This is in effect a mid-2013 progress update. As with many of my blog posts, this is as much a status update for me to get a better handle on where I'm at as it is to broadcast my progress.
mendicott.com is a blog reflecting on my journey with the overall project. This blog started seven years ago, in 2006, with my inquiry into The difference between a web page and a blog.... I had then returned from something like five years of world travel to find the digerati fawning over the blogosphere. At first, I failed to see the difference between a blog and a content management system (CMS) for stock standard web pages. Upon closer examination, I began to realize that the real difference lay in the XML syndication of blog feeds into the real-time web.
meta-guide.com is an attempt to blueprint, or tutorialize, the process. My original Meta Guide 1.0 development in ASP attempted to create automated, or robotic, web pages based on XML feeds from the real-time web. Meta Guide 2.0 development was based on similar feed bots, or Twitter bots, in an attempt to automate, or at least semi-automate, the rapid development of large knowledgebases from social media via knowledge silos. Basically, I use knowledge templates to automatically create the knowledge silos, or large knowledgebases. The knowledge templates are based on my own, proprietary "taxonomies", or more precisely faceted classifications, painstakingly developed over many years.
gaiapassage.com aims to be an automated, or semi-automated, summarization of the knowledge aggregated from social media by feed bots via the proprietary faceted classifications, or knowledge templates. Right now, I'm doing a semi-automated summarization process with Gaia Passage, which consists of automated research in the form of knowledge silos being "massaged" in different ways, but ultimately manually writing the summarization in natural language. This is allowing me to analyze and attempt to dissect the processes involved in order to gradually prototype automation. Summarization technologies, and in particular summarization APIs, are still in their infancy. Examples of currently available summarization technologies include automatedinsights.com and narrativescience.com. The overall field is often referred to as automatic summarization.
In the future, the Gaia Passage human readable summarizations will need to be converted into machine readable dialog system knowledgebase format. The dialog system is basically a chatbot, or conversational user interface (CUI) into a specialized database, called a knowledgebase. Most, common chatbot knowledgebases are based on, or compatible with, XML, such as AIML for example. Voice technologies, both output and input, are generally an additional layer on top of the text based dialog system.
The two main bottlenecks I've come up against are what I like to call artificial intelligence middleware, or frameworks, the "glue" to integrate the various processes, as well as adequate dialog system tools, in particular chatbot knowledgebase tools with both "frontend" and "backend" APIs (application programming interface), in other words a dialog system API on the frontend with a backend API into the knowledgebase for dynamic modification. My favorite cloud based "middleware" is Yahoo! Pipes, which is generally referred to as a mashup platform (aka mashup enabler) for feed based data; however, there are severe performance issues with Yahoo! Pipes -- so, I don't really consider it to be a production ready tool. Like Yahoo! Pipes, my ideal visual, cloud based AI middleware could or should be language agnostic -- eliminating the need to decide on a single programming language for a project. I have also looked into scientific computing packages, such as LabVIEW, Mathematica, and MATLAB, for use as potential AI middleware. Additionally, there are a variety of both natural language and intelligent agent frameworks available. Business oriented cloud based integration, including visual cloud based middleware, is often referred to as iPaaS (integration Platform as a Service), integration PaaS or "Integration as a Service".
The recent closure of the previously open Twitter API with OAuth has set my feed bot, or "smart feed", development back by years. Right now, I'm stuck trying to figure out the best way to use the new Twitter OAuth with Yahoo! Pipes, for instance via YQL, if at all. And if that were not enough, the affordable and user-friendly dialog system API, verbotsonline.com, that I was using went out of business. There are a number of dialog system API alternatives, even cloud based dialog systems, but they are neither free nor cheap, especially for significant throughput volumes. Still to do: 1) complete the Gaia Passage summarizations, 2) make Twitter OAuth work, use a commercial third party data source (such as datasift.com, gnip.com or topsy.com), or abandon Twitter as a primary source (for instance concentrate on other social media APIs instead, such as Facebook), 3) continue the search for a new and better dialog system API provider.
Most basically, the Gaia Passage project is a network of robots that will not only monitor social media buzz about both the environment and tourism but also interpret the inter-relations, cause and effects, between environment and tourism -- such as how climate change effects the tourism industry both negatively or positively, or even what effects the weather has on crime trends for a particular destination -- as well as querying these interpreted inter-relations, or "conclusions", via natural language. If this can be accomplished with any degree of satisfaction, either fully automated or semi-automated, then the system could just as easily be applied to any other vertical. Proposals from potential sponsors, investors, or technology partners are welcomed, and may be sent to mendicot [at] yahoo.com.
Showing posts with label AIML. Show all posts
Showing posts with label AIML. Show all posts
04 September 2013
24 March 2010
MEndicott Meets STELARC & The Thinking Head Project
The adventure continues... at the end of 2008 I moved from Byron Bay in northern New South Wales, where I had been living for some years, to Sydney. Too young to retire, there is only so much beauty and tranquility one can take before going to where the action is; it was Sydney or the bush. I moved to the city hoping to get back into IT work and make some money to continue my research and development; instead, I ended up working in a warehouse for six months unloading trucks in order to afford a flat in the picturesque suburb of Manly on the north shore of Sydney harbor - a short ferry ride from the scenic Sydney Opera House.
I worked eight hard hours a day in the warehouse, then came home to work another eight hours every day on the Internet, mostly exploring the Twitterverse. Just as I arrived in Sydney, a clever geek girl, friend of a friend, insisted the best way for me to break into the Sydney digital world was to meet the Sydney Twitterati at a "Tweetup". I had no idea what that might be but went along anyway. I asked the geek girl what Twitter was, and she said "I don't know exactly what it is, but I've learned a lot from it.” So my journey into the Twitterverse began.
About this time I read a news story, "NASA spawns smart twin in Second Life", and made contact with John Zakos, CIO at Australia-based start-up MyCyberTwin, inquiring about opportunities - to no avail. Later I discovered that Liesl Capper, CEO of MyCyberTwin, lived in Sydney, but was unsuccessful in arranging a meeting despite a number of tries.
Early in 2009, I resolved to shake off the high maintenance big city life and head back to the bush. First Tasmania and then Melbourne, Victoria, the city of my birth. As a result of my Twitter bot @LonelyPlanetBot, built in Sydney burning the midnight oil, I came into contact with the "Innovation Ecosystems Manager" of new Lonely Planet Labs, who came down from BBC Research & Development subsequent to the acquisition of Lonely Planet by BBC Worldwide.
I had arranged to meet with Lonely Planet in March to discuss a proposal, but in February, while I was holidaying in Tasmania, the new owner, BBC, laid-off 10% of their workforce, replacing both the Lonely Planet CEO and Digital Director. Suddenly my contact was out of the country and unable to meet; it wasn't to be…
Since I was in Melbourne anyway, I started looking around for other avenues, other ways, to get my foot in the IT door. I had found the AGENTS-VIC Google Group; so, I contacted one of the moderators and was invited to attend a meeting of the Royal Melbourne Institute of Technology Intelligent Systems Agents Group at RMIT in Melbourne.
There I had a pleasant meeting with Lawrence Cavedon and his associate, Carole Adam, originally from France and who had helped organize the “WACA´ 2006 Second Workshop sur les Agents Conversationnels Animés” in Paris. They told me something about their work in pursuit of emotional expression in toys.
Almost simultaneously I read about the Melbourne performance artist STELARC and his work with robotics installations; so, I contacted him and he arranged a meeting for the next week after his return from Paris. We met for coffee at Druids Cafe on Swanston Street, more or less across the street from RMIT.
STELARC, despite a reputation for being provocative, was a gentleman and as modest and down to earth as could be. Only at the end of our conversation did he roll up his sleeve and show me the ear grafted to his arm, to my relief telling me it wasn't working right at the moment.
STELARC told me about his project, using AIML with the Prosthetic Head; but, more importantly, he told me of how it had lead to his being hired as something like the artistic director for a five-year nearly $5 million project called The Thinking Head funded by the National Health and Medical Research Council and the Australian Research Council. Only then did I find out that Lawrence Cavedon was Chief Architect of The Thinking Head project.
STELARC put me in contact with The Thinking Head Project Leader, Denis Burnham, who was based at the University of Western Sydney MARCS Auditory Laboratories. I subsequently followed-up with STELARC; this time he gave me a tour of his "Virtual STELARC Initiative" space in the RMIT Creative Media region of SecondLife.
After I left Melbourne, a friend put me in contact with David Powers of Flinders University Informatics and Engineering School in Adelaide and a Chief Investigator of the Thinking Head project. His group is apparently working with the Melbourne Museum developing The Thinking Head into a museum guide.
As a result of my contact with The Thinking Head project team, I discovered the Australasian Language Technology Association (ALTA), before leaving Australia….
17 August 2008
Project VagaBot Update August 2008
Following up on my previous post of January 2008, “Corpus linguistics & Concgramming in Verbots and Pandorabots”, you can now see the demo of this VagaBot at http://www.mendicott.com . The results of this trial were not satisfying due to the limitation of the VKB engine at verbotsonline.com not being able to process consecutive, or random, responses from identical input or triggers, basically tags. In other words, the responses with identical input hang on the first response, and not cycle through the series of alternatives. Apparently a commercial implementation of the Verbots platform does allow for the consecutive firing of related replies. Thanks to Matt Palmerlee of Conversive, Inc. for increasing the online knowledgebase storage to accommodate this trial and demo.
Dr. Rich Wallace has recently blogged a very helpful post, “Saying a list of AIML responses in order”, on his Alicebot blog at http://alicebot.blogspot.com . After considerable fiddling, I have successfully installed Program E on my Windows desktop under Wampserver (Apache, MySQL, PHP). I have also found a very easy commercial product for importing RSS feeds into MySQL. Next I will try to bridge the RSS database and the Program E AIML database with Extensible Stylesheet Language Transformations (XSLT) using the previously mentioned xsl-easy.com database adapters… as well as implement Dr. Wallace’s "successor" function on the Program E AIML platform. Once I get the prototype working on my desktop, I will then endeavor to replicate it on a remote server for public access.
The long term goals of Project VagaBot are to create a conversational agent that can not only “read” books, but also web feeds, and “learn” to reply intelligently to questions, in this case on “green travel”, in effect an anthropomorphic frontend utilizing not only my book, "Vagabond Globetrotting 3", but also my entire http://meta-guide.com feed resources as backend. I am not aware of another project that currently makes the contents of a book available using a conversational agent, nor one that “learns” from web feeds. I hope to eventually be able to send the VagaBot avatar into smartphones using both voice output and input. I would be very interested in hearing from anyone interested in investing or otherwise supporting this development.
Dr. Rich Wallace has recently blogged a very helpful post, “Saying a list of AIML responses in order”, on his Alicebot blog at http://alicebot.blogspot.com . After considerable fiddling, I have successfully installed Program E on my Windows desktop under Wampserver (Apache, MySQL, PHP). I have also found a very easy commercial product for importing RSS feeds into MySQL. Next I will try to bridge the RSS database and the Program E AIML database with Extensible Stylesheet Language Transformations (XSLT) using the previously mentioned xsl-easy.com database adapters… as well as implement Dr. Wallace’s "successor" function on the Program E AIML platform. Once I get the prototype working on my desktop, I will then endeavor to replicate it on a remote server for public access.
The long term goals of Project VagaBot are to create a conversational agent that can not only “read” books, but also web feeds, and “learn” to reply intelligently to questions, in this case on “green travel”, in effect an anthropomorphic frontend utilizing not only my book, "Vagabond Globetrotting 3", but also my entire http://meta-guide.com feed resources as backend. I am not aware of another project that currently makes the contents of a book available using a conversational agent, nor one that “learns” from web feeds. I hope to eventually be able to send the VagaBot avatar into smartphones using both voice output and input. I would be very interested in hearing from anyone interested in investing or otherwise supporting this development.
20 January 2008
Corpus linguistics & Concgramming in Verbots and Pandorabots
One of the definitions of semantic, as in Semantic Web or Web 3.0, is the property of language pertaining to meaning, meaning being significance arising from relation, for instance the relation of words. I don’t recall hearing about corpus linguistics before deciding to animate and make my book interactive. Apparently there has been a long history of corpus linguistics trying to derive rules from natural language, such as the work of George Kingsley Zipf. As someone with a degree in psychology, I do know something of cognitive linguistics and its reaction to the machine mind paradigm.
The man who coined the term, called artificial intelligence "the science and engineering of making intelligent machines,” which today is referred to as "the study and design of intelligent agents." Wikipedia defines intelligence as “a property of the mind that encompasses… the capacities to reason, to plan, to solve problems, to think abstractly, to comprehend ideas, to use language, and to learn.” Computational linguistics has emerged as an interdisciplinary field involved with “the statistical and/or rule-based modeling of natural language.”
In publishing, a concordance is an alphabetical list of the main words used in a text, along with their immediate contexts or relations. Concordances are frequently used in linguistics to study the body of a text. A concordancer is the program that constructs a concordance. In corpus linguistics, concordancers are used to retrieve sorted lists from a corpus or text. Concordancers that I looked at included AntConc, ConcordanceSoftware, WordSmith Tools and ConcApp. I found ConcApp and in particular the additional program ConcGram to be most interesting. (Examples of web based concordancers include KWICFinder.com and the WebAsCorpus.org Web Concordancer.)
Concgramming is a new computer-based method for categorising word relations and deriving the phraseological profile or ‘aboutness’ of a text or corpus. A concgram constitutes all of the permutations generated by the association of two or more words, revealing all of the word association patterns that exist in a corpus. Concgrams are used by language learners and teachers to study the importance of the phraseological tendency in language.
I was in fact successful in stripping out all the sentences from my latest book, VAGABOND GLOBETROTTING 3, by simply reformatting them as paragraphs with MSWord. I then saved them as a CSV file, actually just a text file with one sentence per line. I was able to make a little utility which ran all those sentences through the Yahoo! Term Extraction API, extracting key terms and associating those terms with their sentences in the form of XML output, as terms equal title and sentences equal description. Using the great XSLT editor xsl-easy.com, I could convert that XML output quickly and easily into AIML with a simple template.
The problem I encountered was that all those key terms extracted from my book sentences when tested formed something like second level knowledge that you couldn’t get out of the chatbot unless you already knew the subject matter…. So I then decided to try adding the concgrams to see if that could bridge the gap. I had to get someone to create a special program to marry the 2 word concgrams from the entire book (minus the 100 most common words in English) to their sentences in a form I could use.
It was only then that I began to discover some underlying differences between the verbotsonline.com and pandorabots.com chatbot engine platforms. I've been using verbotsonline because it seemed easier and cheaper, than adding a mediasemantics.com character to the pandorabot. However, there is a 2.5 Meg limit with verbotsonline knowledgebases, which I've reached three times already. Also, verbotsonline.com does not seem to accept multiple SAME patterns with different templates, at least the AIML-Verbot Converter apparently removes the “duplicate” categories.
In verbots, spaces automatically match to zero or more words, so wildcards are only necessary to match partial words. This means in verbots words are automatically wildcarded, which makes it much easier to achieve matches with verbots. So far, I have been unable to replicate this simple system with AIML, which makes AIML more precise or controllable, but perhaps less versatile, at least in this case. Even with the AIML knowledgebase replicated eight times with the following patterns, I could not duplicate the same results in pandorabots as the verbots do with one file, wildcarding on all words in a phrase or term.
dog cat
dog cat *
_ dog cat
_ dog cat *
dog * cat
dog * cat *
_ dog * cat
_ dog * cat *
The problem I encountered with AIML trying to “star” all words was that when starred at the beginning of a pattern only one word was accepted and not more words, and when replaced with the underscore apparently affects pattern prioritization. So there I am at the moment stuck between verbots and pandorabots, not being able to do what I want with either, verbotsonline for lack of capacity and inability to convert “duplicate” categories into VKB, and pandorabots for inability to conform to my fully wildcarded spectral word association strategy….
The man who coined the term, called artificial intelligence "the science and engineering of making intelligent machines,” which today is referred to as "the study and design of intelligent agents." Wikipedia defines intelligence as “a property of the mind that encompasses… the capacities to reason, to plan, to solve problems, to think abstractly, to comprehend ideas, to use language, and to learn.” Computational linguistics has emerged as an interdisciplinary field involved with “the statistical and/or rule-based modeling of natural language.”
In publishing, a concordance is an alphabetical list of the main words used in a text, along with their immediate contexts or relations. Concordances are frequently used in linguistics to study the body of a text. A concordancer is the program that constructs a concordance. In corpus linguistics, concordancers are used to retrieve sorted lists from a corpus or text. Concordancers that I looked at included AntConc, ConcordanceSoftware, WordSmith Tools and ConcApp. I found ConcApp and in particular the additional program ConcGram to be most interesting. (Examples of web based concordancers include KWICFinder.com and the WebAsCorpus.org Web Concordancer.)
Concgramming is a new computer-based method for categorising word relations and deriving the phraseological profile or ‘aboutness’ of a text or corpus. A concgram constitutes all of the permutations generated by the association of two or more words, revealing all of the word association patterns that exist in a corpus. Concgrams are used by language learners and teachers to study the importance of the phraseological tendency in language.
I was in fact successful in stripping out all the sentences from my latest book, VAGABOND GLOBETROTTING 3, by simply reformatting them as paragraphs with MSWord. I then saved them as a CSV file, actually just a text file with one sentence per line. I was able to make a little utility which ran all those sentences through the Yahoo! Term Extraction API, extracting key terms and associating those terms with their sentences in the form of XML output, as terms equal title and sentences equal description. Using the great XSLT editor xsl-easy.com, I could convert that XML output quickly and easily into AIML with a simple template.
The problem I encountered was that all those key terms extracted from my book sentences when tested formed something like second level knowledge that you couldn’t get out of the chatbot unless you already knew the subject matter…. So I then decided to try adding the concgrams to see if that could bridge the gap. I had to get someone to create a special program to marry the 2 word concgrams from the entire book (minus the 100 most common words in English) to their sentences in a form I could use.
It was only then that I began to discover some underlying differences between the verbotsonline.com and pandorabots.com chatbot engine platforms. I've been using verbotsonline because it seemed easier and cheaper, than adding a mediasemantics.com character to the pandorabot. However, there is a 2.5 Meg limit with verbotsonline knowledgebases, which I've reached three times already. Also, verbotsonline.com does not seem to accept multiple SAME patterns with different templates, at least the AIML-Verbot Converter apparently removes the “duplicate” categories.
In verbots, spaces automatically match to zero or more words, so wildcards are only necessary to match partial words. This means in verbots words are automatically wildcarded, which makes it much easier to achieve matches with verbots. So far, I have been unable to replicate this simple system with AIML, which makes AIML more precise or controllable, but perhaps less versatile, at least in this case. Even with the AIML knowledgebase replicated eight times with the following patterns, I could not duplicate the same results in pandorabots as the verbots do with one file, wildcarding on all words in a phrase or term.
dog cat
dog cat *
_ dog cat
_ dog cat *
dog * cat
dog * cat *
_ dog * cat
_ dog * cat *
The problem I encountered with AIML trying to “star” all words was that when starred at the beginning of a pattern only one word was accepted and not more words, and when replaced with the underscore apparently affects pattern prioritization. So there I am at the moment stuck between verbots and pandorabots, not being able to do what I want with either, verbotsonline for lack of capacity and inability to convert “duplicate” categories into VKB, and pandorabots for inability to conform to my fully wildcarded spectral word association strategy….
08 January 2008
Books, metadata and chatbots… in search of the XML Rosetta Stone
I am an author and I build chatbots (aka chatterbots). A chatbot is a conversational agent, driven by a knowledgebase. I am currently trying to understand the best way to convert a book into a chatbot knowledgebase.
A knowledgebase is a form of database, and the chatbot is actually a type of search… an anthropomorphic form of search and therefore an ergonomic form of search. This simple fact is usually shrouded by the jargon of “natural language processing”, which may or may not be actual voice input or output.
According to the ruling precepts of the “Turing test”, chatbots must be as close as possible to conversational, and this is what differentiates them from pure “search”…. With chatbots there is a significant element of “smoke and mirrors” involved, which introduces the human psychological element into the machine in the form of cultural, linguistic and thematic assumptions and expectations, so becoming in a sense a sort of “mind game”.
I’m actually approaching this from two directions. I would also like to be able to feed RSS into a chatbot knowledgebase. There is currently no working example of this available. Parsing RSS into AIML (Artificial Intelligence Markup Language), the most common chatbot dialect, is problematic and yet to be cracked effectively. So, my thinking arrived at somehow breaking a book into a form that resembles RSS. The Wikipedia List of XML markup languages revealed a number of attempts to add metadata to books.
Dr. Wallace, the originator of AIML, recently responded on the pandorabots-general group, that using RSS title fields would usually be too specific to make them useful as chatbot concept triggers. However, I believe utilities such as the Yahoo! Term Extraction API could be used to create tags for feed items, which might then prove more useful when mapped to AIML patterns….
My supposition is that a *good* book index is in effect a “taxonomy” of that book. Paragraphs would generally be too large to meet the specialized “conversational” needs of a chatbot. The results of a conventional concordance would be too general to be useful in a chatbot…. If RSS as we know it is currently too specific to function effectively in a chatbot, what if that index were mapped back to the referring sentences as “tags”, somewhat like RSS?
I figure that if you can relatively quickly break a book down into a sentence “concordance”, you could then point that at something like the Yahoo! Term Extraction API to quickly generate relevant keywords (or “tags”) for each sentence, which could then be used in AIML as triggers for those sentences in a chatbot…. Is there such a beast as a “sentence parser” for a corpus such as a common book? All I want to do at this point is strip out all the sentences and line them up, as a conventional concordance does with individual words.
There are a number of examples of desktop chatbots using proprietary Windows speech recognition today, however to my knowledge there are currently no chatbots available online or via VoIP that accept voice input (*not* IM or IRC bots)…. So, I’ve also spent some time lately looking into voiceXML (VXML), ccXML and the Voxeo callXML, as well as the Speech Recognition Grammar Specification (SRGS) and the mythical voice browser…. The only thing I could find that actually accepts voice input online for processing is Midomi.com, which accepts voice input in the form of hummed tune for tune recognition…. Apparently goog411, which is basically interactive voice response (IVR) rather than true speech recognition, is as close as it gets to a practical hybrid online/offline voice search application at this time. So, what if Google could talk?
A knowledgebase is a form of database, and the chatbot is actually a type of search… an anthropomorphic form of search and therefore an ergonomic form of search. This simple fact is usually shrouded by the jargon of “natural language processing”, which may or may not be actual voice input or output.
According to the ruling precepts of the “Turing test”, chatbots must be as close as possible to conversational, and this is what differentiates them from pure “search”…. With chatbots there is a significant element of “smoke and mirrors” involved, which introduces the human psychological element into the machine in the form of cultural, linguistic and thematic assumptions and expectations, so becoming in a sense a sort of “mind game”.
I’m actually approaching this from two directions. I would also like to be able to feed RSS into a chatbot knowledgebase. There is currently no working example of this available. Parsing RSS into AIML (Artificial Intelligence Markup Language), the most common chatbot dialect, is problematic and yet to be cracked effectively. So, my thinking arrived at somehow breaking a book into a form that resembles RSS. The Wikipedia List of XML markup languages revealed a number of attempts to add metadata to books.
Dr. Wallace, the originator of AIML, recently responded on the pandorabots-general group, that using RSS title fields would usually be too specific to make them useful as chatbot concept triggers. However, I believe utilities such as the Yahoo! Term Extraction API could be used to create tags for feed items, which might then prove more useful when mapped to AIML patterns….
My supposition is that a *good* book index is in effect a “taxonomy” of that book. Paragraphs would generally be too large to meet the specialized “conversational” needs of a chatbot. The results of a conventional concordance would be too general to be useful in a chatbot…. If RSS as we know it is currently too specific to function effectively in a chatbot, what if that index were mapped back to the referring sentences as “tags”, somewhat like RSS?
I figure that if you can relatively quickly break a book down into a sentence “concordance”, you could then point that at something like the Yahoo! Term Extraction API to quickly generate relevant keywords (or “tags”) for each sentence, which could then be used in AIML as triggers for those sentences in a chatbot…. Is there such a beast as a “sentence parser” for a corpus such as a common book? All I want to do at this point is strip out all the sentences and line them up, as a conventional concordance does with individual words.
There are a number of examples of desktop chatbots using proprietary Windows speech recognition today, however to my knowledge there are currently no chatbots available online or via VoIP that accept voice input (*not* IM or IRC bots)…. So, I’ve also spent some time lately looking into voiceXML (VXML), ccXML and the Voxeo callXML, as well as the Speech Recognition Grammar Specification (SRGS) and the mythical voice browser…. The only thing I could find that actually accepts voice input online for processing is Midomi.com, which accepts voice input in the form of hummed tune for tune recognition…. Apparently goog411, which is basically interactive voice response (IVR) rather than true speech recognition, is as close as it gets to a practical hybrid online/offline voice search application at this time. So, what if Google could talk?
30 December 2007
AIML <-> OWL ??
Since I posted my original query to the pandorabots-general list in July, I'm beginning to understand the concepts involved a little better, thanks also to replies from this group and others, such as the protege-owl list.
In a comment to my recent blog entry ("I'm dreaming of RSS in => AIML out"), Jean-Claude Morand has mentioned that RSS 1.0 would probably be more conducive to conversion into RDF or AIML than RSS 2.0. He also mentioned that the Dublin Core metadata standard may eventually overtake RSS in primacy....
So, can XSL transforms really be used to translate between RSS and RDF, and between RDF and AIML?? My understanding at this point is that talking about AIML and OWL is a bit like apples and oranges.... Apparently the output from an OWL Reasoner would be in RDF? I have by now discovered the Robitron group and am finding that archive to be a rich resource....
What does this have to do with Pandorabots? I would like to address a brief question, in particular to Dr. Wallace... what do you see as the impediments to upgrading the Pandorabots service to include an OWL Reasoner (or in chaining it to another service that would provide the same function)? Surely you've considered this.... Where are the bottlenecks (other than time and money of course)? Is it an unreasonable expectation to be able to upload OWL ontologies much the same as we can upload AIML knowledgebases today?
As I have mentioned previously, one of my interests is creating knowledgebases on the fly using taxonomies. My belief is that quick and dirty knowledgebases are a more productive focus than pouring time and energy into trying to meet the requirements of the Turing test (another rant for another day....) Certainly with chatbots there is a substantial element of smoke and mirrors involved in any case.... One can always go back and refine as needed based on actual chat logs.
The next step for me will be to try and convert my most recent book, VAGABOND GLOBETROTTING 3, into a VagaBot.... I would like to hear from anyone with experience in converting books into AIML knowledgebases! My supposition is that a *good* book index is in effect a "taxonomy" of that book.... My guess is that I can use the index entries as patterns, and their referring sections as templates... to create at least the core of a knowledgebase. If more detail is needed then a concordance can always be applied to the book.
After that I hope to tackle creating quick and dirty AIML knowledgebases on the fly from RSS feed title and description fields... not in pursuit of the chimera of the Turing test, but simply to build a better bot. (Now, I wonder if anyone has ever created RSS from a book?!? ;^))
In a comment to my recent blog entry ("I'm dreaming of RSS in => AIML out"), Jean-Claude Morand has mentioned that RSS 1.0 would probably be more conducive to conversion into RDF or AIML than RSS 2.0. He also mentioned that the Dublin Core metadata standard may eventually overtake RSS in primacy....
So, can XSL transforms really be used to translate between RSS and RDF, and between RDF and AIML?? My understanding at this point is that talking about AIML and OWL is a bit like apples and oranges.... Apparently the output from an OWL Reasoner would be in RDF? I have by now discovered the Robitron group and am finding that archive to be a rich resource....
What does this have to do with Pandorabots? I would like to address a brief question, in particular to Dr. Wallace... what do you see as the impediments to upgrading the Pandorabots service to include an OWL Reasoner (or in chaining it to another service that would provide the same function)? Surely you've considered this.... Where are the bottlenecks (other than time and money of course)? Is it an unreasonable expectation to be able to upload OWL ontologies much the same as we can upload AIML knowledgebases today?
As I have mentioned previously, one of my interests is creating knowledgebases on the fly using taxonomies. My belief is that quick and dirty knowledgebases are a more productive focus than pouring time and energy into trying to meet the requirements of the Turing test (another rant for another day....) Certainly with chatbots there is a substantial element of smoke and mirrors involved in any case.... One can always go back and refine as needed based on actual chat logs.
The next step for me will be to try and convert my most recent book, VAGABOND GLOBETROTTING 3, into a VagaBot.... I would like to hear from anyone with experience in converting books into AIML knowledgebases! My supposition is that a *good* book index is in effect a "taxonomy" of that book.... My guess is that I can use the index entries as patterns, and their referring sections as templates... to create at least the core of a knowledgebase. If more detail is needed then a concordance can always be applied to the book.
After that I hope to tackle creating quick and dirty AIML knowledgebases on the fly from RSS feed title and description fields... not in pursuit of the chimera of the Turing test, but simply to build a better bot. (Now, I wonder if anyone has ever created RSS from a book?!? ;^))
22 December 2007
I'm dreaming of RSS in => AIML out
I am still trying to get my head around the relationship between chatbots and the Semantic Web, or Web 3.0.... Any thoughts or comments on the precise nature of this relationship are welcome.
Converting from VKB back into AIML was my first crash course in working with XML dialects.... Since then the old lightbulb has gone off, or rather "on" I should say, and it suddenly dawned on me that the whole hullabaloo about Web 2.0 largely centers on the exchange of metadata, most often in the form of RSS, another XML dialect.
I was really stoked to learn of the work of Eric Freese, apparently processing logic using the Jena framework then manually(?) converting that RDF into AIML; however, I continue to wait for word of his "Semetag/AIMEE" example at http://www.semetag.com .
My understanding is that it is quite do-able, as in off the shelf, to pull RSS into a database and accumulate it there.... Could such a database of RSS not be used as a potential knowledgebase for a chatbot?
The missing element seems to be the processing, or DL Reasoner(?).... I have been unable to find any reference to such a web-based, modular DL Reasoner yet....
http://www.knoodl.com seems to be the closest thing to a "Web 2.0-style" collaborative ontology editor, which is fine for creating ontologies collectively, however falls short of meeting the processing requirement.
In short, I'm dreaming of RSS in => AIML out. At this point I would be happy with a "toy" or abbreviated system just to begin playing around with all this affordably (not least time-wise). So it seems what's still needed is a simple, plug and play "Web 2.0-style" (or is that "Web 3.0" style?) web-based DL Reasoner that accepts common OWL ontologies, then automagically goes from RDF into AIML....
Converting from VKB back into AIML was my first crash course in working with XML dialects.... Since then the old lightbulb has gone off, or rather "on" I should say, and it suddenly dawned on me that the whole hullabaloo about Web 2.0 largely centers on the exchange of metadata, most often in the form of RSS, another XML dialect.
I was really stoked to learn of the work of Eric Freese, apparently processing logic using the Jena framework then manually(?) converting that RDF into AIML; however, I continue to wait for word of his "Semetag/AIMEE" example at http://www.semetag.com .
My understanding is that it is quite do-able, as in off the shelf, to pull RSS into a database and accumulate it there.... Could such a database of RSS not be used as a potential knowledgebase for a chatbot?
The missing element seems to be the processing, or DL Reasoner(?).... I have been unable to find any reference to such a web-based, modular DL Reasoner yet....
http://www.knoodl.com seems to be the closest thing to a "Web 2.0-style" collaborative ontology editor, which is fine for creating ontologies collectively, however falls short of meeting the processing requirement.
In short, I'm dreaming of RSS in => AIML out. At this point I would be happy with a "toy" or abbreviated system just to begin playing around with all this affordably (not least time-wise). So it seems what's still needed is a simple, plug and play "Web 2.0-style" (or is that "Web 3.0" style?) web-based DL Reasoner that accepts common OWL ontologies, then automagically goes from RDF into AIML....
14 February 2007
Marcus Endicott's Second Life Travel Guide
Lately, I've been working on porting my ByronBot (http://www.mendicott.com/byronbay/) over to the currently popular Second Life (http://www.mendicott.com/secondlife/) 3D virtual world.
My ByronBot is built on the Verbots Online (http://www.verbotsonline.com/) platform.
Metaverse Technology (http://www.metaversetech.com/) has built a Second Life Chatbot product based on the Pandorabots (http://www.pandorabots.com/) platform.
Pandorabots is based on Artificial Intelligence Markup Language, rather than the proprietary Verbot KnowledgeBase files.
The Metaverse Technology S-Bot product combines the Second Life Linden Scripting Language with the Pandorabots AIML.
There is an open source AIML-Verbot Converter tool going from AIML to VKB, but not vice versa....
So, I've been using the recommended GaitoBot AIML editor to recreate the ByronBot KnowledgeBase in Pandorabots AIML.
You should be able to find my new Second Life ByronBot near Byron Bay @ 5 O'Clock Somewhere at http://slurl.com/secondlife/Plush%20Omega/101/21/22 .
Ideally, the Second Life ByronBot will emulate a tourist information officer, of for instance a DMO (Destination Marketing Organization), inside the 3D virtual world... providing information about Byron Bay, Northern New South Wales, Australia.
My ByronBot is built on the Verbots Online (http://www.verbotsonline.com/) platform.
Metaverse Technology (http://www.metaversetech.com/) has built a Second Life Chatbot product based on the Pandorabots (http://www.pandorabots.com/) platform.
Pandorabots is based on Artificial Intelligence Markup Language, rather than the proprietary Verbot KnowledgeBase files.
The Metaverse Technology S-Bot product combines the Second Life Linden Scripting Language with the Pandorabots AIML.
There is an open source AIML-Verbot Converter tool going from AIML to VKB, but not vice versa....
So, I've been using the recommended GaitoBot AIML editor to recreate the ByronBot KnowledgeBase in Pandorabots AIML.
You should be able to find my new Second Life ByronBot near Byron Bay @ 5 O'Clock Somewhere at http://slurl.com/secondlife/Plush%20Omega/101/21/22 .
Ideally, the Second Life ByronBot will emulate a tourist information officer, of for instance a DMO (Destination Marketing Organization), inside the 3D virtual world... providing information about Byron Bay, Northern New South Wales, Australia.
31 December 2006
ByronBot - Byron Bay, Australia
Lately I've been working with chatterbot technology, a computer program designed to simulate an intelligent conversation with one or more human users via auditory or textual methods, using AIML or Artificial Intelligence Markup Language, an XML dialect for creating natural language software agents.
My latest creation is ByronBot (http://www.mendicott.com/byronbay/), which you can ask questions about Byron Bay and the Rainbow Region of northern New South Wales, Australia - where I now live.
Previously, I created the meta-guide geobot (http://www.meta-guide.com/), which knows continents, regions, all countries, all of their capitals, and can provide more such as maps, travel books and cool travel videos - as well as country-specific information about ecotourism and sustainable tourism.
One associate has compared ByronBot with Microsoft's Ms. Dewey (http://www.msdewey.com/), an Adobe Flash-based experimental interface for Windows Live Search.
My latest creation is ByronBot (http://www.mendicott.com/byronbay/), which you can ask questions about Byron Bay and the Rainbow Region of northern New South Wales, Australia - where I now live.
Previously, I created the meta-guide geobot (http://www.meta-guide.com/), which knows continents, regions, all countries, all of their capitals, and can provide more such as maps, travel books and cool travel videos - as well as country-specific information about ecotourism and sustainable tourism.
One associate has compared ByronBot with Microsoft's Ms. Dewey (http://www.msdewey.com/), an Adobe Flash-based experimental interface for Windows Live Search.
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