Proposition Knowledge Graphs. Gabriel Stanovsky Omer Levy Ido Dagan Bar-Ilan University Israel
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1 Proposition Knowledge Graphs Gabriel Stanovsky Omer Levy Ido Dagan Bar-Ilan University Israel 1
2 Problem End User 2
3 Case Study: Curiosity (Mars Rover) Curiosity is a fully equipped lab. Curiosity is a rover. Mars rover Curiosity will look for environments where life could have taken hold. The Mars rover Curiosity is a mobile science lab. Curiosity will look for evidence that Mars might have had conditions for supporting life. Curiosity, the Mars rover, functions as a mobile science laboratory. Curiosity successfully landed on Mars. Mars rover Curiosity successfully landed on the red planet. 3
4 Goal: Representation for Information Discovery Representing a Single Sentence: Captures maximum of the meaning conveyed Consolidation Across Multiple Sentences: Groups semantically-equivalent propositions Traversable Representation: Allows its end user to semantically navigate its structure 4
5 Talk Outline Single Sentence Representation SRL AMR Open-IE Proposition Structure Proposition Knowledge Graphs From Single to Multiple Sentence Representation 5
6 Representing a Single Sentence Existing Frameworks SRL AMR Open-IE 6
7 Semantic Role Labeling (SRL) Maps predicates and arguments in a sentence to a predefined ontology Existing ontologies: PropBank FrameNet NomBank 7
8 Semantic Role Labeling (SRL) Curiosity successfully landed on Mars, after entering its atmosphere. 8
9 Semantic Role Labeling (SRL) thing landing time manner location place entered Curiosity successfully landed on Mars, after entering its atmosphere. entity entering 9
10 Semantic Role Labeling (SRL) Pros Semantically expressive Cons Misses propositions Mars has an atmosphere Relies on an external lexicon such as Propbank 10
11 Representing a Single Sentence Existing Frameworks SRL AMR Open-IE 11
12 Abstract Meaning Representation (AMR) Maps a sentence onto a hierarchical structure of propositions Uses PropBank for predicates, where possible 12
13 Abstract Meaning Representation (AMR) Curiosity successfully landed on Mars, after entering its atmosphere. (l / land-01 :arg1 (c / Curiosity) :location (m / Mars) :manner (s / successful) :time (b / after :op1 (e / enter-01 :arg0 c :arg1 (a / atmosphere :poss m)))) 13
14 Abstract Meaning Representation (AMR) Pros Semantically expressive Unbounded by lexicon Cons Imposes a rooted structure Mars has an atmosphere Requires deep semantic analysis 14
15 Representing a Single Sentence Existing Frameworks SRL AMR Open-IE 15
16 Open Information Extraction (Open IE) Extracts propositions from text based on surface/syntactic patterns Represents propositions as predicate-argument tuples Each element is a natural language string 16
17 Open Information Extraction (Open IE) Curiosity successfully landed on Mars, after entering its atmosphere. (( Curiosity, successfully landed on, Mars ); ClausalModifier: after entering its atmosphere ) 17
18 Open Information Extraction (Open IE) Pros Unbounded by lexicon Uses syntactic patterns Extracts discrete propositions Information retrieval scenario Cons Misses propositions Mars has an atmosphere Does not analyse semantics 18
19 Proposition Knowledge Graphs Representing a Single Sentence Consolidation Across Multiple Sentences Traversing the Representation 19
20 Representing a Single Sentence Curiosity will look for evidence that Mars might have had conditions for supporting life. 20
21 Representing a Single Sentence Curiosity will look for evidence that Mars might have had conditions for supporting life. Predicate: Tense: Subject: Object: look for future Curiosity evidence Predicate: have Tense: future Modality: might Subject: Mars Object: conditions Predicate: supporting Object: life Nodes are propositions 21
22 Representing a Single Sentence Curiosity will look for evidence that Mars might have had conditions for supporting life. Predicate: Tense: Subject: Object: look for future Curiosity evidence Predicate: have Tense: future Modality: might Subject: Mars Object: conditions Predicate: supporting Object: life Edges are syntactic relations 22
23 Representing a Single Sentence Curiosity will look for evidence that Mars might have had conditions for supporting life. Predicate: Tense: Subject: Object: look for future Curiosity evidence Object Predicate: have Tense: future Modality: might Subject: Mars Object: conditions Predicate: supporting Object: life Edges are syntactic relations 23
24 Representing a Single Sentence Propositions can be implied from syntax Curiosity s robotic arm is used to collect samples Curiosity has a robotic arm Curiosity, the Mars rover, landed on Mars Curiosity is the Mars rover Implied propositions can also be introduced by adjectives, nominalizations, conjunctions, and more 24
25 Proposition Knowledge Graphs (PKG) Pros Marks implied propositions Curiosity has a robotic arm Cons Does not analyse semantics Marks discrete propositions along with inner structure Unbounded by lexicon 25
26 Proposition Knowledge Graphs (PKG) We have seen: PKG adopts Open-IE robustness PKG improves over its expressiveness Semantic relations are left for higher level representation Which we will see next 26
27 Proposition Knowledge Graphs Representing a Single Sentence Consolidation Across Multiple Sentences Traversing the Representation 27
28 Consolidation Proposition structures serve as backbone for higher level representation Predicate: Tense: Subject: Object: look for future Curiosity evidence Predicate: have Tense: future Modality: might Subject: Mars Object: conditions Predicate: supporting Object: life Curiosity will look for evidence that Mars might have had conditions for supporting life. 28
29 Consolidation Semantic edges are drawn between sentences Entailment Temporal Conditional Causality 29
30 Paraphrases NASA utilizes the Mars rover, Curiosity, to examine rock samples from Mars paraphrase NASA uses Curiosity rover, to take a closer look at rock samples found on Mars 30
31 Entailment Curiosity is a rover. entailment NASA utilizes the Mars rover, Curiosity, to examine rock samples from Mars NASA uses Curiosity rover, to take a closer look at rock samples found on Mars 31
32 Temporal Curiosity is a rover. entailment NASA utilizes the Mars rover, Curiosity, to examine rock samples from Mars NASA uses Curiosity rover, to take a closer look at rock samples found on Mars temporal Curiosity successfully landed on Mars. 32
33 Proposition Knowledge Graphs Representing a Single Sentence Consolidation Across Multiple Sentences Traversing the Representation 33
34 Curiosity is a rover. entailment Q: What did Curiosity do after landing? NASA utilizes the Mars rover, Curiosity, to examine rock samples from Mars NASA uses Curiosity rover, to take a closer look at rock samples found on Mars temporal Curiosity successfully landed on Mars. Mars rover Curiosity successfully landed on the red planet. 34
35 Curiosity is a rover. entailment Q: What did Curiosity do after landing? NASA utilizes the Mars rover, Curiosity, to examine rock samples from Mars NASA uses Curiosity rover, to take a closer look at rock samples found on Mars temporal Curiosity successfully landed on Mars. Mars rover Curiosity successfully landed on the red planet. 35
36 Predicate: Subject: Object: Comp: utilize NASA the Mars rover examine Predicate: Subject: Object: examine the Mars rover rock samples rock samples Modifier: from Mars NASA utilizes the Mars rover to examine rock samples from Mars 36
37 Predicate: Subject: Object: Comp: utilize NASA the Mars rover examine Predicate: Subject: Object: examine the Mars rover rock samples rock samples Modifier: from Mars NASA utilizes the Mars rover to examine rock samples from Mars Q: Who utilizes the Mars rover? 37
38 Predicate: Subject: Object: Comp: utilize NASA the Mars rover examine Predicate: Subject: Object: examine the Mars rover rock samples rock samples Modifier: from Mars NASA utilizes the Mars rover to examine rock samples from Mars Q: Who utilizes the Mars rover? Q: What did the Mars rover examine? 38
39 Challenges (Ongoing Work) Extract rich propositions from text Extract inter-proposition relations implied by text Discover semantic relations between sentences not implied by text Thank you for listening! 39
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