave_braines' that has 'Dave' as forename and has 'Braines' as surname and has 'Dave Braines' as full name and has the organisation 'IBM UK' as default organisation and has 'https:/.../dave_braines.jpg' as profile picture. the person 'dave_braines' has the person 'alun_preece' as co-author. there is a document named 'doc-1009' that is a external conference paper and has 'A Generative Model For Predicting Terrorist Incidents' as title and applies to the programme 'DAIS-ITA' and has the event 'SPIE DSS 2017' as venue and has the project 'IPP16P4' as project. the published person 'dave_braines' wrote the document 'doc-1276'.
We operationalize the CE language in the open source
ce-store5 library which is a simple Java-based web-services
component to allow developers to easily integrate with CE
knowledge graphs using REST APIs that are familiar to them
4Hudson natural language apis for the ce-store,
https://github.com/cestore/ce-store/wiki#hudson
5ITA Controlled English store (ce-store),
https://github.com/ce-store/cestore
from using other technologies. For the developer we want the
environment to be as familiar and easy to use as possible, even
if they don’t need to know that the underlying implementation
is CE. In our previous research we have written extensively
about the use of CE and ce-store in a variety of settings directly
relevant to the usage in this paper. In [
The ability to rapidly define conceptual models in
realtime to support the capture of complex semantic data came
from our field trial work on various exercises, for example
as reported in [
In addition to the core ce-store engine being available as open source, we have also open sourced the Science Library user interface6 and the Controlled English conceptual models that underpin it7. All the components and functionality presented in this paper can be used freely by other solutions; the entire approach is open sourced and available for anyone else to adopt as a separate, stand-alone instance. Full details and tutorials on how to set up a similar Science Gateway using 6Science library user interface, https://github.com/ce-store/science-library 7Science library data model for DAIS ITA, https://github.com/ce-store/sldata-dais CE can be found on the ce-store wiki page8.
It is this ability to rapidly define the underlying semantic
conceptual models and then populate them with corresponding
data that enabled us to build the Science Library application in
a very short amount of time. We were able to develop simple
models quickly, populate a small amount of data and then
build some example visualizations in a very rapid manner.
The flexibility to modify and extend the concepts, properties
or instances in the underlying graph gave us a very fluid
environment in which we could rapidly explore new ideas and
new conceptualizations before finalizing on those that worked.
In Figure 8 we show the high-level conceptual model for the
Science Library. Key concepts are document (an academic
publication), ordered author (an authorship of a paper), person
(an individual who may author multiple papers) , organization,
event and so on. Each of these concepts has one or more named
relationships to other concepts, or textual attributes (such as
title or abstract) which are collectively known as properties.
These concepts and properties which are the equivalent of a
conceptual graph [
In Figure 9 we show the implementation of the system, 8ce-store wiki and tutorials, https://github.com/ce-store/ce-store/wiki and how the information flows from our private content management system into the public Science Library application. We have designed a system to place minimum administrative burden on our consortium of paper authors, enabling them to simply provide a PDF of their publication, and we as administrators manually extract all of the required information (authors, venue, organizations etc) in order to publish this on their behalf.
Once the paper authors upload a PDF to the Drupal based content management system, the administrators manually create an instance of a Science Library publication and add the related properties. A Jpeg thumbnail of the paper is generated using the PDFBox tool and the citation statistics are manually pulled from Google Scholar and also added to the Science Library publication instance.
The ce-store engine is run to ingest and generate CE for the new publications and to update the existing instances and relationships. Finally, the Science Library makes calls to the ce-store semantic knowledge graphs via simple REST APIs to generate the visualizations of relationships and present the data to the user on the interactive website.
There is limited literature around the use of CNL to
drive Science Gateways and interactive visualizations, however
methods to allow users to quickly and easily contribute to
ontologies using CNL are known [
Domain representation in general aims to effectively present
the relevant information to users from a complex and vast set
of data. There are various techniques and workflows to
visualize digital networks. A key aspect of domain representation
lies in the ability to allow the user to explore the data and
understand the relationships between entities in an intuitive
way. An example technique is visually presenting the semantic
relationships between entities alongside an ordered list to give
the user a wider perspective of the data [
We are continually improving the Science Library and the underlying technologies which underpin the project. One such improvement is to enhance the user interface of the website to make it accessible from any device, including mobile phones and tablets.
A better solution to link directly to an external citation source that can return the citation data in a structured form, to allow computation and processing within the Science Library will be developed to replace the current manual system of collecting data from Google Scholar.
We are also planning to improve the way the new instances in the ce-store model get created as well as updated, to improve the efficiency of the publishing process. A Node.js based tool will enable generation of new instances and link to existing instances (e.g. people, organisations, projects). This will enable us to publish to the Science Library more effectively by automatically generating the required CE.
The Science Library holds the publication information of the DAIS-ITA research program and provides the community with an interactive, visual web portal to view this data. It presents contextual information about the relationships between papers, authors and topics alongside the traditional information captured in the references and citations. Users are able to interact with the rich body of information in different forms including lists, relationship graphs and timelines to better understand the wider context of the publication, authors and topics.
ITA Controlled English was used to capture and generate the semantic knowledge graph which drives the Science Library. Since CE is language that is both machine processable and human readable at the same time, it has been the key in how we were able to implement and rapidly develop this Science Gateway. We have open sourced the core ce-store engine as well as the entire Science Library user interface to share this flexible framework with the wider scientific community.
This research was sponsored by the U.S. Army Research Laboratory and the UK Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the UK Ministry of Defence or the UK Government. The U.S. and UK Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copy-right notation hereon.