JAKUB J . MOSKAL

MIECZYSLAW “MITCH” M. KOKAR

BRIAN E . UL ICNY

N OV E M B E R 1 9 , 2 0 1 4

Comprehension of RDF Data Using Situation Theory and

Concept Maps

2

Outline

Lots of RDF data. Querying with SPARQL produces complicated RDF graphs

Objective: Generate “simple” Concept MapsSituation Theory (Barwise, Perry, Devlin)STO: Situation Theory OntologyProcess outline and processing stepsExamplesConclusions

3

Abundance of Data

Analysts are required to sift through tremendously large amounts of data Keyword-based queries

yield poor results Structured data is

neededThe number of RDF data

sets is growing rapidly Even though RDF data is

structured, it can be very difficult to analyze

Source: http://lod-cloud.net/

4

Linked Open Data cloud diagramAs of 08/30/2014

Linking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/

5

Example Query

Query: What were the circumstances of Richard H. Barter’s

death?RDF Data:

SPARQL Endpoint: http://dbpedia.org/sparqlSPARQL query:

PREFIX dbpedia-owl: <http://dbpedia.org/ontology/>

DESCRIBE ?resourceWHERE {

?resource dbpedia-owl:abstract ?abstract.FILTER langMatches(lang(?abstract), "EN" ).FILTER REGEX(str(?abstract), "Richard H. Barter")

}LIMIT 10

6

Query Result: RDF graph

7

Our Approach

Objective: Given a query, transform the input RDF graph to a Concept

Map that: Provides answer to the query Contains facts that are relevant to the query (context) Is more abstract than the original RDF graph(easier to

comprehend)

Approach: Use key aspects of Situation Theory of Barwise and Perry

(extended and formalized by Devlin) Map the problem to this theory and implement algorithms

for constructing concept maps based on such a framework

8

Expected Benefits

Increased analyst productivity Easier comprehension Tailored visualization Explanatory facts

Improved quality of analyst products Fewer false alarms More detections of relevant events

Enriched fact base via inference Augmented with situation types and their instances

Integration with other analyst tools Export to standard formats

9

Concept Maps

10

A Bit of Situation Theory

- Infon

- S “supports” Infon

- Situation Type- Abstract Situation

- Definitional Query

- Inferring situations and their types

- “Relevance” – via entailment

11

Situation Theory – Relevance Reasoning

Relevant entities with respect to a given query Q are those entities that are necessary for proving that a specific set of facts SQ supported by a situation satisfies Q.

12

Situation Theory – Why?

It grounds meaning in the world, rather than in the language (unlike in FrameNets)

It allows specifying views of the world (situations) that are globally inconsistent, but locally consistent

Situations are first-class citizens – they have their own relations and attributes

Meaning of a declarative sentence is a relation between utterances and described situations

13

STO Ontology

14

CONOPS

15

Representing Queries(in terms of ElementaryInfon and Situation Type)

“Did an insurgent visit a weapons cache?” Expressible in pure OWL:

InsurgentWeaponsCacheSituation ≡ Situation and (supportedInfon some (ElementaryInfon and (anchor1 some Insurgent) and (anchor2 some WeaponsCache) and (relation value visit)))

“Which insurgents spied on a relative?” Not expressible in pure OWL, requires use of

variables Rules are necessary, for instance:

Situation(s) ∧ ElementaryInfon(i) ∧ Object(a1) ∧ Object(a2) ∧ Relation(spiedOn) ∧ supportedInfon(s, i) ∧ anchor1(i, a1) ∧ anchor2(i, a2) ∧ relation(i, spiedOn) ∧ Insurgent(a1) ∧ Person(a2) ∧ relative(anchor1, anchor2) → RelativeSpySituation(s)

16

Answering Queries: Process

17

A Running Example (based on SynCOIN)

Query: Which known insurgents are connected to people who

have been to a weapons cache? WCSit ≡ Situation and (supportedInfon some (ElementaryInfon

and (anchor1 some Insurgent) and (anchor2 some (Person and hasBeeonTo some WeaponsCache))) and (relation value isConnectedTo)))

Initial facts:

18

(1) Domain Inference

Infer implicit facts about the domain If necessary, add additional axioms to the dataset

We added a few axioms to SynCOIN:

19

(2) Situation Reasoning

Analyze situation type definitions (both in OWL and rules) Extract relevant relations used in definitions (visit and spiedOn

in previous examples). Then extract relevant individuals.For each relation rel that is part of a situation type:

For each pair of individuals a1 and a2 that are associated with each other by the property rel: 1. Assert that there is an individual s of RDF type sto:Situation 2. Assert that there is an individual i of RDF type

sto:ElementaryInfon, supported by situation s 3. Assert the following facts:

(i anchor1 a1), (i anchor2 a2) and (i relation rel)

20

Example – cont.

Initial Graph:

Current Answer:

21

(3) Context Derivation

Derive the context for the answer Find relevant facts: all individuals and relations that are relevant to

the situation that represents the answer to the query

Derivation based on domain-independent rules, which backtrack OWL inference Currently: Property chain, sub-property, transitive property

Example derivation rule for transitive property: For a situation s, and a query q, if s satisfies the query:

For every fact (i1 rel i2) relevant to s and an individual i3, if rel is a transitive property and if (i1 rel i3) and (i3 rel i2) are facts asserted in the knowledge base: 1. Add (i1 rel i3) and (i3 rel i2) as facts relevant to s.

22

Example – cont.

Previous Step:

Current Answer

23

(4) Simplification

Context derivation is likely to produce a lot of “noise” We need to remove facts that are relevant to a situation, but

that are not necessary to comprehend the graph Simplification based on domain-independent rules

Example simplification rule for sub-property relation between relevant relations: For a situation s, and a query q, if s satisfies the query:

For every relation r1 and r2 relevant to s, if r1 is a sub-property of r2: For every two facts (i1 r1 i2) and (i1 r2 i2) that are both relevant to

s: 1. Remove (i1 r2 i2) from the context of s.

24

Example – cont.

Previous step:

Final answer:

25

Conclusions

Objective: simplify answers to queries against RDF dataApproach: use Situation Theory (Barwise, Perry, Devlin)

Approximate Situation Theory formalization by using STO: Situation Theory Ontology, OWL and Rules

Queries represented by STO:ElementaryInfon and STO:SituationUsed OWL axioms to enhance reasoning about the domainDeveloped domain-agnostic rules for inferring relevant

situations, situation types, relations and individuals in situations

Developed context derivation rules Developed context simplification rules Developed a prototype and showed (on examples) that it worksBaseVISor was used for inferenceTo make it practical, “meta-reasoning” was needed.

26

Future Work

More domain-independent inference rules neededClustering

Inference-driven generalizationMachine Learning

Feedback collected from GUI Concept/Link removal (affects transformation rules) Graphical arrangement (affects clustering)

Scalability Very large scale graph databases

Integration with data analyticsEvaluate with analysts!

Thank You