dr._boris_katz by Epoch Foundation

Interfacing with Big Data Repositories Boris Katz MIT Computer Science and Artificial Intelligence Laboratory July 18, 2013

Our Claim As we develop storage capacity, compute platforms and algorithms for scaling to big data, we will need to create new ways to access and interact with massive scale data

MIT

The Big Picture Q: ----A: ----Q: ----A: -----

Data But, what about access?Language Visualization Interaction imagery feature spaces graphs diagrams

terms relationships descriptions

Analysis

Big Data unstructured

semi-structured

structured

We can view Big Data through…

Big Data

… language-colored glasses Query: “What diseases present with fever and a rash?”

Query: What diseases present with fever and a rash? Answer: Scarlet fever is an illness with a characteristic rash that is caused by a strep infection. Chickenpox, Fifth Disease and Systemic Lupus Erythematosus Roseola – this is one of the most common causes of fever and rash in infants and young children. It starts out with three days of moderate to ...

… visualizationcolored glasses

Language can help manipulate visualization Query: “Rule out patients under 25.” MIT

From Big Data to Manageable Data by understanding structure Parse into T-expressions

Apply S-Rules

OPV Model Manageable Data

Big Data

Annotate

Decomposition

â&#x20AC;˘â&#x20AC;Ż Language focuses our attention on what is important in data and helps make data more manageable

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START: Natural language tools ¢

Providing Machines with New Knowledge: NL text

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semantic representation

Explaining Computer Actions or Describing its Knowledge: semantic representation

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NL text

Testing Computer Understanding by Answering Questions: NL queries

semantic representation

NL responses computer actions

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Building blocks in the START system ¢

Syntactic Analysis: parse trees

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Semantic Representation: ternary expressions

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Matching and Transformational Rules

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Language Generation

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Replying

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Object–Property–Value Data Model

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Question Decomposition MIT

Syntactic Analysis: parse trees â&#x20AC;&#x153;Because the average sea level in the Northeast is expected to rise due to climate change, flooding will likely become more frequent and cause property damage in coastal areas to increase.â&#x20AC;?

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Semantic Representation: From Parse Trees to Ternary Expressions “Because the average sea level in the Northeast is expected to rise due to climate change, flooding will likely become more frequent and cause property damage in coastal areas to increase.” [subject relation object] [become because expect] [flooding become frequent] [become has_modifier likely] [somebody expect rise] [level related_to sea] [level is average] [level in Northeast] [frequent has_quantity more] [level rise null]

[rise due_to change] [change related_to climate] [flooding cause increase] [damage increase null] [damage related_to property] [damage in areas] [areas is coastal] …

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Ternary expression representation

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a versatile syntax-driven representation of language

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highlights significant semantic relations

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very efficient for indexing, matching and retrieval

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Three types of Ternary Expressions “A young man’s friend was visiting Taiwan” ¢

Related to the syntactic structure of the sentence [friend visit Taiwan] [friend related_to man] [man has_property young]

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Related to syntactic features that change from sentence to sentence [visit has_tense past] [visit is_progressive yes] [man has_det indefinite]

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Related to lexical features of words that don’t change from sentence to sentence [Taiwan is_proper yes] [man has_number singular]

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Creating semantic representations

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Matching T-Expressions Assertion: “Average sea level in the Northeast is expected to rise higher due to climate change.” Query:

“What sea levels are expected to rise?

T-Expressions" from Query"

Matcher"

T-Expressions" from Assertion"

[somebody expect rise]

[level related_to sea]

[level related_to sea] [level is average] [level in Northeast]

[level rise null]

[level rise null] [rise due_to change] [change related_to climate] MIT

Matching in START T-Exps from Questions

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term matching: l  l  l

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T-Exps from Assertions

lexical match synonym match hyponym match

structure matching: l  l

exact match match via transformational S-rules

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Verb argument alternations and paraphrases “Surprise”: “The patient surprised the doctor with his fast recovery.” “The patient’s fast recovery surprised the doctor.” “Load”: “The crane loaded the ship with containers.” “The crane loaded containers onto the ship.” “Provide”: “Did Iran provide Syria with weapons?” “Did Iran provide weapons to Syria?”

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Verb classes and S-Rules “Surprise”: “The patient surprised the doctor with his fast recovery.” “The patient’s fast recovery surprised the doctor.” “Confuse”: “The patient confused the doctor with his slow recovery.” “The patient’s slow recovery confuse the doctor.” … Emotional Reaction Verbs (semantic class): anger, confuse, disappoint, embarrass, frighten, impress, please, surprise, threaten, … S-Rule: If: Then:

[[subject verb object1] with object2] [object2 verb object1] [object2 related_to subject]

Provided:

verb ∈ emotional reaction class MIT

Language generation As intelligent systems become more mature, they will be expected to... l  l  l  l  l  l  l

Explain their actions Answer complex questions Keep track of conversation history and state Engage in mixed-initiative dialog Offer related information of potential interest to the user Help users correct and refine their questions Indicate incomplete understanding of questions and offer partial responses

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START's generator structural ternary expressions ternary expressions for syntactic features ternary expressions for lexical features user/machine-provided task specification Generator •  linguistic constraints •  syntactic rules •  morphological rules •  lexical knowledge •  anaphoric reference •  heuristic defaults natural language sentence MIT

Generator in action

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Generator in action

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Replying to a question after a match Generate a sentence from semantic representation related-to"

Query “How are the glucose molecules converted into pyruvate molecules?”

pyruvate" quantity"

into"

two" converts"

molecules"

related-to"

is"

related-to"

smaller"

chain" quantifier" glucose"

reactions" molecule"

A chain of reactions converts each molecule of glucose into two smaller molecules of pyruvate."

each"

Execute a procedure to obtain an answer from the data source Script"

Query “Who directed Gone with the Wind?”

Annotation"

• get http://us.imdb.com/ Details?0031381" • match regexp...

+!IMDb"

Gone with the Wind (1939) was directed by George Cukor, Victor Fleming, and Sam Wood. Source: The Internet Movie Database

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START in action

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Google in action

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The Object–Property–Value data model The object–property–value (OPV) model applies to: ¢

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structured data: Record