=Paper=
{{Paper
|id=Vol-2786/Paper58
|storemode=property
|title=Bharathi –An Applied Semantic Intelligence Use Case for Public Data in India
|pdfUrl=https://ceur-ws.org/Vol-2786/Paper58.pdf
|volume=Vol-2786
|authors=Asha Subramanian,Manikanta Vikkurthi,Gunjan Pattnayak,Akshay K S,Harika Vikkurthi
|dblpUrl=https://dblp.org/rec/conf/isic2/SubramanianMPSV21
}}
==Bharathi –An Applied Semantic Intelligence Use Case for Public Data in India==
https://ceur-ws.org/Vol-2786/Paper58.pdf
Bharathi–An Applied Semantic Intelligence Use
Case for Public Data in India
Asha Subramaniana , Manikanta Vikkurthia , Gunjan Pattnayaka , Akshay
K Sa and Harika Vikkurthia
a Semantic Web India, Bangalore, Karnataka, India. Home Page: http:// www.semanticwebindia.com/
Abstract
Data sets published for public consumption related to governance typically contain common metadata.
This metadata normally describes the publishing organisation, the domain, the administrative region
and a topic that the data best describes. A relevant semantic vocabulary will not only facilitate linking
the metadata through meaningful contexts but also enable grounding related information from diverse
public data sets using semantic entities defining the metadata. In this paper, we present Bharathi –Linked
Data Vocabulary for the Indian context. Bharathi contains information regarding government organi-
sations at the union & state government level, administrative regions, sectors, sub sectors and common
topics used frequently in the vocabulary of the government functions. Further, Bharathi contains links
to other open vocabularies such as GeoNames for geographical locations. The schema of Bharathi uses
existing established ontologies making it inter-operable and extensible. We describe Bharathi along with
a live use case of its application in accomplishing a semantic Covid19 data analysis for India.
Keywords
Linked Data, Knowledge Graph, Ontology, Semantic Intelligence
1. Introduction datasets from disparate sources. This meta-
data contains valuable information relating
Metadata related to publicly accessible data to pivotal entities such as the publishing gov-
published on open government data por- ernment organisation along with its classi-
tals and others such as data.gov.in1 , Rainfall fication (e.g. Ministry of Health and Family
Statistics2 , Covid19 related datasets3 etc. of- Welfare –Union Government, Department of
ten tend to repeat themselves across multiple Health and Family Welfare –State Govern-
International Semantic Intelligence Conference (ISIC ment of Karnataka), sector (e.g. Environ-
2021), New Delhi, India: February 25-27, 2021 ment, Health, Education), the administrative
" asha@semanticwebindia.com (A. Subramanian); region for which the data has been collected
manikanta@semanticwebindia.com (M. Vikkurthi); (e.g. State –Karnataka, District –Bangalore
gunjan@semanticwebindia.com (G. Pattnayak);
akshay@semanticwebindia.com (A.K. S); Urban, City –Bangalore) etc. The meta-
harika@semanticwebindia.com (H. Vikkurthi) data is available in un-structured and semi-
structured formats in various home pages
© 2021 Copyright for this paper by its authors. Use permit-
ted under Creative Commons License Attribution 4.0 Inter- of government of India web sites such as
national (CC BY 4.0).
CEUR
CEUR Workshop
http://ceur-ws.org
Proceedings GOI Directory4 , Census India5 limiting the
(CEUR-WS.org)
Workshop ISSN 1613-0073
Proceedings
1 Open
Government Data (OGD) Platform India:
https://data.gov.in/ 4 https://www.goidirectory.nic.in/ –Indian Govern-
2 Indian Meteorological Department: https: ment Organisations and their classifications at the Cen-
//mausam.imd.gov.in/imd_latest/contents/rainfall_ tral Government and State Government level
statistics.php 5 http://censusindia.gov.in/DigitalLibrary/
3 Crowdsourced Covid19 data: https://www. 2011CodeDirectory.aspx –State-wise Rural and Ur-
covid19india.org/ ban administrative units location codes maintained by
487
discovery of inter-connected relationships sample information from Bharathi.
across these pivotal entities by humans and We start with the canvas of Related Work
machines. In many cases, the same entity (a (Section 2) where we list references to sim-
government organisation or an administra- ilar work and set the context for our con-
tive region) is referred by its respective code tribution. Next, we provide an overview of
across different web sites making it a humon- Bharathi followed by an introduction to the
gous task to connect the information across Bharathi ontology in Section 4. Section 5 de-
the two data sources although they relate to tails the process of generation of Bharathi.
the same entity. Therefore, creating an ap- Real world applications of Bharathi are pre-
propriate semantic framework for a knowl- sented in Section 6. We conclude with a brief
edge based representation for this metadata summary and thoughts on future directions
is paramount. Such a semantically enriched in Section 7.
knowledge representation will substantially
help in linking public data sets and also pro-
vide a valuable inter-operable, reproducible 2. Related Work
and machine-readable resource for the scien-
Several research efforts such as [1, 2, 3, 4,
tific and industrial community. To the best
5, 6, 7, 8] have shown interest in linking
of our knowledge, no reasonably large and
public data with semantic metadata to en-
up-to-date corpus of linked data information
able intuitive linking of information from di-
about government entities, their classifica-
verse sources. The research efforts specifi-
tions and their inter relationships specifically
cally to model the metadata around public
for the Indian context has been made publicly
data can be found in [9] where the authors
available yet, in the form of a machine read-
presents Gridworks-based data workbench
able resource. This paper presents a use case
application to help in converting open gov-
for applied semantic intelligence – Bharathi
ernment data to linked data by using stan-
and its potential applications.
dard Data Catalog Vocabulary. The data cat-
Bharathi is populated using a semi-
alogs of open data are an input to the appli-
automated pipeline that periodically har-
cation, yielding the linked data form. [10]
vests data from various government of India
presents a semantic government vocabulary
websites, annotating the entities and lifting
to create annotations of open government
the semantics into a RDF6 grounded by a
data. It details the different layers of the vo-
sound model based on established vocabu-
cabulary depending on different ontological
laries such as Organisation Ontology7 , SKOS
terms found in open government datasets. It
Vocabulary8 and the GeoNames Knowledge
also demonstrates how to use the vocabulary
base9 . We provide a web interface accessible
to annotate open government data at differ-
at http://semanticwebindia.in/bharathi for
ent levels. [11] explores the documentation
exploring the underlying knowledge graph
needs for open government data with specific
of Bharathi, enabling users from outside the
focus on metadata interoperability issues. It
scientific community to find, retrieve and
presents two methodologies for interoper-
ability after studying a variety of open gov-
the Registrar General and Census Commissioner, India
6 Resource Description Framework: https://www. ernment metadata, globally recognized stan-
w3.org/RDF/ dards and guidelines. The outcomes of the
7 org: http://www.w3.org/ns/org#
8 skos: http://www.w3.org/2004/02/skos/core#
first approach are juxtaposed with those of
9 gn: http://www.geonames.org the second in the light of interoperability
488
during the metadata integration process. [12] metadata specifically for the Indian context
presents a methodology, five basic steps and a to enable a powerful semantic integration of
model to publish statistical data coming from public data related to governance in India.
tabular data sources or relational databases
in the form of linked open data. It follows
the best practices for publishing linked data, 3. Bharathi Overview
a W3C working group note such as provid-
Bharathi is populated using data extracted
ing URI construction suggestions following
from a number of government of India web
URI policy rules, reuse of standard vocab-
sites whose content is created and main-
ularies, such as the Data Cube Vocabulary,
tained by the National Informatics Centre
SKOS, etc., converting data to RDF and pro-
(NIC), Ministry of Electronics and Informa-
viding access to the converted data. [13] de-
tion Technology, Government of India. In the
scribes the attempt at building “AKTivePSI”-
current version of Bharathi, we consider in-
an initiative by the Office of Public Sector In-
formation from https://lgdirectory.gov.in/13 ,
formation, UK to adopt semantic web tech-
goidirectory.nic.in/, http://censusindia.gov.
nology for large scale integration, sharing
in/ and https://data.gov.in/ to build the
and reusing the public data for the benefits
knowledge graph for metadata around ad-
of government, businesses and citizens alike.
ministrative regions and their codes, govern-
The key outcome due to the success of this
ment entities and their classification, various
pilot project led to more increased aware-
sectors and their links within the respective
ness amongst the government bodies about
government function. While Bharathi is
the power of semantic web technology.
extensible in including new sources of in-
While there is a prolific attempt to intro-
formation, the extraction pipeline will need
duce a semantic knowledge framework to
modification depending on the structural
extract and maintain the metadata required
specifications of the source. The output
for semantic integration of public data in
from the extraction pipeline is converted
the western nations, there have been none
into semantic entities using the Bharathi
such coordinated and specific efforts in In-
ontology (Refer Section 4). The Bharathi
dia to the best of our knowledge. DBpedia
knowledge graph is currently accessible
resources (extracted from Wikipedia infor-
from a Virtuoso triplestore with a SPARQL
mation) do exist for India, though in many
endpoint at http://www.semanticwebindia.
cases they are not up-to-date with the in-
in/BharathiLive/sparql. All entities of
formation published on the Government of
Bharathi are semantically linked and acces-
India web sites. The motivation to create
sible using persistent resolvable identifiers
Bharathi stems from extensive research work
following the W3C best practices. Bharathi
that accomplished semantic integration of
is released under a Creative Commons
open data tables published in data.gov.in us-
Attribution-ShareAlike 4.0 International
ing entities from Linked Open Data (LOD)10
License14 . The web interface is accessible at
namely DBpedia11 and Wikidata12 [14], [15].
http://www.semanticwebindia.in/bharathi/
Bharathi is our contribution to create, main-
allowing users to search and browse
tain and sustain this knowledge graph of
10 LOD: https://lod-cloud.net/ 13 https://lgdirectory.gov.in/ : LOCAL GOVERN-
11 DBpedia: https://wiki.dbpedia.org/ MENT DIRECTORY – Complete directory of land re-
12 Wikidata: https://www.wikidata.org/wiki/ gions/revenue, rural and urban local governments
Wikidata:Main_Page 14 https://creativecommons.org/licenses/by-sa/4.0/
489
Figure 1: Bharathi Model depicting the ontology and sample instances
Bharathi. The knowledge graph is up- tion of established vocabularies was made to
dated every 3 - 6 months, depending on ensure 1) interoperability of the knowledge
the changes to the information at source. base through reuse 2) adherence to global
Information related to the knowledge graph definitions of schema for known entities by
dumps and ontology metrics can be accessed reusing terms and most importantly 3) lever-
at http://semanticwebindia.in/bharathi/ aging of community support for linked open
BharathiKG. data cloud vocabularies.
The core element of Bharathi on-
tology is the Organisation class
4. Bharathi Ontology (bhorg:Organisation16 ) derived as a subClass
of org:organization. This entity is used to
The underlying schema for Bharathi uses es-
denote any public, private or government
tablished ontologies and vocabularies such
organsiation in India. The other core ele-
as the Organisation ontology and the SKOS
ment of the Bharathi ontology is the use
vocabulary to define the building blocks for
of skos:Concept to represent the classifica-
the metadata pertaining to government or-
tion hierarchy to denote one of the three
ganisations, administrative regions, gover-
classifications namely –an organisation
nance sectors and the classification hierar-
classification, a governance sector or a topic
chy linking these entities. Figure 1 illustrates
within a sector. Separate Concept Schemes
the domain model for the Bharathi ontol-
(skos:ConceptScheme) have been defined to
ogy. The Bharathi ontology with name prefix
distinguish between these three different
bhorg15 contains the base classes and proper-
types of classifications. Each concept scheme
ties used by the knowledge graph. The selec-
has its own hierarchy starting from a Top
15 bhorg: http://semanticwebindia.in/bharathi/ont/ 16 bhorg:Organisation - http://semanticwebindia.in/
org/ bharathi/ont/org/Organisation
490
Concept (skos:hasTopConcept). An instance 5. Generating Bharathi
of type bhorg:Organisation has the following
object properties 1) org:hasSubOrganization Bharathi is built using a semi-automated ex-
–inherited from the org:Organization class traction pipeline that periodically crawls the
and used to link sub organisations 2) Government of India web sites mentioned in
org:subOrganizationOf –inherited from the Section 3, annotates the semantic entities and
org:Organization class and used to link serializes the knowledge graph tuples in the
to the parent organisation 3) org:linkedTo form of a N-Triples file17 . The knowledge
–inherited from the org:Organization class graph is generated inline with the Bharathi
and used to link related organisations and model explained in Section 4. Each of these
4) bhorg:hasSpatial –property defined in steps is explained in detail below:
the extended class to link to the GeoNames
Feature (gn:Feature) associated with the 5.1. Extraction of Metadata
instance. The bhorg:Organisation entity also
has data properties to associate the names The source files exists in the form of either
(primary and alternate), URL, abbreviations HTML pages or CSV (Comma Separated Val-
and codes with the organisation. All the ex- ues) files. The extractor is customised for
tended classes and properties of the Bharathi each of the source web sites to take care of
ontology have been provided with detailed the specific nuances and organisation of in-
annotations in line with RDF principles. formation within the respective source web
Refer Figure 1, the oval objects represent site. Note that this feature of the extraction
entities and the corresponding rectangular pipeline can be extended in the future to in-
entities linked by rdf:type (a) labelled link clude more sources of web sites as identified.
(black dashed link in the figure) indicate the The information extracted mainly consists of
respective instances. All government or- the government entity, its descriptors (such
ganisations and administrative regions are as name, URL, abbreviations), the classifica-
instances of the bhorg:Organisation entity tion of the organisation and the list of sub or-
while organisation classifications, gover- ganisations. This information is consolidated
nance sectors and their hierarchical struc- from various sources into a large single data
tures are defined using the SKOS ontology. set (CSV file). The information regarding sec-
As illustrated in Figure 1, “Science and Tech- tor, sub sector and related topics is extracted
nology Department West Bengal” is a gov- into a separate CSV dataset as this data struc-
ernment entity having classification “State ture is different from the extraction format
Department” and is a sub organisation in used for the government organisations.
the “State” of “West Bengal”. The adminis- The administrative regions, sectors, top-
trative region “West Bengal” is linked to a ics and other organisations do change peri-
GeoNames Feature using the corresponding odically (name changes, addition of new dis-
GeoNames knowledge base. While the SKOS tricts etc.). These changes are handled us-
concepts “State” and “State Department” be- ing the Provenance Ontology18 . Wherever
long to the concept scheme “Organisation the source mentions a version, the same is
Classification”, the SKOS concept “Science recorded in Bharathi with a history of ac-
Technology & Research” belongs to the con- tivity that triggered the change. URL links
cept scheme “Governance Sector” and is as- 17 https://www.w3.org/TR/n-triples/
sociated with the organisation “Science and 18 PROV-O: The PROV Ontology –https://www.w3.
Technology Department West Bengal”. org/TR/prov-o/
491
Figure 2: represents the overall architecture for the generation of Bharathi Knowledge Graph.
generated for the Bharathi entities are static. used to create and serialize the tuples us-
They don’t change for the entities once de- ing the Bharathi ontology definitions dis-
fined in Bharathi thus assuring continuity to cussed in Section 4. Unique persistent URLs
the users of Bharathi. are generated for each instance following
the RDF principles. The knowledge graph
5.2. Entity Annotations can be accessed at http://semanticwebindia.
in/bharathi/BharathiKG. Figure 2 illustrates
A unique entity is created for each new in- the overall architecture for the generation of
stance of a government organisation or a the Bharathi knowledge graph.
classification depending upon the attributes
of the entity extracted. GeoNames web ser-
vices are used to link administrative regions 6. Real World Applications
to its corresponding GeoNames feature. The using Bharathi
entity annotation routine is sensitive enough
to detect that entities with the same label We present two real world applications using
(hasName or prefLabel) need not represent the Bharathi knowledge graph.
the same entity in the Bharathi knowledge
graph. For eg. “Chandigarh” is a Union Ter-
ritory, a District as well as a Smart City in In-
6.1. Web Interface to Bharathi
dia. To distinguish this aspect, surrounding This is a web application interface for users
attributes of the extracted information from outside the scientific community to search,
the source web site are used to make the dis- browse and explore the entities and their re-
tinction. We use three versions of the Entity lationships in Bharathi. The users are al-
Annotator, one each for government organi- lowed to search for a particular entity or find
sations & administrative regions, one for or- it through one of its classifications. The in-
ganisation classifications and one for gover- formation is organised in a hierarchical man-
nance sectors and topics. ner to understand the hierarchical classifica-
tion of each entity in the government func-
5.3. Serialization of the tion. Further, the linked entities can be tra-
versed just as reaching out from one link to
Knowledge Graph
the other in a web page. Each page regard-
We have created a Python 3.6 script to ing a government organisation or an admin-
generate the N-Triples (.nt) file using the istrative region or a classification presents a
CSV file generated in the extraction step. consolidated identity to the entity. This is
SPARQL wrapper rdflib routines have been an important feature of Bharathi. Most of
492
Figure 3: Landing page of Bharathi Application - The External Interface to the Linked Data Vocabulary
for the Indian Context
the metadata used for the Indian context is “Chandigarh - Smart City”. The right pane
spread across many sources with the same of the functional page shows the hierarchy
administrative region sometimes referred by of the selected entity.
its code or its various names or its abbrevia-
tion. 6.2. Semantic Covid19 India
Further, provenance history is
Analysis
available for public view at http:
//semanticwebindia.in/bharathi/download. This application has been built by harness-
Also, version history for a semantic entity in ing the semantic framework of Bharathi.
Bharathi can be viewed at its functional link Covid 19 datasets are sourced from multi-
e.g. (http://semanticwebindia.in/bharathi/ ple public data sources such as https://www.
home?id=14900&type=org). A feedback covid19india.org/ for covid data related to In-
page has been provisioned to capture the dia and https://datahub.io/core/covid-19 for
user’s inputs on how the information can covid data related to other countries. A de-
be enhanced with additional metadata from tailed entity disambiguation routine identi-
other sources. Figure 3 shows the landing fies the linkable semantic entities from the
page of Bharathi for the external user. Users raw Covid19 datasets. The Covid19 indica-
can search for an administrative region, tors such as “Total Confimed”, “Total De-
government organisation or a sector / sub ceased”, “Total Recovered”, “Total Tested”
sector. Bharathi also holds a modest set of etc. were created as sub topics under the
common topics found in public data sites Sector - “Health”, “Sub Sector” - "Covid19".
published by the government. Figure 4 Each dataset refers to a geographical loca-
shows the functional page for the entity tion for which the indicators have been col-
493
Figure 4: Functional page of a Bharathi Entity
lected and these could be at the state or the erarchy of the administrative regions, with
sub state Level (eg. districts, cities). These ge- minimal effort, the Covid19 application facil-
ographical entities are the administrative re- itates aggregation of the various indicators
gions already existing in Bharathi. Using the at the state level (all districts within a state
semantic links of the administrative regions have a hierarchical relation to their parent
with GeoNames resources and their classi- state) and analysis of Covid19 transmission
fications such as “State” or “District” in In- across states sharing boundaries. The DBpe-
dia, we are able to answer complex intuitive dia knowledge graph is linked to the GeoN-
Covid19 Analysis questions such as 1) What ames knowledge base and the GeoNames en-
is the trend of “Total Confirmed Cases” in tities are linked to Bharathi administrative
the neighbourhood of “District” Coimbatore regions. Therefore, this semantic link en-
(a district in the state of Tamil Nadu in India) ables us to explore the effect of Covid trans-
within a distance of 100 km. 2) How do states mission across states sharing boundaries.
with comparable population density fare on Figure 5 and Figure 6 show a
the “Number of Deaths” in a given time pe- glimpse of Covid19 Analysis India
riod? 3) What are the indicators in the family application and can be accessed at
of “Testing Indicators” that can be compared http://sandhicovid.semanticwebindia.com/.
for a given region and timeline? Each of the
above questions is answered by linking the Analysing Covid19 events : Conceptu-
additional metadata from related knowledge ally an event brings together an activity or
graphs using the linked data properties of the a milestone, a location and a time period in a
Bharathi knowledge graph. single entity. We extended the “Event Class”
Additionally, using the classification hi-
494
Figure 5: Covid19 Analysis India built by harnessing the semantic layer provided by the Bharathi
Knowledge Graph – Landing Page
from the Event ontology19 to define a cus- data available in the Bharathi knowledge
tom “Event” in the Sandhi Covid19 applica- graph. Thus Bharathi enables advanced anal-
tion. Apart from the usual attributes inher- ysis based on entity-centric search, explo-
ited from the standard event ontology, we in- ration and information discovery through
troduced object properties for “predecessor” the linked data properties of the underlying
and ”successor”, thus enabling an in-depth knowledge graph.
analysis of related events in the Covid19
timeline. “Lockdown 1.0”, “Lockdown 2.0” 6.3. Sample SPARQL queries to
etc. were created as individual instances
the Bharathi Knowledge
of the extended Event class. The seman-
tics facilitated by these linked entities enable Graph
a detailed “Event Based Analysis” using the Here we present an assorted list of SPARQL
Covid19 data. queries to the knowledge graph and describe
Figure 7 shows “Event Based Analysis” their expected result.
feature of Covid19 Analysis India applica- The SPARQL query in Table 1 returns all
tion. the top level organisation classifications in
Note that this semantic layer can be ex- the Government of India.
tended to any collection of open datasets The SPARQL query in Table 2 returns
containing entities pertaining to the Indian all the States and Union Territories of In-
context that can be linked to the meta- dia along with their lat-long coordinates by
linking Bharathi knowledge graph with the
19 The Event Ontology: http://motools.sourceforge. GeoNames knowledge base.
net/event/event.html
495
Figure 6: Covid19 Analysis India built by harnessing the semantic layer provided by the Bharathi
Knowledge Graph – Compare a Neighbourhood
Table 1 Table 2
SPARQL Query requesting the top level Organi- SPARQL Query requesting all the States and
sation Classifications in Government of India Union Territories of India along with their lat-long
coordinates by linking Bharathi knowledge graph
PREFIX skos: with the GeoNames knowledge base
PREFIX bhorg:
PREFIX org:
select distinct ?c ?cpreflabel ?c1 ?c1preflabel
from PREFIX gn:
where { PREFIX skos:
?c a skos:Concept. PREFIX bhorg:
?c skos:inScheme PREFIX org:
. PREFIX wgs84_pos:
skos:hasTopConcept ?topc. select ?org1 ?gc ?classname ?name ?lat ?long
?c skos:broader ?topc. where
?c1 skos:broader ?c. {
?c skos:prefLabel ?cpreflabel. ?org1 a bhorg:Organisation. ?org1 bhorg:hasName ?name.
?c1 skos:prefLabel ?c1preflabel. ?org1 bhorg:hasSpatial ?gc.
} ?org1 org:classification ?c.
?c skos:prefLabel ?classname. FILTER (?classname IN
("State", "Union Territory", "National Capital Territory")).
?gc wgs84_pos:lat ?lat.
?gc wgs84_pos:long ?long
}
order by ?name
The SPARQL query in Table 3 returns all
the government organisations and their sub
organisations belonging to the classification
- "Legislature of India".
496
Figure 7: Covid19 Analysis India built by harnessing the semantic layer provided by the Bharathi
Knowledge Graph – Event Based Analysis
Table 3 cilitates the semantic integration of public
SPARQL Query requesting all the Government datasets related to governance data avail-
Organisations and their Sub Organisations be- able on the public domain. The semantic
longing to the classification - "Legislature of In- linking of entities using Bharathi for public
dia" data referring to commonly occuring meta-
data such as government organisations, ad-
PREFIX skos:
PREFIX bhorg:
ministrative regions, commonly used gover-
PREFIX org:
nance sectors and related topics transform
select distinct ?c1 ?c ?classname ?org1 ?org1name ?sub ?suborg
?subclass ?subclassname
the public datasets into a rich semantic web
from
where {
of knowledge that can be intuitively explored
?org1 a bhorg:Organisation. ?org1 org:classification ?c.
?c skos:prefLabel ?classname.
and analysed by exploiting the inter relation-
?c skos:broader ?c1. ?c1 skos:prefLabel "Legislature".
FILTER NOT EXISTS {?org1 org:subOrganizationOf ?parent}.
ships across information from diverse data.
?org1 bhorg:hasName ?org1name.
OPTIONAL {?org1 org:hasSubOrganization ?sub. ?sub bhorg:hasName
Bharathi is expected to provide the much
?suborg. ?sub org:classification ?subclass.
?subclass skos:prefLabel ?subclassname}
needed break through in connecting public
}
data specifically for the Indian context since,
to the best of our knowledge, such a large
scale publishing of linked information for
commonly used metadata in India has not
7. Conclusions and Future been made available till now.
Work
Comparisons with DBpedia : DBpedia
We have introduced Bharathi - Linked Data is project to convert structured content of
Vocabulary for the Indian context, which fa- Wikipedia into linked data and does contain
497
a lot of information regarding India docu- ernment data, in: Linking government
mented in Wikipedia. However, this infor- data, Springer, 2011, pp. 205–219.
mation is largely incomplete and does not [4] L. Ding, T. Lebo, J. S. Erickson,
align with some of the most credible updates D. Difranzo, G. T. Williams, X. Li,
published by the Government of India (GoI) J. Michaelis, A. Graves, J. G. Zheng,
websites. Bharathi is an effort to exclusively Z. Shangguan, et al., Twc logd: A
extract information from GoI websites and portal for linked open government data
maintain its currency. ecosystems, Journal of Web Semantics
There are several limitations of the 9 (2011) 325–333.
Bharathi knowledge graph that are the [5] L. Ding, D. DiFranzo, A. Graves, J. R.
focus of ongoing and short-term efforts. Michaelis, X. Li, D. L. McGuinness, J. A.
Bharathi can be enriched with reasoning Hendler, Twc data-gov corpus: incre-
statements from the OWL vocabulary to mentally generating linked government
enable reasoning and creation of new facts data from data. gov, in: Proceedings
using reasoners such as Apache Jena. We of the 19th international conference on
also intend to extend the content of our World wide web, 2010, pp. 1383–1386.
graph to other sources of information in [6] T. Lebo, G. T. Williams, Converting
collaboration with the Government of India governmental datasets into linked data,
to facilitate a larger variety of instances in in: Proceedings of the 6th International
Bharathi. Finally, our continued efforts on Conference on Semantic Systems, 2010,
Bharathi knowledge graph aims to build a pp. 1–3.
multi-lingual search across Indian languages [7] J. Höchtl, P. Reichstädter, Linked open
to enable larger support and patronage for data-a means for public sector infor-
Bharathi. mation management, in: International
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