Big Data and the Semantic Web: Challenges and Opportunities
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The document discusses challenges and opportunities at the intersection of big data and the semantic web. It notes that while semantic web technologies can help make sense of large, diverse datasets, building semantic models from big data poses challenges. A global ontology cannot capture all perspectives, and semantic queries rely on contextual relevance and assumptions. Storing and querying large semantic graphs efficiently also presents technological hurdles.
![Big Data Tech Conclave, 26—27 April 2013
Bangalore, India
Random Walks on Big Data
Large scale graph processing solutions for
scaling random walks over Big Data:
● Apache Giraph http://giraph.apache.org/
● Pregel [Malewicz et al., 2010]
● Grappa http://www.cs.washington.edu/node/4217/](https://image.slidesharecdn.com/bigdatasemantics-130425024932-phpapp02/85/Big-Data-and-the-Semantic-Web-Challenges-and-Opportunities-39-320.jpg)
![Big Data Tech Conclave, 26—27 April 2013
Bangalore, India
MCMC
Markov Chain Monte Carlo
Given a state space S and an “equilibrium” distribution
choose a sample s of the state space S so that a Markov chain
on s results in as the stationary distribution
MCMC for logical inference
For a logical inference problem, the equilibrium condition
would be of the form [0,1]m
defined over a set of m predicates
Example Sampling algorithms for MCMC
Gibbs Sampling http://en.wikipedia.org/wiki/Gibbs_sampling
MetropolisHastings algorithm
http://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm](https://image.slidesharecdn.com/bigdatasemantics-130425024932-phpapp02/85/Big-Data-and-the-Semantic-Web-Challenges-and-Opportunities-42-320.jpg)
![Big Data Tech Conclave, 26—27 April 2013
Bangalore, India
Scaling MCMC for Big Data
Distributed MCMC
Several models are explored for distributing MCMC computations
over large datasets making them amenable to diffusing
computations. Some examples include: [Murray 2010; Singh et al
2011]
Distributional models for MCMC beyond the scope of this talk..](https://image.slidesharecdn.com/bigdatasemantics-130425024932-phpapp02/85/Big-Data-and-the-Semantic-Web-Challenges-and-Opportunities-43-320.jpg)
Big Data and the Semantic Web: Challenges and Opportunities
- 1. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Big Data and the Semantic Web: Challenges and Opportunities Srinath Srinivasa Open Systems Laboratory IIIT Bangalore http://osl.iiitb.ac.in/ sri@iiitb.ac.in
- 2. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India http://www.bda2013.net/
- 3. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India OSL Releases Topical Anchors: Given a list of noun phrases, identify a semantic topic for these terms. Powered by Wikipedia cooccurrence graph hosted by Agama Web APIs enable use of Topical Anchors in third party applications
- 4. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India OSL Releases Topic Expansion: Given a term, expands it into semantically relevant topical clusters with different senses. Uses co-occurrence datasets from Wikipedia 2006 or 2011. Web APIs enable use by third party applications
- 5. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India OSL Releases Agama: A graph database for storing large undirected graphs for efficient traversal (not structurebased retrieval) Currently Agama powers a co occurrence graph of all noun phrases from Wikipedia articles hosted in OSL, managing 10s of millions of nodes and 100s of millions of edges
- 6. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India More data beats better algorithms.. meets No data is an island..
- 7. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Outline ● Big Data Characteristics ● Big Data Analytics ● Patterndriven and Modeldriven Analytics ● Big Data and the Semantic Web ● Semantic Challenges ● The myth of a global ontology ● Convergent and divergent semantics ● Semantic interoperability ● Technology Challenges ● Storage, traversal and retrieval of largescale semantic networks ● Inference on Big Data ● On the road ahead
- 8. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Big Data Data that is ● Too large to be processed by conventional databases and data management techniques (Volume) ● Too diverse in structure that no single data model captures all elements of the data (Variety) ● Transient and/or impermanent, especially when pertaining to dynamic phenomena (Velocity)
- 9. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Big Data ● Transaction records ● Network streams ● Experimental output ● Social media data ● Demographic records ● Citation data ● Clickstreams ● Log data ● Weather data ● …
- 10. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Some Big Data Stats ● YouTube users upload 48 hours of video every minute http://gigaom.com/2011/05/25/youtube48hoursofvideoperminute/ ● Facebook data grows by 500TB daily http://www.slashgear.com/facebookdatagrowsbyover500tbdaily23243691/ ● WalMart handles more than 1 million customer transactions every hour http://www.economist.com/node/15557443 ● Akamai analyzes 75 million events per day for targeted advertising http://wikibon.org/blog/tamingbigdata/ ● 90% of data in the world today was created in the last 2 years http://wikibon.org/blog/bigdatainfographics/
- 11. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Big Data Analytics Examine Big Data for useful (often actionable) knowledge The long spectrum of Big Data Analytics Pattern identification Association rule mining Classification/Clustering Record Linkage Security analytics Complex Event Processing Opinion mining Predictive modeling Pattern driven Model driven
- 12. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Pattern Driven Analytics ● Discovery and visualization of recurring patterns in datasets ● Mostly quantitative ● Paradigms in pattern discovery: ● Sampling and aggregation ● Thresholding and filtering Image Source: Wikipedia
- 13. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Pattern Driven Analytics Sampling and Aggregation ● Query based pattern aggregation ● Based on an initial idea of what we are looking for Hypothesis Data Query Patterns Aggregation Presentation
- 14. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Pattern Driven Analytics Tresholding and Filtering ● Based on sifting through the entire dataset (or a view) to look for “interesting” patterns without the context of a query Data Interestingness criteria Patterns Filtering and Segregation Presentation
- 15. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Model Driven Analytics Analytics as a modeldiscovery problem Wedding Images source: Wikipedia Observable Data Latent Concept
- 16. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Model Driven Analytics ● Pattern discovery coupled with semantic modeling ● Nontrivial qualitative modeling challenges ● Model discovery: ● Descriptive model discovery Fit a model to explain the observed data ● Predictive model discovery Discover a model that can predict values of data elements into the future
- 17. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Linked Data Image source: Wikipedia The Linked Data Cloud as of September 2011
- 18. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Linked Data ● Using Semantic Web technologies to connect data elements from disparate data sources ● From Web of Documents to Web of Data ● Elements of Linked Data ● URIs ● HTTP ● Resource Description Framework (RDF) ● Serialization formats (RDFa, RDF/XML, N3, Turtle, and others)
- 19. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Big Data and the Semantic Web Big Data Semantic Web Model Discovery Catalyzation and Predictive Modeling
- 20. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Big Data Semantic Web ● One of the main elements of the Linked Data Cloud: DBpedia is built from a Big Data resource: Wikipedia ● Open Biomedical Ontology (OBO) (http://www.oboedit.org/) created from mining PubMed publications ● Enterprise scale Big Data Analytics helping build organizational models, operational intelligence solutions, etc. Example: Anzo software suite by Cambridge Semantics (www.cambridgesemantics.com), Loom data management suite by Revelytix (www.revelytix.com)
- 21. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Semantic Web Big Data Schema.org ● Collection of schemata on various topics that are recognized by major search providers and used to semantically interpret web content SourceMap ● Linked data augmented with web content and crowdsourced data used to provide details about companies like their carbon footprint, energy use, water use, etc. www.sourcemap.com OpenSteetMap ● Linked data augmenting crowdsourced data on www.openstreetmap.org helped in detailed mapping of disaster scenario during the Jan 2010 Haiti earthquake (http://www.scientificamerican.com/article.cfm?id=bernersleelinkeddata)
- 22. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Big Data and the Semantic Web: Challenges Semantic challenges ● The myth of a global ontology ● Convergent and divergent semantics Technology and system challenges ● Characteristics of a semantic graph ● Managing graph structured data
- 23. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India The Myth of a Global Ontology Several “core” semantic ontologies exist: ● WordNet ● YAGO ● OpenCyc ● SUMO However, none of them (even automated ones) can capture all possible semantic associations and all possible perspectives on a given topic
- 24. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India The Myth of a Global Ontology The open world problem ● We don't know what we don't know.. ● Representation bias in big data sources The neutralbutuseless perspective ● Localized, utilitarian descriptions often more useful than neutral, global descriptions. Ex: Use of “zones” as a geographical element in Indian Railways ● Difficult for disparate perspectives to coexist in a single Ontology, violating design principles like Occam's razor
- 25. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Convergent and Divergent Semantics Wikipedia article on West Bank conflict Palestine POV Israeli POV Historians' POV UN's POV Encyclopedic Semantics
- 26. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Convergent and Divergent Semantics IPL event schedule Traffic planning Advertisement planning around IPL Legal structuring around IPL TV programme scheduling Security planning
- 27. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Semantic Interoperability ● Binary predicates like RDF may not capture complete semantics of the association But it is too difficult to work with higherorder predicates ● Semantic queries are characterized by contextual relevance and default assumptions ● Linked Data can be useful primarily within the context of a model Modelbuilding from predicates as complex a problem as identifying predicates from data
- 28. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Semantic Challenges: Summary ● Hard to distinguish data from noise without a model Especially hard when we are using data to help build a model! ● There may not be a single global model explaining the data ● Model construction as challenging, if not more challenging, as predicate mining ● No clarity on the underlying processes that aid in knowledge aggregation Knowledge aggregation happens differently depending on the kind of knowledge being aggregated (encyclopedic versus operational knowledge)
- 29. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Tech Challenges Storing Big Semantic Data ● Semantic data not amenable to physical access coherence to be efficiently stored in relational tables ● Logical proximity of triples, more important than physical proximity ● Read/Write storage models change logical proximity ● RDF graphs tend to be extremely dense and/or clustered ● Need efficient methods of graph storage and retrieval
- 30. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Semantic store for Big Data ● Databases optimized to store and retrieve interrelated sets of triples of the form (subject, predicate, object) ● Query models based on answering graph queries (usually in SPARQL) rather than SQL queries ● Main design criteria: storage and readahead policies of triples based on their logical proximity rather than physical proximity in order to enable Bulk Synchronous Parallel (BSP) processing
- 31. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Semantic store for Big Data AllegroGraph (http://www.franz.com/agraph/allegrograph/) ● NoSQL Graph based native storage for RDF triples ● ACID compliant ● Interfaces with Solr for free text indexing ● Triple and text level indexing ● MongoDB integration ● RDFS++ Reasoning with dynamic materialization ● SPARQL queries on named graphs and Prolog based inferencing engine
- 32. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Semantic store for Big Data Sesame http://www.openrdf.org/ ● Open source Java framework for parsing, storing, querying and inferencing over RDF data ● Collections of RDF triples can be manipulated in memory using a graph data model ● Compliant with SPARQL 1.1 protocol recommendation ● Provides two levels of APIs: SAIL (Storage and Inference Layer) for low level RDF processing and Repository layer for programmatic interfacing with Sesame
- 33. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Semantic store for Big Data Mulgara http://www.mulgara.org/ ● Native storage model for RDF ● Supports multiple models (databases) per server ● ACID transactions and concurrency support ● Copyonwrite cache semantics ● Fulltext search and support for data types ● Primarily useful as a repository – no evidence of support for logical inferences over RDF
- 34. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Semantic store for Big Data Other examples: ● InfiniteGraph from Objectivity http://www.objectivity.com/ ● BigData http://www.bigdata.com/bigdata/blog/ – A high scaleout storage and computing engine ● Agama https://github.com/arrac/agama/wiki/Agama – Storage, search and traversal support (Ruby library) for very large graphs ● Neo4j http://www.neo4j.org/ – Embedded, diskbased transactional graph database written in Java
- 35. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Logical inference over Big Data ● Problem: Find factual answers to specific questions by reasoning over largescale data. ● Performing extremely largescale deductions over large semantic datasets in interactive response time ● Need to contend with potentially inconsistent predicates, incomplete or missing values and default assumptions ● Varieties of inference over datasets ● Deduction ● Induction ● Abduction ● Statistical inference
- 36. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Logical inference over Big Data Common approaches for scalable inferencing: ● Horn clause inferencing ● Variants of random walks on knowledge graphs ● Distributed MCMC (Markov Chain Monte Carlo) methods
- 37. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Horn Clauses Horn clauses are predicates of the form: atomic sentence with no negation and a single consequent Horn clause knowledge bases can be resolved using “backward chaining” starting from the consequent and building a tree of antecedents until they are grounded in facts Horn clause resolution can be scaled over large datasets by parallelizing resolutions using MapReduce p1∧p2∧...∧pn →u
- 38. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Random Walks on Big Data Random walks on RDF graphs as a means of: ● Belief materialization ● Soft inference a c e d f b R R R R Assuming transitivity of R
- 39. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Random Walks on Big Data Large scale graph processing solutions for scaling random walks over Big Data: ● Apache Giraph http://giraph.apache.org/ ● Pregel [Malewicz et al., 2010] ● Grappa http://www.cs.washington.edu/node/4217/
- 40. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India MCMC A “generic” problem solving method based on local sampling, useful for soft inferences on semantic data Time homogeneous Markov Chain:
- 41. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India MCMC A homogeneous Markov chain can be represented as a set of “states” and “transition probabilities” across states Given an initial “prior” probability distribution across states the “stationary distribution” or “equilibrium condition” is defined as:
- 42. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India MCMC Markov Chain Monte Carlo Given a state space S and an “equilibrium” distribution choose a sample s of the state space S so that a Markov chain on s results in as the stationary distribution MCMC for logical inference For a logical inference problem, the equilibrium condition would be of the form [0,1]m defined over a set of m predicates Example Sampling algorithms for MCMC Gibbs Sampling http://en.wikipedia.org/wiki/Gibbs_sampling MetropolisHastings algorithm http://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm
- 43. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Scaling MCMC for Big Data Distributed MCMC Several models are explored for distributing MCMC computations over large datasets making them amenable to diffusing computations. Some examples include: [Murray 2010; Singh et al 2011] Distributional models for MCMC beyond the scope of this talk..
- 44. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India On the road ahead.. Some promising directions for Big Data and Semantics ● Diffusion models for large scale inference ● Cognitive models for semantics over large scale data ● Modelbased reasoning and reasoning across models ● Soft (probabilistic) inferences, confidence measures, relevance feedback ● Continuous learning over Big Data
- 45. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India Thank You!
- 46. Big Data Tech Conclave, 26—27 April 2013 Bangalore, India References ● Neal Madras. Introduction to Markov Chain Monte Carlo. http://www.cs.cornell.edu/selman/cs475/lectures/intromcmclukas.pdf ● Grzegorz Malewicz, Matthew H. Austern, Aart J.C Bik, James C. Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010. Pregel: a system for largescale graph processing. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data (SIGMOD '10). ACM, New York, NY, USA, 135146. DOI=10.1145/1807167.1807184 http://doi.acm.org/10.1145/1807167.1807184 ● Ni Lao, Tom Mitchell, and William W. Cohen. 2011. Random walk inference and learning in a large scale knowledge base. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '11). Association for Computational Linguistics, Stroudsburg, PA, USA, 529539. ● Lawrence Murray, Distributed Markov Chain Monte Carlo. Proceedings of NIPS 2010 Workshop on Learning on Cores, Clusters and Clouds. http://lccc.eecs.berkeley.edu/ ● Stefan Schoenmackers, Oren Etzioni, and Daniel S. Weld. 2008. Scaling textual inference to the web. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '08). Association for Computational Linguistics, Stroudsburg, PA, USA, 7988. ● Stefan Schoenmackers, Oren Etzioni, Daniel S. Weld, and Jesse Davis. 2010. Learning firstorder Horn clauses from web text. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP '10). Association for Computational Linguistics, Stroudsburg, PA, USA, 10881098. ● Sameer Singh, Amarnag Subramanya, Fernando Pereira, and Andrew McCallum. 2011. Largescale crossdocument coreference using distributed inference and hierarchical models. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies Volume 1 (HLT '11), Vol. 1. Association for Computational Linguistics, Stroudsburg, PA, USA, 793803.