Toward Scalable Semantic Big Data

Julian Dolby

IBM Thomas J. Watson Research Center

SPLASH-I, Vancouver, October 2017

Collaborative Work

Outline

• Running Example
• Scalable expressive reasoning
• Storing RDF data in a database
• Integration of Web data

Running Example

Objects in Our Universe

Example OWL Universe

• Individuals
  $\begin{array}{l}Museum(Athens), Museum(Heraklion), \\ Museum(MOMA), Minoan(LaParisienne),\\ Mycenean(DeathMask),VanGogh(StarryNight)\end{array}$
• Roles
  $\begin{array}{l} has(Athens,DeathMask),has(MOMA,StarryNight)\\ has(Heraklion,LaParisienne) \end{array}$
• Axioms (TGDs)
  $\begin{array}{l} Minoan \sqsubseteq \exists{creationSite.Crete}\\ Mycenaean \sqsubseteq \exists{creationSite.Mycenae} \end{array}$

Example ABox $A$

The Summary ABox

• Map ABox $A$ to $A'$ for scalability using $f$
  $\begin{array}{l} C(a) \in A \implies C(f(a)) \in A'\\ R(a,b) \in A \implies R(f(a), f(b)) \in A' \end{array}$
• We choose concept sets as f

Example Summary ABox $A'$

Pointer Analysis Analogy

class Cell {
 private Object datum;
 Cell(Object d) { datum = d; }
 
 static void catInTheHat() {
  Cell x = new Cell("Thing 1");
  Cell y = new Cell("Thing 2");
 }
}

Heap

Type-Based Abstraction

Example Query

• "Museums that have works from Crete"
• Answer is Heraklion since has Minoan LaParisienne
• DL
  • $Museum \wedge \exists{has.\exists{creationSite.Crete}}$
  • Negate query at each node, find contradictions
  • Entities, edges in contradiction called justification
• Needs DL reasoning: creationSite edge is implicit

Initial Query Answer

Refinement

• J is the justification, i.e. the conflict
• Partition summary nodes by edges in justification
  $key(a) \equiv \left\{ R(s, t) \left| \begin{array}{l} f(a) = s \wedge\\ R(s,t) \in J \wedge\\ \exists b \; R(a,b) \in A \wedge f(b) = t \end{array} \right. \right\}$

Query Refinement

Refined Query Answer

Pointer Analysis Analogy

class Cell {
 private Object datum;
 Cell(Object d) { datum = d; }
 
 static void catInTheHat() {
  Cell x = new Cell("Thing 1");
  Cell y = new Cell("Thing 2");
 }
}

• Pointer analyis query: “what points to ‘Thing 1’?”
  • refinement in the style of Plevyak or Sridharan

Reasoning Results

RDF in a Relational Store

• Numerous large RDF data sources
• DBPedia (>300M triples)
• Web data (>3B triples from BTC)
• Exploit scalable RDBMS technology
• query optimization
• transaction support
• concurrency
• Quetzal
• https://github.com/Quetzal-RDF/quetzal
• Loosely-derived code in DB2 v10.1

Museums with Locations

Quetzal Schema

• Entity-oriented schema
• properties for subject on single row
• rows for predicates and values
• secondary table for multi-valued predicates
• Fit entities onto limited database rows
• generally more predicates than available rows
• graph coloring to maximize density
• spill onto multiple rows only when necessary
• Analogous tables for reverse direction
• Reduces joins for "star" queries

Example Entity-Oriented Table

Register Allocation Analogy

var x = 7
var y = x + 7     
var z = 3
y = y + z

Quetzal Results on LUBM

Data Everywhere

• Numerous structured data sources available
  • medical (Drugbank, Uniprot); general (DBpedia)
  • much data in RDF, queried with SPARQL
  • But data increasingly diverse
• RDF, XML, JSON, CSV formats
  • accessible as dumps, query endpoints and APIs
  • Powerful if integrated and queried effectively
  • reuse and extend existing declarative SPARQL

Modularize SPARQL with Functions

• "museums with some type of exhibit"

function museumsWith(?type ->
                     ?museum ?lat ?long) {
  ?museum has ?art .
  ?art type ?type .
  ?museum at ?loc .
  ?loc geo:lat ?lat .
  ?loc geo:long ?long .
}

• Functions called with bind

bind ?museum ?lat ?long
  as museumsWith(Minoan) .

Web Service http://ip-api.com/

• Web service returns IP-based information

<query>
 <status><![CDATA[success]]></status>
 <country><![CDATA[United States]]></country>
 <countryCode><![CDATA[US]]></countryCode>
 <region><![CDATA[IL]]></region>
 <regionName><![CDATA[Illinois]]></regionName>
 <city><![CDATA[Chicago]]></city>
 <zip><![CDATA[60605]]></zip>
 <lat><![CDATA[41.8632]]></lat>
 <lon><![CDATA[-87.6198]]></lon>
 <timezone><![CDATA[America/Chicago]]>&</timezone>
 <isp><![CDATA[AT&T Services]]></isp>
 <org><![CDATA[Hilton Hotels Corporation]]></org>
 <as><![CDATA[AS7018 AT&T Services, Inc.]]></as>
 <query><![CDATA[12.218.232.8]]></query>
</query>

Web Service Example

• Web service to return latitude and longitude

function geo:getPosition( -> ?lat ?long)
service get http://ip-api.com/xml [] -> xml
"/query": "./lat" "./long"

• getPosition used with bind

select ?lat ?long where {
  bind ?lat ?long as geo:getPosition()
}

Combining Disparate Data

• "Nearby Minoan exhibits"
• RDF museums, http://ip-api.com locations

select ?museum where {
  bind ?museum ?lat1 ?long1
    as museumsWith(Minoan) .
  bind ?lat2 ?long2 as geo:getPosition()
  FILTER(?lat2-?lat1 < .1 &&
         ?long2-?long1 < .1)
}

The Future

• Further extensions to SPARQL
• further modularization, e.g. module systems
• potential for first-class functions
• Semantic big data means reasoning
• put summarization, refinement into Quetzal
• exploit entity-oriented schema for summarization
• Would love others to get involved