Super-intelligence: Write a PhD thesis in an afternoon

Would you like to have this as your toy? Assistant? X-Friday?

I certainly would love to have a machine that I can set on a quest to solve a serious / intriguing problem!

check this out. From:

Philip Larrey (2017), Would Super-Human Machine Intelligence Really Be Super-Human? in G. Dodig-Crnkovic and R. Giovagnoli (eds.), Representation and Reality in Humans, Other Living Organisms and Intelligent Machines, Studies in Applied Philosophy, Epistemology and Rational Ethics


 “The simplest example of speed superintelligence would be a whole brain emulation running on fast hardware. An emulation operating at a speed of ten thousand times that of a biological brain would be able to read a book in a few seconds and write a PhD thesis in an afternoon. With the speedup factor of a million, an emulation could accomplish an entire millennium of intellectual work in one working day”  

Watson for Oncology (WFO) – more details

Back to Watson for Oncology (WFO). … so today was deep dive day to look at what papers were written specifically re WFO.

So,  on Sunday, June 23, 2019, using Google Scholar … the list below is of  the main useful things I could find.


  • Shows promise
  • Not ready for solo flight (i.e. needs clinicians to work with it).
  • Benefits from adding diagnostic tests liken GEA (Gene expression assays)
  • Keep working on improving WFO, and understand specifics better.


Literature I looked at, will look at it again in more detail, and provide further insights.

  1. Choi, Y. I., Chung, J. W., Kim, K. O., Kwon, K. A., Kim, Y. J., Park, D. K., … & Sung, K. H. (2019). Concordance Rate between Clinicians and Watson for Oncology among Patients with Advanced Gastric Cancer: Early, Real-World Experience in Korea. Canadian Journal of Gastroenterology and Hepatology, 2019.
  2. Kim, Y. Y., Oh, S. J., Chun, Y. S., Lee, W. K., & Park, H. K. (2018). Gene expression assay and Watson for Oncology for optimization of treatment in ER-positive, HER2-negative breast cancer. PloS one, 13(7), e0200100.
  3. Schmidt, C. (2017). MD Anderson breaks with IBM Watson, raising questions about artificial intelligence in oncology. JNCI: Journal of the National Cancer Institute, 109(5).
  4. Zhang, X. C., Zhou, N., Zhang, C. T., Lv, H. Y., Li, T. J., Zhu, J. J., … & Liu, G. (2017). 544P Concordance study between IBM Watson for Oncology (WFO) and clinical practice for breast and lung cancer patients in China. Annals of Oncology, 28(suppl_10), mdx678-001.
  5. Zou, F., Liu, C. Y., Liu, X. H., Tang, Y. F., Ma, J. A., & Hu, C. H. (2018). Concordance Study between IBM Watson for Oncology and Real Clinical Practice for Cervical Cancer Patients in China: A Retrospective Analysis. Available at SSRN 3287513.
  6. Somashekhar, S. P., Sepúlveda, M. J., Puglielli, S., Norden, A. D., Shortliffe, E. H., Rohit Kumar, C., … & Ramya, Y. (2018). Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Annals of Oncology, 29(2), 418-423.
  7. Somashekhar, S. P., Sepúlveda, M. J., Norden, A. D., Rauthan, A., Arun, K., Patil, P., … & Kumar, R. C. (2017). Early experience with IBM Watson for Oncology (WFO) cognitive computing system for lung and colorectal cancer treatment.
  8. Somashekhar, S. P., Kumarc, R., Rauthan, A., Arun, K. R., Patil, P., & Ramya, Y. E. (2017). Abstract S6-07: Double blinded validation study to assess performance of IBM artificial intelligence platform, Watson for oncology in comparison with Manipal multidisciplinary tumour board–First study of 638 breast cancer cases.
  9. Liu, C., Liu, X., Wu, F., Xie, M., Feng, Y., & Hu, C. (2018). Using artificial intelligence (Watson for oncology) for treatment recommendations amongst Chinese patients with lung cancer: Feasibility study. Journal of medical Internet research, 20(9), e11087.
  10. Ross, C., & Swetlitz, I. (2017). IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close. STAT News.
  11. Zauderer, M. G., Gucalp, A., Epstein, A. S., Seidman, A. D., Caroline, A., Granovsky, S., … & Petri, J. (2014). Piloting IBM Watson Oncology within Memorial Sloan Kettering’s regional network.
  12. Herath, D. H., Wilson-Ing, D., Ramos, E., & Morstyn, G. (2016). Assessing the natural language processing capabilities of IBM Watson for oncology using real Australian lung cancer cases.
  13. Bach, P., Zauderer, M. G., Gucalp, A., Epstein, A. S., Norton, L., Seidman, A. D., … & Keesing, J. (2013). Beyond Jeopardy!: Harnessing IBM’s Watson to improve oncology decision making.
  14. Kris, M. G., Gucalp, A., Epstein, A. S., Seidman, A. D., Fu, J., Keesing, J., … & Setnes, M. (2015). Assessing the performance of Watson for oncology, a decision support system, using actual contemporary clinical cases.

486 Verbs account for 90% of occurrences


looking at citations’ meaningfulness  in Tahamtan, Iman, and Lutz Bornmann. “What Do Citation Counts Measure? An Updated Review of Studies on Citations in Scientific Documents Published between 2006 and 2018.” arXiv preprint arXiv:1906.04588 (2019).

Bertin and Atanassova (2014) showed that in the introduction section, “70 verbs account for 50% of all verb occurrences, and 486 verbs account for 90% of the occurrences”.

Lots and lots of insights and data here ….


note to self. – follow up and look at related work


Artificial Intelligence for Regenerative Medicine

Next on the research reading queue, pointers to applications of AI in Regenerative Medicine. We’ll be including this in discussions.


Principle texts:

Wikipedia: Regenerative medicine is a branch of translational research ] in tissue engineering and molecular biology which deals with the “process of replacing, engineering or regenerating human cells, tissues or organs to restore or establish normal function”.  This field holds the promise of engineering damaged tissues and organs by stimulating the body’s own repair mechanisms to functionally heal previously irreparable tissues or organs.
Regenerative medicine also includes the possibility of growing tissues and organs in the laboratory and implanting them when the body cannot heal itself. If a regenerated organ’s cells would be derived from the patient’s own tissue or cells, this would potentially solve the problem of the shortage of organs available for donation, and the problem of organ transplant rejection.
Some of the biomedical approaches within the field of regenerative medicine may involve the use of stem cells.[8] Examples include the injection of stem cells or progenitor cells obtained through directed differentiation (cell therapies); the induction of regeneration by biologically active molecules administered alone or as a secretion by infused cells (immunomodulation therapy); and transplantation of in vitro grown organs and tissues (tissue engineering). ]


along these lines, I encountered this interesting title:


Zhavoronkova, Anna A., Polina Mamoshinaa, Quentin Vanhaelena, Morten Scheibye-Knudsene, Alexey Moskalevf and Alex Alipera. “Artificial intelligence for aging and longevity research.” (2018).

Abstract: The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tre- mendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models—extracting the most important features and identifying biological targets and mechanisms. Recent developments in generative adversarial networks (GANs) and re- inforcement learning (RL) permit the generation of diverse synthetic molecular and patient data, identification of novel biological targets, and generation of novel molecular compounds with desired properties and ger- oprotectors. These novel techniques can be combined into a unified, seamless end-to-end biomarker develop- ment, target identification, drug discovery and real world evidence pipeline that may help accelerate and im- prove pharmaceutical research and development practices

AI in medicine – not ready for prime time?

Am exploring what really can be said for AI in medicine.  There are lots of good things going on … but some reality seems to have set in.

I ran into this conclusion in the paper  Deep Learning for Genomics: A Concise Overview by Yue and Wang at Carnegie Mellon. [Yue, Tianwei and Haohan Wang. “Deep Learning for Genomics: A Concise Overview.” CoRR abs/1802.00810 (2018):]

Current applications, however, have not brought about a watershed revolution in genomic research. The predictive performances in most problems have not reach the expec- tation for real-world applications, neither have the interpretations of these abstruse models elucidate insightful knowledge. A plethora of new deep learning methods is constantly being proposed but awaits artful applications in genomics.

I was really hoping we were farther along. Maybe there’s hope … there’s always hope        [Elvis: Farther along we’ll know more about it. Farther along we’ll understand why. Cheer up my brother live in the sunshine].  Right now, what I am seeing with Watson for Genomics, and other ‘production systems ‘ suggest lots of work ahead.

Experiment 2 -The Search for Deep Learning Impact

Now added a recent search that looked for recent USPTO grants that specified Deep Learning and Medicine …

filed under

PATEX 2: Patent Exploration for Artificial Intelligence in Medicine (AIM)

Experiment 2 -The Search for Deep Learning Impact

As an amusing element, it picked up this:

US-10040551-B2. – Drone delivery of coffee based on a cognitive state of an individual.



PATEX 1: Patent Exploration for Artificial Intelligence in Medicine (AIM)

PATEX 1: Patent Exploration for Artificial Intelligence in Medicine (AIM)

Experiment 1 here

What I wanted to do here is see what Google Patent would dig up for a search on recent granted patents that mention medicine and artificial intelligence.


The results leave much room for improvement ….

Did IBM Watson Overpromise on AI Health Care?

Eliza Strickland has an opinion 

IBM Watson’s foundations  certainly had and still have a lot of promise!  Perhaps a more thorough ‘lessons learned’  is on order.

(web article states  article appears in the April 2019 print issue as “IBM Watson, Heal Thyself.”)

IBM-MIT AI collaboration – How’s it doing?

on Sep 7, 2017 Evan Sweeney reported that IBM and MIT launch $240M AI research lab with a focus on healthcare applications. Among other things,  the story stated that MIT faculty  and students will have access to  IBM Watson Health. Question: So what’s happened since then? We’ll check in on that soon. – see

Artificial Intelligence in Medicine (AIM) – IBM’s WATSON and WatsonPaths

Am working on providing pointers and discussion on  AI in Medicine (AIM).

The general place to check first is the main Artificial Intelligence in Medicine resource page.  You’ll find some useful ideas about Hetnets in biomedicine. These are heterogeneous networks with multiple node or relationship types. Useful for data integration, translation, and biomedical knowledge mining. You’ll also find out aboutProject Rephetio (Drug Repurposing) developed to predict new uses for existing compounds.  In addition, you’ll find links to  OBI – the Ontology for Biomedical Investigations.

Referenes and links associated with IBM Watson / WatsonPaths medical applications are located here.

For convenience and robustness, I am including the initial references below. Enjoy.


AI in Medicine (AIM) approaches and applications have assisted in both trivial and profound ways, and they hold great promise. We argue that there are even larger systemic benefits when AI enabled medicine is considered at a national level.

This page aims to present relevant information and links to resources useful in furthering AIM objectives.  Some links point to reports,preprints,  papers, and books, other point to active and inactive databases, still others point software repositories and AIM specific software and platforms.  While some of the links point to completed and/or terminated projects, we believe there’s much to be learned from the linked resources, and we hope these are used to spark curiosity and further ideas and progress in the spirit of “on the shoulders of Giants”.

AIM Related Projects

IBM WATSON / WatsonPaths: IBM’s Watson architecture was and is being employed in Medical applications.
WatsonPaths: Scenario-Based Question Answering and Inference over Unstructured Information is a key paper available here. As an illustration, the paper discusses a Patient with Erythropoietin Deficiency. Via the query “A 32-year-old woman with type 1 diabetes mellitus has had progressive renal failure… Her hemoglobin concentration is 9 g/dL… A blood smear shows normochromic, normocytic cells. What is the problem?

The table below provides links to some of the key patents in IBM’s Watson Intellectual Property portfolio- SCROLL to the right within the table to see immediate links to the patent PDFs.

id title inventor/author priority date grant date result link
US-10216804-B2 Providing answers to questions using hypothesis pruning Jennifer Chu-Carroll, David A. Ferrucci, David C. Gondek, Adam P. Lally, James C. Murdock, IV 9/28/10 2/26/19
US-10133808-B2 Providing answers to questions using logical synthesis of candidate answers Eric W. Brown, Jennifer Chu-Carroll, David A. Ferrucci, Adam P. Lally, James W. Murdock, John M. Prager 9/28/10 11/20/18
US-9805613-B2 System and method for domain adaptation in question answering Sugato Bagchi, David A. Ferrucci, David C. Gondek, Anthony T. Levas, Wlodek W. Zadrozny 5/14/08 10/31/17
US-9798800-B2 Providing question and answers with deferred type evaluation using text with limited structure Pablo A. Duboue, James J. Fan, David A. Ferrucci, James W. Murdock, IV, Christopher A. Welty, Wlodek W. Zadrozny 9/24/10 10/24/17
US-9690861-B2 Deep semantic search of electronic medical records Keerthana Boloor, Eric W. Brown, Murthy V. Devarakonda, David Ferrucci, John M. Prager 7/17/14 6/27/17
US-9529845-B2 Candidate generation in a question answering system Jennifer Chu-Carroll, James J. Fan, David A. Ferrucci 8/13/08 12/27/16
US-9508038-B2 Using ontological information in open domain type coercion David A. Ferrucci, Aditya Kalyanpur, James W. Murdock, IV, Christopher A. Welty, Wlodek W. Zadrozny 9/24/10 11/29/16
US-9454603-B2 Semantically aware, dynamic, multi-modal concordance for unstructured information analysis Branimir K. Boguraev, Youssef Drissi, David A. Ferrucci, Paul T. Keyser, Anthony T. Levas 8/6/10 9/27/16
US-9262938-B2 Combining different type coercion components for deferred type evaluation Sugato Bagchi, James J. Fan, David A. Ferrucci, Aditya A. Kalyanpur, James W. Murdock, IV, Christopher A. Welty 3/15/13 2/16/16
US-9189541-B2 Evidence profiling Eric W. Brown, Jennifer Chu-Carroll, James J. Fan, David A. Ferrucci, David C. Gondek, Anthony T. Levas, James W. Murdock, IV 9/24/10 11/17/15
US-9165252-B2 Utilizing failures in question and answer system responses to enhance the accuracy of question and answer systems Michael A. Barborak, Jennifer Chu-Carroll, David A. Ferrucci, James W. Murdock, IV, Wlodek W. Zadrozny 7/15/11 10/20/15
US-9153142-B2 User interface for an evidence-based, hypothesis-generating decision support system Sugato Bagchi, Michael A. Barborak, Steven D. Daniels, David A. Ferrucci, Anthony T. Levas 5/26/11 10/6/15
US-9146917-B2 Validating that a user is human Michael A. Barborak, David A. Ferrucci, James W. Murdock, IV, Wlodek W. Zadrozny 7/15/11 9/29/15
US-9031832-B2 Context-based disambiguation of acronyms and abbreviations Branimir K. Boguraev, Jennifer Chu-Carroll, David A. Ferrucci, Anthony T. Levas, John M. Prager 9/29/10 5/12/15
US-8972321-B2 Fact checking using and aiding probabilistic question answering David A. Ferrucci, David C. Gondek, Aditya A. Kalyanpur, Adam P. Lally, Siddharth Patwardham 9/29/10 3/3/15
US-8943051-B2 Lexical answer type confidence estimation and application James J. Fan, David A. Ferrucci, David C. Gondek, Aditya A. Kalyanpur, Adam P. Lally, James W. Murdock, Wlodek W. Zadrozny 9/24/10 1/27/15
US-8880388-B2 Predicting lexical answer types in open domain question and answering (QA) systems David A. Ferrucci, Alfio M. Gliozzo, Aditya A. Kalyanpur 8/4/11 11/4/14
US-2014164303-A1 Method of answering questions and scoring answers using structured knowledge mined from a corpus of data Sugato Bagchi, David A. Ferrucci, Anthony T. Levas, Erik T. Mueller 12/11/12
US-8738362-B2 Evidence diffusion among candidate answers during question answering David A. Ferrucci, David C. Gondek, Aditya A. Kalyanpur, Adam P. Lally 9/28/10 5/27/14
US-8738617-B2 Providing answers to questions using multiple models to score candidate answers Eric W. Brown, David A. Ferrucci, James W. Murdock, IV 9/28/10 5/27/14
US-2014108322-A1 Text-based inference chaining David W. Buchanan, David A. Ferrucci, Adam P. Lally 10/12/12
US-2014072948-A1 Generating secondary questions in an introspective question answering system Branimir K. Boguraev, David W. Buchanan, Jennifer Chu-Carroll, David A. Ferrucci, Aditya A. Kalyanpur, James W. Murdock, IV, Siddharth A. Patwardhan 9/11/12
US-8560300-B2 Error correction using fact repositories David A. Ferrucci, David C. Gondek, Wlodek W. Zadrozny 9/9/09 10/15/13
US-8510327-B2 Method and process for semantic or faceted search over unstructured and annotated data Branimir Konstantinov Boguraev, Eric William Brown, Youssef Drissi, David Angelo Ferrucci, Paul Turquand Keyser, Anthony Tom Levas, Dafna Sheinwald 9/24/10 8/13/13
US-8332394-B2 System and method for providing question and answers with deferred type evaluation James Fan, David Ferrucci, David C. Gondek, Wlodek W. Zadrozny 5/23/08 12/11/12
US-8301438-B2 Method for processing natural language questions and apparatus thereof David Angelo Ferrucci, Li Ma, Yue Pan, Zhao Ming Qiu, Chen Wang, Christopher Welty, Lei Zhang 4/23/09 10/30/12
US-8280838-B2 Evidence evaluation system and method based on question answering David A. Ferrucci, Wlodek W. Zadrozny 9/17/09 10/2/12
US-8275803-B2 System and method for providing answers to questions Eric W. Brown, David Ferrucci, Adam Lally, Wlodek W. Zadrozny 5/14/08 9/25/12
CA-2843405-A1 A decision-support application and system for problem solving using a question-answering system Sugato Bagchi, David A. Ferrucci, Anthony T. Levas, Erik T. Mueller 3/8/11
US-8200656-B2 Inference-driven multi-source semantic search Eric W. Brown, Jennifer Chu-Carroll, James J. Fan, David A. Ferrucci, David C. Gondek, Anthony T. Levas, James William Murdock, IV 11/17/09 6/12/12
US-2011125734-A1 Questions and answers generation Pablo A. Duboue, David A. Ferrucci, David C. Gondek, James W. Murdock, IV, Wlodek W. Zadrozny 11/23/09
US-7757163-B2 Method and system for characterizing unknown annotator and its type system with respect to reference annotation types and associated reference taxonomy nodes Yurdaer N. Doganata, Youssef Drissi, David A. Ferrucci, Tong-haing Fin, Genady Grabarnik, Lev Kozakov 1/5/07 7/13/10
US-7333967-B1 Method and system for automatic computation creativity and specifically for story generation Selmer Conrad Bringsjord, David Angelo Ferrucci 12/23/99 2/19/08
US-7178105-B1 Method and system for document component importation and reconciliation David Angelo Ferrucci, Steinar Flatland, Adam Patrick Lally 2/4/00 2/13/07
US-7139752-B2 System, method and computer program product for performing unstructured information management and automatic text analysis, and providing multiple document views derived from different document tokenizations Andrei Z Broder, David Carmel, Arthur C Ciccolo, David Ferrucci, Yoelle Maarek, Yosi Mass, Aya Soffer, Wlodek W Zadrozny 5/30/03 11/21/06
US-7131057-B1 Method and system for loose coupling of document and domain knowledge in interactive document configuration David Angelo Ferrucci, Steinar Flatland, Adam Patrick Lally 2/4/00 10/31/06
US-2004243554-A1 System, method and computer program product for performing unstructured information management and automatic text analysis Andrei Broder, Arthur Ciccolo, David Ferrucci, Alan Marwick, Wlodek Zadrozny 5/30/03
US-2004243556-A1 System, method and computer program product for performing unstructured information management and automatic text analysis, and including a document common analysis system (CAS) David Ferrucci, Thilo Goetz, Thomas Hampp, Alan Marwick, Oliver Suhre, Wlodek Zadrozny 5/30/03
US-2004243560-A1 System, method and computer program product for performing unstructured information management and automatic text analysis, including an annotation inverted file system facilitating indexing and searching Andrei Broder, David Ferrucci, Alan Marwick, Yosi Mass, Wlodek Zadrozny 5/30/03



(unless otherwise specified, these are of general domain applicability)

  1. Rob High  and Tanmay Bakshi,  (2019) Cognitive Computing with IBM Watson: Build smart applications using artificial intelligence as a service 
  2. IBM Redbooks, IBM Watson Content Analytics: Discovering Actionable Insight from Your Content. 3rd Edition
  3. Steven Baker, (2011), Final Jeopardy: Man vs. Machine and the Quest to Know Everything 


  1. IBM’s DeepQA Research Team Publications
  2. Ferrucci et al., “Towards the Open Advancement of Question Answering Systems,” IBM Technical Report RC24789, Computer Science, Apr. 22, 2009.
  3. David Ferrucci, Eric Brown, Jennifer Chu-Carroll, James Fan, David Gondek, Aditya A. Kalyanpur, Adam Lally, J. William Murdock, Eric Nyberg, John Prager, Nico Schlaefer, and Chris Welty, (2010) *Building Watson: An Overview of the DeepQA Project, AI Magazine Fall, 2010.
  4. William Murdock (2015), Decision Making in IBM Watson Question Answering Web presentation: Ontology Summit 2015
  5. M. Devarakonda, Dongyang Zhang, Ching-Huei Tsou, M. Bornea, Problem-oriented patient record summary: An early report on a Watson application, e-Health Networking, Applications and Services (Healthcom), 2014 IEEE 16th International Conference on, pp. 281-286
  6. WatsonPaths: Scenario-based Question Answering and Inference over Unstructured Information,IBM Research Report RC25489, IBM, 2014
  7. Nico Schlaefer, (2011),Statistical Source Expansion for Question Answering, PHD Thesis,CMU-LTI-11-019
  8. Special Issue on Question Answering, AI Magazine Vol 31 No 3: Fall 2010
  9. Bernstein et al., Ginseng: A Guided Input Natural Language Search Engine for Querying Ontologies, 2006, Jena User Conference, pp. 1-3.
  10. Blitzer, Domain Adaptation of Natural Language Processing Systems, Presented to the Faculties of the University of Pennsylvania in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy, 2007.
  11. Bollen et al., Mining associative relations from website logs and their application to context-dependent retrieva
  12. l using spreading activation, 1999, ACM, pp. 1-6.
  13. Broekstra et al., Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema, 2002, ISWC, vol. 2342/2002, pp. 54-68.
  14. Chang et al., “Creating An Online Dictionary of Abbreviations from MEDLINE,” J Am Med Inform Assoc. 2002; 9:612-620. DOI 10.1197/jamia.M1139.
  15. Chu-Carroll et al., “In Question-Ansering, Two Heads are Better than One”, HLT-NAACL’03, May-Jun. 2003, pp. 24-31, Edmonton, Canada.
  16. Cucerzan et al., “Factoid Question Answering over Unstructured and Structured Web Content”, In Proceedings of the 14th Text Retrieval Conference TREC 2005, Dec. 31, 2005.
  17. Finin, Swoogle: a search and metadata engine for the semantic web, 2004, ACM, pp. 652-659.
  18. Fininet al., Information Retrieval and the Semantic Web, 2005, System Sciences, pp. 1-10.
  19. Kaufmann et al., How Useful Are Natural Language Interfaces to the Semantic Web for Casual End-Users?, 2007, Springer, pp. 281-294.
  20. Ran et al., Natrual Language Query System for RDF Repositories, 2007, SNLP, pp. 1-6.
  21. Tablan et al., A Natural Language Query Interface to Structured Information, 2008, Springer pp. 1-15.
  22. Wang et al., PANTO: A Portable Natural Language Interface to Ontologies, 2007, Springer, pp. 473-487.
  23. Bouzegboub et al, Search and Composition of Learning Objects in a Visual Environment, in Learning in the Synergy of Multiple Disciplines Lecture Notes in Computer Science, Springer: Berlin & Heidelberg, vol. 5794 (2009) ISSN 0302-9743 (Print) 1611-3349 (Online), ISBN 978-3-642-04635-3.
  24. Cao, TH. et al.; A robust ontology-based method for translating natural language queries to conceptual graphs, 2008.
  25. Chabane Djeraba, Marinette Bouet, and Henri Briand, “Concept-Based Query in Visual Information Systems,” IEEE International Forum on Research and Technology Advances in Digital Libraries ADL’98 ,pp. 299-308.
  26. D. Braga, A. Campi, and S. Ceri, XQBE (XQuery by Example): A Visual Interface to the Standard XML Query Language, ACM Transactions on Database Systems 30.2 (2005) 398-443.
  27. G. Barzdins, E. Liepins, M. Veilande, & M. Zviedris, “Ontology Enabled Graphical Database Query Tool for End-Users,” in H.-M. Haav & A. Kalja, edd., Databases and Information Systems V (2009) 105-116.
  28. Gustavo O. Arocena, Alberto O. Mendelzon, and George A. Mihailal, “Applications of a Web query language,” Computer Networks and ISDN Systems ,29.8-13 (Sep. 1997) 1305-1316 = Papers from the Sixth International World Wide Web Conference.
  29. Irna M.R. Evangelista Filha, Altigran S. Da Silva, Alberto H.F. Laender, and David W. Embley, “Using Nested Tables for Representing and Querying Semistructured Web Data,” in Anne Banks Pidduck, John Mylopoulos, Carson C. Woo, and M. Tamer Ozsu, edd., Advanced Information Systems Engineering LNCS 2348 (2002) 719-723.
  30. Kudelka, M. et al.; Semantic Analysis of Web Pages Using Web Patterns, 2006 (IEEE).
  31. Li et al, “XGI: A Graphical Interface for XQuery Creation,” in AMIA Annu Symp Proc . (2007) 453-457.
  32. Moller, M. et al, RadSem: semantic annotation and retrieval for medical images,2009.
  33. Petropoulos et al, (Querying and Reporting Semistructured Data, QURSED, 2002.
  34. S. Jeromy Carriere and Rick Kazman, “WebQuery: searching and visualizing the Web through connectivity,” Computer Networks and ISDN Systems 29.8-13 (Sep. 1997) 1257-1267 = Papers from the Sixth International World Wide Web Conference.
  35. Sriram Raghavana and Hector Garcia-Molina, “Complex Queries over Web Repositories,” Proceedings 2003 VLDB Conference (2003) 33-44.
  36. Urbain, J. et al.; Probabilistic passages models for semantic search search of genomics literature, 2008.
  37. Wen-Syan Li and Junho Shim, “Facilitating complex Web queries through visual user interfaces and query relaxation,” in Computer Networks and ISDN Systems ,vol. 30, Issues 1-7, Apr. 1998, pp. 149-159 = Proceedings of the Seventh International World Wide Web Conference.
  38. Wen-Syan Li, Junho Shim and K. Selcuk Candan, “WebDB: A System for Querying Semi-structured Data on the Web,” Journal o/Visual Languages & Computing 13.1 (Feb. 2002) 3-33.
  39. Xian Ding et al, An ontology-based semantic expansion search model using semantic condition transform,2009.
  40. Apache incubator, Apache UIMA,
  41. Berger et al., A Maximum Entropy Approach to Natural Language Processing, Association for Computational Linguistics, 1996.
  42. Etzioni et al.,”Open information extraction from the web” Communications of the ACM , vol. 51 Issue 12, Dec. 2008 pp. 68-74. *
  43. Wikipedia, UIMA,
  44. “INDRI Language modeling meets inference networks,”, last modified May 23, 2011; pp. 1-2.
  45. Question answering,” From Wikipedia, the free encyclopedia,
  46. Adar, “SaRAD: a Simple and Robust Abbreviation Dictionary,” Bioinformatics, Mar. 2004, pp. 527-533, vol. 20 Issue 4.
  47. Aditya et al., “Leveraging Community-built Knowledge for Type Coercion in Question Answering,” Proceedings of ISWC 2011.
  48. Balahur, “Going Beyond Traditional QA Systems: Challenges and Keys in Opinions Question Answering,” Coling 2010: Poster Volume, pp. 27-35, Beijing, Aug. 2010.
  49.  — more references coming 🙂