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Our journey with semantic
embedding
Rob Koopman, Shenghui Wang
OCLC
Fourth Annual KnoweScape Conference, 22-24 Feb 2017
Agenda
● What is semantic embedding
● Applications:
○ Context explorer
○ Topic delineation
○ Information retrieval
○ Conce...
An example by Stefan Evert: what’s the meaning of bardiwac?
•He handed her her glass of bardiwac.
•Beef dishes are made to...
How can we calculate the similarity/relatedness?
● Discrete encoding does not help to automatically process
the underlying...
Let’s embed words in a vector space
● Words are represented in a continuous vector space
where semantically similar words ...
What can we do with the similarity?
● Context explorer
● Topic delineation
● Information retrieval
● Concept drift
Context explorer
What can we do with the similarity?
● Context exploration
http://thoth.pica.nl/astro/relate?input=supernovae&type=7
Document clustering
Topic delineation based on clustering
● Generate vectors for entities
● Generate vectors for articles based on weighted av...
Information Retrieval
1. 2014 glycated nail proteins a new approach for detecting diabetes in
developing countries
2. 2015 glycation of nail pro...
Now let’s evaluate and compare
Word embedding techniques
Two main categories of approaches:
● global co-occurrence count-based methods, such as
Latent Se...
Word embedding techniques
Two main categories of approaches:
● global co-occurrence count-based methods, such as
Latent Se...
Word embedding techniques
● Ariadne (OCLC): based on Random Projection of the
global co-occurrence matrix
● Word2Vec (Goog...
Different models lead to different embeddings
knee
Word2Vec ankle, hip, elbow, knees, shoulder, patellofemoral, joint,
wri...
Word analogy evaluation
Which word is the most similar to Italy in the same sense as
Paris is similar to France?
X=vector(...
Word analogy evaluation
Which word is the most similar to Italy in the same sense as
Paris is similar to France?
X=vector(...
Information retrieval evaluation
Use case: evidence-based medical guideline
Statement There are no indications to suggest ...
From word embedding to document distance
● Doc2Vec: an extension of Word2Vec, that learns to
correlate documents and words...
A tiny gold set
● 29 statements (16 breast cancer, 4 hepatitis C, 4 lung
cancer, 5 ovarian cancer)
● 103 (96 unique) sourc...
Average recall
Average precision
Concept drift
Now let’s talk about concept drift
● 20 million Medline articles published since 1977
● 1.5 million entities (subjects, au...
Jaccard similarity based on important subjects
Most and least stable subjects
Most stable subjects Least stable subjects
history 15th century
history 18th century
histor...
Subjects most related to “trauma nervous system”
1977-
1982
anatomy regional, fracture fixation internal, bulgaria, piedra...
Global drift based on Self Organising Maps
- Create document vectors
- Put the documents in a self organizing map
- For ea...
Summary
● Semantic indexing enables the operations directly on the
underlying semantics
● It helps to explore the context ...
Our journey with semantic embedding
Our journey with semantic embedding
Our journey with semantic embedding
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Our journey with semantic embedding

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Fourth Annual KnoweScape Conference (KnowEscape2017) http://knowescape.org/knowescape2017/

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Our journey with semantic embedding

  1. 1. Our journey with semantic embedding Rob Koopman, Shenghui Wang OCLC Fourth Annual KnoweScape Conference, 22-24 Feb 2017
  2. 2. Agenda ● What is semantic embedding ● Applications: ○ Context explorer ○ Topic delineation ○ Information retrieval ○ Concept drift
  3. 3. An example by Stefan Evert: what’s the meaning of bardiwac? •He handed her her glass of bardiwac. •Beef dishes are made to complement the bardiwacs. •Nigel staggered to his feet, face flushed from too much bardiwac. •Malbec, one of the lesser-known bardiwac grapes, responds well to Australia’s sunshine. •I dined on bread and cheese and this excellent bardiwac. •The drinks were delicious: blood-red bardiwac as well as light, sweet Rhenish. • ⇒ ‘bardiwac’ is a heavy red alcoholic beverage made from grapes
  4. 4. How can we calculate the similarity/relatedness? ● Discrete encoding does not help to automatically process the underlying semantics ● Statistical Semantics [furnas1983, weaver1955] based on the assumption of “a word is characterized by the company it keeps” [firth1957] ● Distributional Hypothesis [harris1954, sahlgren2008]: words that occur in similar contexts tend to have similar meanings.
  5. 5. Let’s embed words in a vector space ● Words are represented in a continuous vector space where semantically similar words are mapped to nearby points ('are embedded nearby each other'). ● A desirable property: cosine similarity
  6. 6. What can we do with the similarity? ● Context explorer ● Topic delineation ● Information retrieval ● Concept drift
  7. 7. Context explorer
  8. 8. What can we do with the similarity? ● Context exploration http://thoth.pica.nl/astro/relate?input=supernovae&type=7
  9. 9. Document clustering
  10. 10. Topic delineation based on clustering ● Generate vectors for entities ● Generate vectors for articles based on weighted average of entity vectors ● Use standard clustering methods to cluster articles ● At the end this approach has proven to be remarkably compatible with methods based on citation networks. Wang, S., & Koopman, R. (2017). Clustering articles based on semantic similarity. In J. Gläser, A. Scharnhorst, & W. Glänzel (Eds.), Same data—Different results? (pp. 234–556). Towards a comparative approach to the identification of thematic structures in science. Special Issue of Scientometrics
  11. 11. Information Retrieval
  12. 12. 1. 2014 glycated nail proteins a new approach for detecting diabetes in developing countries 2. 2015 glycation of nail proteins from basic biochemical findings to a representative marker for diabetic glycation associated target organ damage 3. 2005 glycation products as markers and predictors of the progression of diabetic complications 4. 2015 glycated nail proteins as a new biomarker in management of the south kivu congolese diabetics 5. 2005 advanced glycosylation end products in skin serum saliva and urine and its association with complications of patients with type 2 diabetes mellitus 6. 1993 review of diabetes identification of markers for early detection glycemic control and monitoring clinical complications 7. 2012 glycation and biomarkers of vascular complications of diabetes 8. 2005 the nail under fungal siege in patients with type ii diabetes mellitus 9. 2003 improvement in quality of diabetes control and concentrations of age products in patients with type 1 and insulin treated type 2 diabetes mellitus studied over a period of 10 years jevin 10. 2005 a novel advanced glycation index and its association with diabetes and microangiopathy
  13. 13. Now let’s evaluate and compare
  14. 14. Word embedding techniques Two main categories of approaches: ● global co-occurrence count-based methods, such as Latent Semantic Analysis and Random Projection ● local context predictive methods, such as neural probabilistic language models
  15. 15. Word embedding techniques Two main categories of approaches: ● global co-occurrence count-based methods, such as Latent Semantic Analysis and Random Projection --- suffer in word analogy tasks ● local context predictive methods, such as neural probabilistic language models --- do not leverage the global statistics
  16. 16. Word embedding techniques ● Ariadne (OCLC): based on Random Projection of the global co-occurrence matrix ● Word2Vec (Google): shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words ● GloVe (Stanford): a global log-bilinear regression model to learn word vectors based on the ratio of the co-occurrence probabilities of two words
  17. 17. Different models lead to different embeddings knee Word2Vec ankle, hip, elbow, knees, shoulder, patellofemoral, joint, wrist, tka, patellar GloVe ankle, hip, joint, knees, arthroplasty, osteoarthritis, elbow, flexion, cruciate, joints Ariadne knees, knee joint, contralateral knee, tibiofemoral, knee pain, knee motion, medial compartment, lateral compartment, operated knees, right knee frog Word2Vec toad, bullfrog, amphibian, rana, turtle, salamander, caudiverbera, frogs, leptodactylid, pleurodema GloVe rana, toad, amphibian, bullfrog, frogs, temporaria, laevis, xenopus, anuran, catesbeiana Ariadne frogs, isolated frog, frog muscle, rana pipiens, anurans, hyla, anuran, tree frog, anuran species, hylid
  18. 18. Word analogy evaluation Which word is the most similar to Italy in the same sense as Paris is similar to France? X=vector(``Paris'')-vector(``France'')+vector(``Italy'')
  19. 19. Word analogy evaluation Which word is the most similar to Italy in the same sense as Paris is similar to France? X=vector(``Paris'')-vector(``France'')+vector(``Italy'') Method Accuracy (%) Runtime (seconds) #Thread Word2Vec 61.4 32,432 16 GloVe 53.6 22,680 16 Ariadne 1.6 15,020 1
  20. 20. Information retrieval evaluation Use case: evidence-based medical guideline Statement There are no indications to suggest that a skin-sparing mastectomy followed by immediate reconstruction leads to a higher risk of local or systemic recurrence of breast cancer. Old references (pmid) 9142378, 1985335 New references (pmid) 9142378, 9694613, 18210199
  21. 21. From word embedding to document distance ● Doc2Vec: an extension of Word2Vec, that learns to correlate documents and words, rather than words with other words ● Ariadne: weighted average of word vectors
  22. 22. A tiny gold set ● 29 statements (16 breast cancer, 4 hepatitis C, 4 lung cancer, 5 ovarian cancer) ● 103 (96 unique) source articles, 156 (145 unique) target articles, in total 180 unique articles ● 66 articles are in both source and target lists, so the baseline total recall is 42.3% (the average baseline recall is 45.8%) ● These articles were published between 1984 and 2012.
  23. 23. Average recall
  24. 24. Average precision
  25. 25. Concept drift
  26. 26. Now let’s talk about concept drift ● 20 million Medline articles published since 1977 ● 1.5 million entities (subjects, authors, journals, words) ● 8 five-year periods ● Each subject is embedded in 8 chronological vector spaces ● Is there concept drift and can we detect it?
  27. 27. Jaccard similarity based on important subjects
  28. 28. Most and least stable subjects Most stable subjects Least stable subjects history 15th century history 18th century history 17th century history 16th century history 19th century thymoma history ancient history medieval rabies history diagnostic techniques surgical chromium isotopes shock surgical iodine isotopes diagnostic techniques and procedures blood circulation time trauma nervous system cesium isotopes liver extracts macroglobulins
  29. 29. Subjects most related to “trauma nervous system” 1977- 1982 anatomy regional, fracture fixation internal, bulgaria, piedra, surgery plastic, germany west, wound infection, carbuncle, burns 1982- 1987 legionellosis, povidone, tropocollagen, attention deficit disorder with hyperactivity, legionnaires disease, transfer psychology 1987- 1992 leg injuries, neurosurgical procedures, arm injuries, wound infection, orthopedic equipment, dermatomycoses, multiple trauma, candidiasis cutaneous, fractures closed 1992- 1997 piperacillin, tazobactam, microbiology, diagnostic errors, sorption detoxification, arthroplasty, hsp40 heat shock proteins, emaciation, professional patient relations 1997- 2002 defensive medicine, insurance liability, diagnostic errors, expert testimony, birth injuries, maleic anhydrides, dimethyl sulfate, medical errors, p protein hepatitis b virus 2002- 2007 peripheral nervous system diseases, peripheral nerve injuries, neurologic examination, male, recovery of function, peripheral nerves, elbow, comorbidity, mother child relations 2007- 2012 peripheral nerve injuries, sciatic neuropathy, papilledema, sciatic nerve, peripheral nerves, nerve crush, neuroma, nerve regeneration, acute disease 2012- 2017 mitochondrial dynamics, dental records, park7 protein human, persistent vegetative state, dnm1l protein human, platelet derived growth factor bb, dual specificity phosphatases, lingual nerve injuries, dental care defensive medicine, insurance liability, diagnostic errors, expert testimony, birth injuries, anatomy regional, fracture fixation internal, bulgaria, piedra, surgery plastic
  30. 30. Global drift based on Self Organising Maps - Create document vectors - Put the documents in a self organizing map - For each point in the map count the documents in a year range - Make sub maps for each year range - Now color code lower than expected as blue and higher than expected in red - The result shows global drift A point of attention is that this shows how the content of the medline database drifts over time, not necessarily how science drifts over time.
  31. 31. Summary ● Semantic indexing enables the operations directly on the underlying semantics ● It helps to explore the context of subject, cluster and retrieve related documents, and study drift ● Different methods have their own limitations ● The choice is application sensitive

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