This document discusses using semantic web technologies to help make sense of big data by linking and integrating heterogeneous data sources. It presents a self-adaptive natural language interface model that takes a natural language query as input, considers possible concept annotations and SPARQL query patterns, runs the queries, and returns results to a reasoner to identify the correct query and answer. The model was tested on geography and Quran ontologies and was able to correctly answer questions with different SPARQL patterns. The conclusion discusses how semantic web and linked data can help analyze big data and create more personalized applications.

1. Self-Adaptive Based
Natural Language Interface
for Disambiguation of
Semantic Search
NURFADHL INA MOHD SHARE F NURFADHL INA@UPM.EDU.MY
MOHAMMAD YAS S ER SHAFAZAND 79. ZAND@GMAI L .COM
FACULT Y OF COMPUT ER SCI ENCE AND INFORMAT ION T ECHNOLOGY,
UNIVERS I T I PUTRA MALAYS IA
S ERDANG, S E LANGOR, MALAYS IA

2. "Big Data" refers to data sets whose size is beyond the ability of
typical database software tools to capture, store manage and analyze
(McKinsey).
“Linked Data” stands for semantically well structured,
interconnected, syntactically interoperable datasets that are
distributed among several repositories either inside or outside
organisations http://www.semantic-web.at/big-data-linked-data

3. Utilizing Linked Data and Big Data for organisational and
enterprise purposes will be one of the next big challenges in
the evolution of the web.
Big Data takes account of the fact that new techniques and
technologies are needed for the sustainable and socially
balanced exploitation of huge data pools. The Linked Data
paradigm is one approach to cope with Big Data, as it advances
the hypertext principle from a web of documents to a web of
rich data.

5. Semantic Web: a webby way to link data
Open Data meets the Semantic Web: Linked Open Data
http://www.semantic-web-journal.net/system/files/swj488.pdf

6. One of the key challenges in making use of Big Data lies in
finding ways of dealing with heterogeneity, diversity, and
complexity of the data, while its volume and velocity forbid
solutions available for smaller datasets as based, e.g., on
manual curation or manual integration of data. Semantic
Web Technologies are meant to deal with these issues,
and indeed since the advent of Linked Data a few years ago,
they have become central to mainstream Semantic Web
research and development.
We can easily understand Linked Data as being a part of the
greater Big Data landscape, as many of the challenges are
the same. The linking component of Linked Data, however,
puts an additional focus on the integration and conflation
of data across multiple sources.

7. Volume Velocity Variety
BIG DATA
Value and
Veracity
Supercomputing
Internet of Things
Semantic Web
Social Science

8. Smart Data
Smart data makes sense out of Big data
http://amitsheth.blogspot.com/2013/06/transforming-big-data-into-smart-data.html
It provides value from harnessing the challenges posed by
volume, velocity, variety and veracity of big data, in-turn providing actionable
information and improve decision making.
uses background knowledge, experiences, advanced and contextualized
reasoning, and is often highly personalized
focused on the actionable value in data creation, processing and consumption
phases for improving the human experience

9. 5 steps to Turn Big Data into Smart Data
http://tdwi.org/Articles/2014/07/15/Turning-Big-Data-into-Smart-Data-2.aspx?Page=1
1. Add meaning
2. Add context
3. Embrace Graphs
4. Iterate
5. Adopt standard

10. Natural Language Query Generated SPARQL
What is the lowest point in kansas? SELECT ?c0
WHERE {
?c0 ?p0 ?i0 . ?c0 a geo:LoPoint .
filter (?i0 = geo:kansas) .
filter ( ?p0 = geo:isLowestPointOf ) .
}
What is the area of idaho? SELECT ?i0
WHERE {
?c0 ?p0 ?i0 .
filter (?c0 = geo:idaho) .
filter ( ?p0 = geo:stateArea ) .
}
what states border oklahoma? SELECT ?i0
WHERE {
?c0 ?p0 ?i0 . ?i0 a geo:State .
filter (?c0 = geo:oklahoma) .
filter ( ?p0 = geo:borders ) .
}
what is the population of oregon? SELECT ?i0
WHERE {
?c0 ?p0 ?i0 .
filter (?c0 = geo:oregon) .
filter ( ?p0 = geo:statePopulation ) .
}

11. Ambiguities in Querying Big Data
when there are more than one possible concept annotation for a word in the
NL input
when a word inside the NL input cannot be matched with any KB concept
when constructing the SPARQL where there is more than one possibility of
SPARQL pattern

12. Self Adaptive Model for Semantic Data Search in Big Data

13. Input: NL query
Output: Answer
Process:
1. Load ontology and build a matrix of the object properties, classes and instances and its
connections
2. Let T as the tokenized and stemmed NL query
3. For each tT, let A be the set of annotation based on relevant concepts
4. For each aA
a. Create and add possible triplets, filters and options statements using dictionary
and reasoner (using bottom up reasoning rules)
b. Create new SPARQL syntax using (4(a))
c. Run SPARQL and send statements and results to reasoner.
5. Return last created SPARQL syntax which has results.

14. Results
The SANLI is tested on two different datasets namely the Mooney’s Geography
ontology and a Quran structure ontology.
SANLI is able to correctly answer all questions in the geography ontology
where the questions have <s, p, o>, <o, p, s>, <p, o>, <o, p> and <o > patterns
identified.
Rules for other patterns have not yet been implemented. For example <o, p, o>
patterns mostly result in a true false result as in “Does Texas border Oklahoma?”
which we have not implemented yet.

15. Conclusion
The Semantic Web can leverage the sophisticated analytics with big
data.
Big Data and Linked Data will be an integral part of the future web
infrastructure, where massive amounts of data are available,
connected and identifiable via Uniform Resource Identifiers.
More personalized-based applications to exploit smart data to its
maximum potential