cicm 2016, doctoral program · •extract identifier semantics by combining math and ... –special...
TRANSCRIPT
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Moritz Schubotz
31/07/2016 www.formulasearchengine.com 1
CICM 2016, Doctoral program
Augmenting Mathematical Formulae for More Effective
Querying & Presentation
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Motivation
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26th of February 2011
28th of March18.03.-26.03.2011
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Example 1: 1
𝑥≤
1
𝑥
<apply>
<leq/>
<apply>
<divide/>
<cn type="integer">1</cn>
<apply>
<mean/>
<qvar>x</qvar></apply></apply>
<apply>
<mean/>
<apply>
<divide>
<cn type="integer">1</cn>
<qvar>x</qvar></apply></apply>…
1. Different forms e.g. 𝑥1
𝑥≥ 1
2. Different notations e.g.
𝑋𝑓 𝑥 𝑥𝑑𝑥 = ⟨𝑥⟩
3. Exact match seldom
4. Ambiguity in syntax e.g. 𝐸Ψ =𝐻Ψ
5. no TeX-function mean
$\frac 1 \mean ?x \le
\mean \frac 1 ?x$
NTCIR-11 Math-2 WMC-D1
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Example 1: 1
𝑥≤
1
𝑥
<apply>
<leq/>
<apply>
<divide/>
<cn type="integer">1</cn>
<apply>
<mean/>
<qvar>x</qvar></apply></apply>
<apply>
<mean/>
<apply>
<divide>
<cn type="integer">1</cn>
<qvar>x</qvar></apply></apply>…
1. Different forms e.g. 𝑥1
𝑥≥ 1
2. Different notations e.g.
𝑋𝑓 𝑥 𝑥𝑑𝑥 = ⟨𝑥⟩
3. Exact match seldom
4. Ambiguity in syntax e.g. 𝐸Ψ =𝐻Ψ
5. no TeX-function mean
$\frac 1 \mean ?x \le
\mean \frac 1 ?x$
NTCIR-11 Math-2 WMC-D1
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Result 1: 𝜑(𝔼[𝑋]) ≤ 𝔼[𝜑(𝑋)]
$?f[type=function] \mean ?x \le
\mean ?f[type=function] ?x $
$\frac 1 \mean ?x \le
\mean \frac 1 ?x$
$?f[type=function] \mean ?x \le
\mean ?f[type=function] ?x $
$\frac 1 \mean ?x \le
\mean \frac 1 ?x$
\le
\mean
?x
\mean
?f[type=function]
?f[type=function]
?x
\le
\mean
?x
\mean
\frac
\frac
?x
1
1
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Result 1: 𝜑(𝔼[𝑋]) ≤ 𝔼[𝜑(𝑋)]
<apply>
<leq/>
<apply>
<divide/>
<cn type="integer">1</cn>
<apply>
<mean/>
<qvar>x</qvar></apply></apply>
<apply>
<mean/>
<apply>
<divide>
<cn type="integer">1</cn>
<qvar>x</qvar></apply></apply>…
<apply>
<leq/>
<apply>
<qvar type="function">f</qvar>
<apply>
<mean/>
<qvar>x</qvar></apply></apply>
<apply>
<mean/>
<apply>
<qvar type="function">f</qvar>
<qvar>x</qvar></apply></apply>…
$?f[type=function] \mean ?x \le
\mean ?f[type=function] ?x $
$\frac 1 \mean ?x \le
\mean \frac 1 ?x$
$\frac 1$ -> $?f[type=function]$
Not trivial
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Solution 1 inexact matches
• Refined query:$\superconceptOf[
orderby = editdistance ]{
\frac 1 \mean ?x \le
\mean \frac 1 ?x
}$
• Computational complexity
• Restriction of the search space
• Check most likely solutions at first
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But there are diverse information needs
1. Definition look-up
2. Explanation look-up
3. Proof look-up
4. Application look-up
5. Computation assistance
6. General formula search
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, and the data looks like that
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Levels of Abstraction
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Presentation
Content
Semantic
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Overview
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Image pro-
cessingImage Structure
detectionPresen-tation
Entity LinkageContent Semantic
Integrated Queries
Math
Presen-tation
Content Semantic
Text
Keywords Relations
Meta-data
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Completed Research
Querying Processing Scalibility
- Making Math Searchable in Wikipedia (CICM 2012)
- Evaluation of Similarity-Measure Factors for Formulae (NTCIR 2015)
- Wikipedia Subtask at NTCIR 11 (SIGIR 2015)
- Exploring the single-brainbarrier (NTCIR 2016)
- Mathematical Language Processing (CICM 2014)
- Digital Repository of Mathematical Formulae (CICM2014 coauthor)
- Growing the DRMF with generic LaTeX sources
- Mathoid: Accessible MathRendering for Wikipedia ( CICM 2014)
- Semantification of Identifiers in Mathematics for Better Math Information Retrieval (SIGIR 2016)
- Applying Stratosphere for Big Data Analytics (coauthor, BTW 2013)
- Querying large Collections of Mathematical Publications (NTCIR 2013 with Marcus Leich)
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Integrated Queries
Math
Presen-tation
Content Semantic
Text
Keywords Relations
Meta-data
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Mathoid: Robust, Scalable, Fast and Accessible Math Rendering for
Wikipedia
𝜑(𝔼[𝑋]) ≤ 𝔼[𝜑(𝑋)].
• convex function (Q319913, NDL ID 00573442)
• subclass of function
• ja:凸関数
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Let be a probability space, X an integrable real-
valued random variable and φ a convex function.
Then:
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Exploring the single-brain barrier
• “one-brain barrier” [1]– Metaphor: relevant knowledge to conduct math research
needs to be co-located in one brain
• Goals of our contribution to NTCIR12: – Create a point of reference w.r.t. to this barrier for a
trained mathematician– Compare the performance of a human to MIR systems and
analyse characteristic strengths and weaknesses– Derive insights to improve MIR systems– Combine the relevant results of the human and the MIR
systems to create a gold standard
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Exploring the single-brain barrier
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Exploring the single-brain barrier
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Exploring the single-brain barrier
• Strengths of MIR systems:
– Definition lookup queries
– Application lookup
• Weaknesses of MIR systems
– Low precision
– No unified query language to specify query type
• Gold standard dataset can help to develop a math-aware search engine for Wikipedia
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Semantification of Identifiers in Mathematics for Better Math
Information Retrieval
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• First step to enable computer to understand
mathematicians notations
• Focus on identifiers
• Extract identifier semantics by combining math and
• Use computers to find relevant mathematics
• Computers must understand semantics in math to
provide needed information
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Math Augmentation Approach
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(1) Extract formulae
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(2) Extract identifiers
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(3) Find identifiers
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(4) Find definiens candidates
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(5) Score all identifer-definienspairs
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(6) Generate feature vectors
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(7) Cluster feature vectors
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(8) Map clusters to subject hierarchy
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Wikipedia Subtask at NTCIR 11
• NTCIR 11 Wikipedia dataset*
• 30k Wikipedia Articles
• 280k Formulae
• 100 queries
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*) Moritz Schubotz, Abdou Youssef, Volker Markl, and Howard S. Cohl. 2015. Challenges of Mathematical Information Retrieval in the NTCIR-11 Math Wikipedia Task. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '15). ACM, New York, NY, USA, 951-954.
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Wikipedia Subtask at NTCIR 11
• CICM 2012(2 Participants)
• NTCIR 2013 (pilot)(6 Participants)
• NTCIR 2014
arXiv (8 Participants) Wikipedia (7 Participants)
31/07/2016 30Part III / IV Completed Research
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Wikipedia Task results
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Higher recall
Hig
her
„p
reci
sio
n“
Part III / IV Completed Research
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Gold standardavailable from http://mlp.formulasearchengine.com
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Gold standard details
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174
4
27
97
8310 Identifiers
Wikidata item Two Wikidata items Wikidata item + NP
Individual NP Multiple NP
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Results: Identifiers
• 294/310 correctly extracted (94.8%)
• 57 false positive (fp)
• Problems
– Incorrect markup (8fn, 33fp) 𝜂 =𝑄1−𝑄2
𝑄1
– Symbols (9fp) d
d𝑥
– Sub-super script (3fp, 2fn) 𝜎𝑦2
– Special notation (10fp, 2fn) 𝒖 × 𝒗 = 𝜖𝑖𝑗𝑘𝑢𝑗𝑣𝑘𝒆𝒊
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Results: (4) Find definienscandidates
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Results: Definitions
88
120
19
83
Definitions
Exact match Partial match not found not in document
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Distribution of identifier counts
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Results: Definitions with Namespace support
103
147
60
Definitions
Exact match Partial match not found
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Discovered namespaces
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• 250 clusters -> 167 mapped to classification schemata
• 5618 definitions with 𝑠 > 1 (2124 Wikidata concepts)
• Evaluate 6 randomly sampled namespaces
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Impression from namespace samples
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129
78
144
Definitions in Namespaces
correct wrong unspecific cannot say
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Discovered namespaces
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• Physics
• Identifiers are significant for formulae
• Mathematics
• Identifiers might be less significant for formulae
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Conclusions
• For 10% of the identifiers used in Wikipedia (en) we could assign the associated Wikidata item
• 90% ahead– More specific Wikidata items needed
– Combine data from different language versions
– Improve recognition rate within a document
• Namespaces for mathematical identifiers could be identified
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Next steps
• The identifier information is available from the Wikipedia API today http://en.wikipedia.org/api
• Develop tools to augment the user experience for math in Wikipedia and beyond– Tooltips (ongoing)– Physical Dimensions (ongoing)– Translation to Computer Algebra Systems (started)– Math Question and Answering (ongoing)– Author assistance (ongoing with WMF)– Related formulae search (started)
• Justification for semantification effort
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Contact
Moritz Schubotz (now at Universität Konstanz)
+49 7531 88 4438
Mobile: +49 1578 047 1397
www.isg.uni-konstanz.de
www.formulasearchengine.com
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