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Continuous Learning Algorithms in Machine Intelligence and their Application to Less Consistent Decision Making Processes Like Name Matching Analysis Abstract General software intelligences are still held to be outside our current capacity to build. While the definition of intelligence which we apply to machine learning and artificial intelligence generally has expanded over time as our practical computational scales increase, little exploration has been conducted around the other aspect of intelligence, which is the capacity to constantly learn and improve through interaction with the environment. If we are to define a software intelligence as an algorithm that is capable of interacting with its environment and adapting to it over time, then this exploration is critical to the development of such a system. This body of research will attempt to make the first step into the area of continual feedback for a machine learning algorithm, evaluating it against an area which has traditionally been difficult for computers to emulate – Name Matching Analysis. If a machine learning algorithm can be used to ‘tune’ a soft-search name matching algorithm based on continual feedback generated from the results of that engine and the feedback provided by human experts, then this technique of constant feedback not only has immediate practical value but could be explored further in more ambitious research projects.Final Research Paper, Information Science Extension Studies 4 (7867) Page 1 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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Table of Contents Section Page 1. Introduction 3 2. Literature Review 5 2.1 A History of Artificial Intelligence 5 2.2 Modern AI Research into Data and Text Classification 6 2.3 A History of Soft Search Techniques for Identity Resolution 7 2.4 Issues in Name Matching and Existing Research in the Field 9 3. Research Problem or Knowledge Gap 12 4. Further Questions 13 5. Conclusion 15 6. References 16Final Research Paper, Information Science Extension Studies 4 (7867) Page 2 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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1. Introduction Dreams of a general artificial intelligence have been with us for some time (Turing,1950; McCarthy & Hay, 1969). Initial experimentation with the game of chess (de Groot, 1965)lead to heavy expectations in the field, and this field also started to inform the approach toresearching how the human mind itself actually worked (Simon & Chase, 1973). The problemsassociated with achieving a general artificial intelligence have generally fallen into twocategories – computational power and training models. Today, modern computing power and the advent of the internet, which can provideaccess to massive amounts of information to any machine learning algorithm, are eroding thefirst restriction. Research into the use of this new power is being actively explored (Gillick et al,2006). Traditional soft search name matching algorithms have also been in existence for sometime. The first documented linguistic approach to name matching came from a doctoral thesis(Hermansen, 1985) which also outlined a classification process using fuzzy logic matchingcounterbalanced with a large array of ‘linguistic’ rules. At approximately the same time, a smallcompany (Search Software Australia) was developing fuzzy logic rules around the orthographicapproach to name matching (Halloway and Dunkerley, 1999). In the last 10 years, there has been an influx of academic writings in the field of namematching, from comparisons of existing techniques (Christen, 2006; Snae, 2007) through toFinal Research Paper, Information Science Extension Studies 4 (7867) Page 3 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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suggestions on how best to approach the problem (Oshika et al, 1988; Bilenko et al, 2003;Freeman et al, 2006). Name Matching as a process however has a more gradiated result set due to the finergranularity of names generally and the complete lack of an enforced standard globalnomenclature (Do & Rahn, 2007). Often a name that is linguistically (means-like) similar to asearch term is not orthographically (looks-like) or phonetically (sounds-like) similar. To thatend, often these three approaches are considered contradictory yet all 3 (and possibly more)are required for a successful name matching algorithm (See Table 1).Original Name Orthographic Error Phonetic Error Linguistic ErrorSean Saen Shaun John Seam Shorn ShaneElizabeth Elizadeth Elisabeth Bethany Ellixabeth Ellizabef Lisa Table 1 – Name variations possible through error or natural variation So, how do people learn to analyse name matches? While they can be given some initialtraining, it is generally held within the industry that the best teacher is experience (Wang et al,1995). This approach of learning ‘on the job’ and over time is something that has never beenattempted within a name matching machine learning context before.Final Research Paper, Information Science Extension Studies 4 (7867) Page 4 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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2. Literature Review This is a wide topic, drawing from several different complementary disciplines. It istherefore prudent to categorise the material in this literature review across four topics. Thefirst will be a discussion on the history of research in Artificial Intelligence and the changingscope and definition of an artificially intelligent system over time. Second, we will address thecurrent direction of AI research as it applies to data analysis and the complexity of moderndatabase holdings. Third will be the history of computerized soft name matching systems andfinally we will engage in a discussion about the linguistic challenges faced by such algorithmsand how these might be addressed by new technologies. 2.1 A History of Artificial Intelligence As early as 1950 there have been discussions about the possibility of computers possessing artificial intelligence in a manner which allowed it to learn and adapt to its environment at least in some limited way (Turing, 1950). The famous Turing Test is a model commonly used as a benchmark when attempting to create systems that are capable of conversing with humans. In 1969, it was clear that the computational power and storage required for an artificial intelligence capable of interacting with the world at large was not available, and likewise training it would have taken prohibitive amounts of time and manual input. Still, the ‘mathematisation’ of various fields of interest has extended past chess with theFinal Research Paper, Information Science Extension Studies 4 (7867) Page 5 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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intent of using computers to simulate and inform on the thought processes, not just of the individual, but of society (Laland, 1993). This more recent work implies that many of the issues around scale and complexity are no longer insurmountable. Nevertheless, we do not have a functioning general machine intelligence. Generally, machine learning has been dedicated to Non- Polynomial problems (Deitrich, 2000) like the travelling salesman problem (Dorigo, 1997). If the scale and capacity problems can be solved, then the sole remaining issue appears to be the simulation of a suitable learning mechanism. So, what is the current focus of research in this field and how effective has it been to date? 2.2 Modern AI Research into Data and Text Classification There has been much written in the field of using machine learning for text classification and categorization (Joachims, 2002). This category of research is better known as Natural Language Processing (NLP). This work has two broad aims – the first is to allow a computer to intelligently categorise data in free text form so that humans can read and absorb that text which is considered to be of a higher priority than the remainder. The second is to facilitate machine learning by allowing a computer to categorise text in a manner that leads to contextual awareness of the content (Sebastiani, 2002). While there have been successes in this field, there are always ‘border cases’ (cases where a body of text could easily belong to more than one category). For the most part however, the classification itself can generally beFinal Research Paper, Information Science Extension Studies 4 (7867) Page 6 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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considered either ‘right’ or ‘wrong’ by the human expert(s) (Sebastiani, 2002) making it easier to create a training model for a system designed to categorise or classify text. Teaching computers to understand text is considered to be essential to general intelligence models (Jurafsky et al, 2000) and intelligent categorization of data by computer would by definition be a primary task of most machine learning approaches (Witten & Frank, 2010). If the ability to classify and contextualize text is so important to a system intelligence, and progress is being made in this field, does that mean that Name Matching benefits from these advances? 2.3 A History of Soft Search Techniques for Identity Resolution There is a wide array of papers that have been published on name matching. Starting with Jack Hermansen’s seminal paper which was also his Doctoral Thesis (Hermansen, 1985), we find the start of a new field of Computational Linguistics, especially as applicable to the matching of names. Mr Hermansen’s approach was to create a large database of name variations to which every name could be compared, so that it could be grouped appropriately. While it also did some basic similarity tests across names to cater for error being introduced, the approach was primarily designed around names having a distinct meaning (Linguistic Approach). By 1999, there were several commercial firms like Search Software Australia (Halloway and Dunkerley, 1999) who were approaching the topic from a completely different approach very loosely based on Soundex. This approach attempts to drawFinal Research Paper, Information Science Extension Studies 4 (7867) Page 7 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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similarity from the order and placement of letters within the name, attempting to discern whether two names being compared against each other have a ‘distance’ from each other which is within acceptable limits so that it can be included as a potential match. In this design, the words in the name have no meaning whatsoever and the names are being compared as a series of letters (Orthographic Approach). These two approaches have their strengths and weaknesses. The linguistic approach would do well at recognizing that Peggy and Maggie may well be the same name (both derived from Margaret), but the orthographic approach would find it far simpler to match Maggie and Magpie, which a linguistic engine may understand to be two completely different words yet could easily just be a typo if you look at their orthographic distance from each other. So, by removing meaning from names you are more adept at picking up user errors and simple mistakes in your data. Unfortunately, without meaning it is difficult to categorise a name (Pfeiffer et al, 1996). Current research (Christen, 2006) also demonstrates that no technique performs significantly better than any other over a reasonable data pool. Identity resolution still needs to be able to deal with linguistic, phonetic AND orthographic errors thanks to the many different types of errors that can be introduced during data capture. While the emulation of human decisions and learning has been attempted in more commonly understood domains like chess (Furkranz, 1996) it can be argued thatFinal Research Paper, Information Science Extension Studies 4 (7867) Page 8 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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success in such an environment is inevitable because of the strict rules and objectives, no matter how complicated they might be. On the other hand, name matching has moved away from machine learning in the literature, focusing specifically on the different approaches and their comparison (Bilenko et al, 2003). One gets the impression from the body of papers on the subject that there is a reluctance to introduce machine learning into this field of research. But why? 2.4 Issues in Name Matching and Existing Research in the Field Is the problem that names in databases are constantly changing? Certainly there is evidence to demonstrate that the rules that we take for granted in the use of names are not only changing but they are being broken. This has now reached a point in general society where the recording of names is being considered more carefully for specific professions like the legal system (Emens, 2007). Most commercial systems devoted to name matching claim a flexible approach to name matching however, meaning that a machine learning algorithm could build on that flexibility to get around the problem. Is it that naming trends and fashions are constantly changing in different ways across multiple cultures? Again, we see evidence that this is the case. A modern example of this would be how naming conventions changed in Indonesia during Dutch colonization and after they gained their independence (Anderson, 1999). Add to thatFinal Research Paper, Information Science Extension Studies 4 (7867) Page 9 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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the experience of the African Americans in modern times, literally striving to create their own sense of culture by creating unique names for the next generation which are devoid of any cultural attachments from either African or Western societies (Lieberson & Mikelson, 1995). In point of fact, the African American approach actually simplifies the process rather than complicating it. As the names being used are new, there is no linguistic elements to consider, leaving the simpler orthographic and phonetic comparisons. Generally speaking, it is the older names which are rich in linguistic heritage and have been used differently in multiple cultures which cause the biggest headaches for a machine learning approach. After all, most lay-people wouldn’t know that John, Sean, Ian, Johan, Juan, Zane, Giovanni and Ivan are effectively the same name from an original Hebrew source – it’s even harder for a system to learn it from experience. So is the problem that machine learning can’t cope with drift? Actually, it can. There are already studies (Klinkenberg, 2004) that explore a similar problem to this in that one would expect users to become more adept at selecting their name matches over time, therefore would consider a completely different set of names to be acceptable ten years from when they first started. This requires constant learning feedback meaning that the traditional use of training and testing sets are less subject to dataset shift (Quionero-Candela et al, 2009), but other issues are introduced. For instance, how does machine learning cope with changing answers or even conflicting answers from different trainers?Final Research Paper, Information Science Extension Studies 4 (7867) Page 10 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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One possible solution is demonstrated in a case study (Doan et al, 2001) of a system that is trained by people with different skills and experience in a way that allows the system to build a meta-learner, or an algorithm designed to learn how to learn rather than learn a specific approach. This is a similar approach to the one that I plan to use, however the group lead by Doan have specialized in semantic connections which is a more consistent field of inquiry.Final Research Paper, Information Science Extension Studies 4 (7867) Page 11 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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3. Research Problem or Knowledge Gap There are several gaps in the current body of knowledge which are directly relevant tomy aims. We already have name matching engines which perform soft searches to resolveidentities in data pools (Miller et al, 2008). We already have a body of knowledge in linguisticsaround names (the specific study of which is known as Onomastics) and there is a significantbody of research around machine learning and artificial intelligence generally (Bishop, 2006).There is an increasing body of research into text categorization using machine learning(Sebastiani, 2002), but I could find no papers that discussed NAME categorization. Papers that covered linguistic association of names such as Hermansen (1985) andFreeman et al (2006) did so in the context of cross cultural mappings. All the other papers Iread that discussed name matching techniques in detail tended to focus on items like ‘editdistance’ (Cohen et al, 2003) and other similar orthographic techniques. These techniquesconsider the name to be no more than a sequence of letters, and therefore the name carries nomeaning. So, can a system intelligence continue to improve over time at problems for which theanswers appear at best inconsistent and at worst contradictory if one provides a constantstream of feedback to use as learning data? This question has not been explored conclusivelyand appears to be a gap in the literature around the topics of name matching and artificialintelligence or machine learning.Final Research Paper, Information Science Extension Studies 4 (7867) Page 12 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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4. Further Questions Can a computer emulate the learning style employed by humans in this field? If so, doesthis mean the name matching process algorithmic in nature, despite the apparentinconsistencies and contradictions that seem to occur within the process? If so, it wouldindicate that the problem is not so much the ability of computers to emulate the apparentlyinconsistent decisions of humans, but the inability of humans to articulate sufficiently complexalgorithms in code. The implication of such an outcome is that continual feedback training cannot‘overtrain’ a process which is sufficiently complex that humans get better at it the more theylearn about it themselves. Of course, that in turn suggests that an AI that is being constantlytrained by being asked questions and feedback being provided on the answers is more likely tobehave like a human intelligence because it’s closer to the way humans learn. We don’t tend tolearn a task by focusing on the training alone, but rather we are constantly learning that task(even after we are taught it) through practice (McGeoch & Irion, 1952). Of course, this also then informs the debate between the two famous cosmologists andmathematicians regarding the universe and whether or not it is algorithmic by nature (Penrose,1989; Hawking, 1988). If a truly general machine intelligence could be built using this technique pioneered withname matching algorithms, that would support Hawking’s view that the universe is algorithmicin nature and therefore awareness is a by-product of a sufficiently complex algorithm. On theFinal Research Paper, Information Science Extension Studies 4 (7867) Page 13 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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other hand, if all attempts to use constant feedback training on a general machine learningalgorithm failed, then it could indicate that Penrose is correct, and awareness is due to the non-algorithmic nature of insight, which he believes is a property possessed by all humans thatcannot be replicated within a computer program.Final Research Paper, Information Science Extension Studies 4 (7867) Page 14 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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5. Conclusion Because we process unstructured information gathered from our environmentinstinctively it is very easy to forget just how much of it is processed by our minds every second.We not only process the immediate information provided by our senses, but we also have thecapacity to process what we’ve stored in our memories. The next step in the creation of ageneral machine intelligence is to see if a modern computing system is capable of a similar featand some research in this area is already being conducted. Research into the scale problem is already underway (Rosenbloom, 1996) however myresearch will address the aspect of continual learning instead. Part of that is addressing theperceived inconsistencies (irrational decisions) that we see in some aspects of what we do. Arethey a matter of emotions or insight disrupting an otherwise perfect (if complex) algorithm, orare they a matter of an algorithm that is much more complex than we originally thought? Arehumans high complexity, high entropy beings where our choices are in fact easy to interpret asbeing part of many different possible ordered states? Is this in turn why we find it so difficult tounderstand the motivations of others when they choose to help or hinder our own efforts? If we can build a self-tuning name matching algorithm that continues to improve overtime through the ‘experience’ provided by matching experts (rather than reaching a plateau ordegrading after an initial improvement which would represent the overtraining curve effect),then perhaps some of these questions will be within range for future research topics. These questions inform the direction and intent of my proposed research.Final Research Paper, Information Science Extension Studies 4 (7867) Page 15 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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6. ReferencesAnderson B R OG, (1999) Indonesian Nationalism Today and in the Future, Indonesia No. 67, pps. 1-11Bilenko, M.; Mooney, R.; Cohen, W.; Ravikumar, P.; Fienberg, S. (2003) Adaptive name matching in informationintegration. Intelligent Systems, IEEE Vol 18 Issue 5 pps. 16 - 23Bishop, Christopher M, (2006) Pattern Recognition and Machine Learning, New York, NY, SpringerChristen, Peter; (2006) A Comparison of Personal Name Matching: Techniques and Practical Issues, Sixth IEEEInternational Conference on Data Mining Workshops, pps. 290 – 294Cohen, W., Ravikumar, P., & Fienberg, S. (2003). A comparison of string metrics for matching names and records.KDD Workshop on Data Cleaning and Object Consolidation Vol. 3, pps. 73-78Dietterich, Thomas (2000). Ensemble Methods in Machine Learning, Lecture Notes in Computer Science Vol 1857pp 1-15, Springer Berlin / Heidelberg.Do HH, Rahm E (2007), Matching Large Schemas: Approaches and Evaluation, Information Systems, Volume 32,Issue 6, pps. 857-885Doan, AnHai; Domingos, Pedro and Halevy, Alon Y. (2001). Reconciling schemas of disparate data sources: amachine-learning approach. In Proceedings of the 2001 ACM SIGMOD international conference on Managementof data (SIGMOD 01), Timos Sellis (Ed.). ACM, New York, NY, USA, pps. 509-520.Dorigo, M, Gambardella, L.M. (1997) Ant colony system: a cooperative learning approach to the traveling salesmanproblem, Evolutionary Computation, IEEE Transactions Vol 1 Issue 1 pps. 53-66Emens, EF (2007), Changing Name Changing: Framing Rules and the Future of Marital Names, The University ofChicago Law Review Vol. 74, No. 3, pps. 761-863Freeman, Andrew T; Condon, Sherri L. and Ackerman, Christopher M. (2006). Cross linguistic name matching inEnglish and Arabic: a "one to many mapping" extension of the Levenshtein edit distance algorithm. In Proceedingsof the main conference on Human Language Technology Conference of the North American Chapter of theAssociation of Computational Linguistics (HLT-NAACL 06). Association for Computational Linguistics, Stroudsburg,PA, USA, pps. 471-478.Fürnkranz J (1996), Machine Learning In Computer Chess: The Next Generation, International Computer ChessAssociation JournalGillick D, Faria A, Denero J (2006); MapReduce: Distributed Computing for Machine Learningde Groot, AD (1965). Thought and choice in chess, Moulton Publishers, The Hague, The NetherlandsHalloway, G; Dunkerley, M (1999) The Math, Myth & Magic of Name Search and Matching, Search SoftwareAmericaHawking, Stephen (1988) A Brief History of Time, Bantam Dell Publishing GroupHermansen, J.C. (1985) Automatic Name Searching in Large Databases of International Names (Ph.D. Thesis,Georgetown University)Final Research Paper, Information Science Extension Studies 4 (7867) Page 16 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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Joachims, Thorsten (2002) Learning to Classify Text Using Support Vector Machines: Methods, Theory, andAlgorithms. Kluwer Academic PublishersJurafsky, Daniel and Martin, James H. (2000) Speech and Language Processing: An Introduction to NaturalLanguage Processing, Computational Linguistics, and Speech Recognition (University of Colorado, Boulder) UpperSaddle River, NJ: Prentice Hall (Prentice Hall series in artificial intelligence, edited by Stuart Russell and PeterNorvig) , xxvi+934 ppsKlinkenberg, Ralf. (2004). Learning drifting concepts: Example selection vs. example weighting. Intell. Data Anal. Vol8, No 3, pps. 281-300.Laland, K. N. (1993), The mathematical modelling of human culture and its implications for psychology and thehuman sciences. British Journal of Psychology, pps. 84: 145–169.Lieberson S, Mikelson KS (1995), Distinctive African American Names: An Experimental, Historical, and LinguisticAnalysis of Innovation, American Sociological Review Vol. 60, No. 6, pps. 928-946Mccarthy, John & Hayes, Patrick J. (1969) Some Philosophical Problems from the Standpoint of ArtificialIntelligence, Edinburgh University PressMcGeoch, John A.; Irion, Arthur L. (1952) The psychology of human learning. (2nd ed.). Oxford, England: Longmans,Green & Co. xxii, 596 pps.Miller, Keith J; Arehart, Mark; Ball, Catherine; Polk, John; Rubenstein, Alan; Samuel, Ken; Schroeder, Elizabeth;Vecchi Eva; & Wolf, Chris (2008); An Infrastructure, Tools and Methodology for Evaluation of Multicultural NameMatching Systems, Proceedings of the 6th international conference on Language Resources and Evaluation, pps.3179 – 3184Oshika, R; Machi, F; Evans, B; and Tom, J. (1988) Computational Techniques for Improved Name Search,Proceedings of Second Conference on Applied Natural Language Processing pps. 203-210Penrose, Roger (1989) - The emperors new mind: Concerning computers, minds, and the laws of physics. New York,NY, US: Oxford University Press. 466 pps.Pfeiffer, U; Poersch, T & Fuhr, R (1996) - Retrieval Effectiveness of Proper Name Search Methods, InformationProcessing & Management, Issue 32: pps.667-679Quionero-Candela, Joaquin; Sugiyama, Masashi; Schwaighofer, Anton and Lawrence, Neil D. (2009). Dataset Shiftin Machine Learning. The MIT Press.Rosenbloom, Paul S; Laird, John E; Newell, Allen; McCarl, Robert (1991) - A preliminary analysis of the Soararchitecture as a basis for general intelligence, Artificial Intelligence, Volume 47, Issues 1-3, pps. 289-325Sebastiani, Fabrizio. (2002). Machine learning in automated text categorization. ACM Comput. Surv. 34, 1,DOI=10.1145/505282.505283 http://doi.acm.org/10.1145/505282.505283 pps. 1-47.Simon, Herbert A & Chase, William G (1973) - Skill in Chess: Experiments with chess playing tasks and computersimulation of skilled performance throw light on some human perceptual and memory processes - AmericanScientist, Vol 61 No 4Snae, C (2007); Comparison and Analysis of Name Matching Algorithms, Proceedings of World Academy of Science,Engineering and Technology, Volume 21Turing, AM (1950). Computing Machinery and Intelligence, Mind (Oxford University Press), Vol. 59, No. 236Final Research Paper, Information Science Extension Studies 4 (7867) Page 17 of 18Tim Barlow (u3055036) Submitted 27 November 2011

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Wang L; Siegel H J; Roychowdhury V P (1997). Task matching and scheduling in heterogeneous computingenvironments using a genetic-algorithm-based approach, Journal of Parallel and Distributed Computing, Vol 47 No01Wang, R.Y.; Storey, V.C. and Firth, C.P. (1995) A framework for analysis of data quality research, IEEE Transactionson Knowledge and Data Engineering, Vol 7 Issue 4, pps. 623 - 640Final Research Paper, Information Science Extension Studies 4 (7867) Page 18 of 18Tim Barlow (u3055036) Submitted 27 November 2011