Self adaptive based natural language interface for disambiguation of
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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.
Self adaptive based natural language interface for disambiguation of
- 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