The article of EISIE
by Andrzej Góralczyk
The purpose of intellectual communication is not merely to send and receive a message. It is to understand the message.
It seems trivial as long as it concerns everyday life. We are accustomed with everyday misunderstandings causing no more than a smile or laugh.
However, understanding is not trivial when it comes to the communication with big value or big risk behind. Think for example about a huge loss when millions of people do not find relevant information in the Internet at the first glance, despite it exists there. How big value could carry education if really multicultural thanks to better understanding of people with different mindset, concepts and lexicon? How big chances for success could have business thanks to better understanding trends hidden in the background of the stream of messages from the company and form outside? How secure could we feel thanks to better understanding of the dynamics of the road congestion, epidemic disease, “terrorist” attack, blackout or hurricane evolution?
Understanding and technology.
The notion of intellectual communication appeared for a short time in the beginning of 1970-ties. There was a model of two different communicating thesauri. And there was a simple question – how they come to understanding from the initial state of misunderstanding or understanding nothing at all due to the differences in their content and structure? There were attempts to build a theory of communication between such two different thesauri. Today it is clear, that the notion is to broad to be taken as the subject of useful theory albeit it is still inspiring and convenient framework to depict broad domain of the issues of communication and understanding.
Intellectual communication with artificial agent or with the use of artificial mediator became hot issue today. The research and practical applications cover such diverse fields as machine translation, intelligent building, geographic information systems (GIS), cognitive systems, semantic web and semantic search, expert systems, advanced Business Intelligence, “intelligent” user interfaces, security and anti-criminal analytics, early threat detection etc.
Some of technologies and concepts underlying these applications are quite new and immature, and suffer from ignoring thousands years of humanistic knowledge about meaning, reasoning and learning. Some suffer from dreams. For example a dream about computer able to understand, think and decide like a man or even more adequately, exactly, logically etc. We are sure that – except very special applications, like expert systems or mission-critical devices – imitating a man or human thinking (reasoning) is a nonsense. This is a user who has to understand, think and decide! Computer is, potentially, a strong tool to facilitate these tasks. The more pertinent division of tasks between man and machine the more sensible application of IT in intellectual communication.
Consider for example so called Semantic Web . The idea of Tim Berners-Lee, the inventor of World Wide Web, is to extend this net in such a manner that its content can be comprehended by machines. What for? In order to enable artificial agents (i. e. computer applications) provide some services to the users. For example to appoint a visit to therapist’s clinic: finding the “trustworthy” clinic near patient’s house, checking the calendars of the patient and doctor, etc. So, the agent has to be able to reformulate the user’s request to the query appropriate for searching the web, to use the query to find appropriate data in the web, to present options to te user, and finally to make some transactions in both calendars. We could describe such behaviour of the agent as “using data in particular manner” or “combining data from various sources at particular purpose” or so. It seems to be much less than human understanding.
The experts in the field of Semantic Web tend to equip machines with huge controlled vocabularies called “ontologies”. It is nothing to do with the ontology in its original philosophical sense. It is rather a specific “map” of the particular domain (or conceptualisation of the part of the world). Vocabulary has to provide a substance for machine reasoning, and therefore it has to have some special properties. First, it has to be exact and unambiguous – very different from our human everyday conceptualisation. Second, the concepts of an ontology have to be organized in a strict tree-like hierarchy (so called taxonomy), with formally defined relations between them – the structure very different from that of real world.
In this way, using two inadequate models, the experts want to enable artificial agent to communicate about real world to the real man in real everyday situations! Seems not impossible, albeit very complicated and resource-demanding task.
The Meaning and the Understanding
There are several ways to model the meaning in order to make it computable with machine if we want to use computing to simulate human thinking. For example, we can say that meaning of the expression (a sentence, a word, a concept) is a set of “things” (entities, beings) it denotes (indicates to). Such definition is very convenient as long as simulating thinking with a logic or algebra of sets is sufficient for particular task. In fact, such classical approach is widely used in tree-like formal ontologies mentioned above. However, many experts in the field are getting conscious that usefulness of the approach is very limited if the relations in the tree are purely logical. Many rediscover the context – a wealth of semantic, factual, casual, modal etc. relations enriching the meaning in real use (see , theses 3.3 and 3.314, and for example ). Some endeavour to the other extreme proclaiming “meaning is context” or even “context is king”. In fact, understanding has very little to do with indicating, and very much to do with the context.
By the way – the two concepts of meaning seem to be incompatible because the first is about function (indicating), and the second is about structure (relations). Probably this is the fundamental reason why the problem of identifier is not solvable within the framework of Semantic Web, nor in the framework of Topic Maps (contrary to the illusion of advocates) [4-6]. However, there is no rationale to dismiss the supposition that there exist frameworks in which a problem of identifier vanishes.
Even if we relax the requirement of machine reasoning, leaving only the requirement of machine “understanding”, the obvious solution is a huge dictionary defining all the words (terms) useful in the communication. If no restriction is imposed as to the domain of communication, the dictionary should be countable albeit infinitive. So, the machine “having knowledge” about Everything is another nonsense, for technical reasons. There can be domain ontologies instead (see for example  and ).
Hermeneutic Searching Engine
We are developing solutions for intellectual communication based on the requirements much more relaxed that those discussed above. First, we don’t assume that machine has to “understand” – it is human who has to understand. Second, we don’t suppose the idea of communicating Everything is reasonable in practice – there can be nothing like “shared vocabulary” about Everything. Moreover, there is only partially shared vocabulary in general case of intellectual communication, and this is why one party is often asking the other party for something she doesn’t know!. Third, we believe that properly balanced abstraction (see below) is a proper way to reduce the demand for terms in the dictionary mutually understood by both parties of communication and, at the same time to cover context broad enough to enable rich understanding. Simply speaking – abstraction enables to communicate rich content with minimum words. This “economy” is compliant with one of the main rules of our approach to system engineering.
Take for example a searching engine. We require it should find, in the “documentation pool”, a set of information objects documenting context of particular matter in order to improve User’s understanding of this matter. What exactly the engine should do? It should “understand” the User’s query and find a documentation of the same context plus some extra RELEVANT context beyond the meaning of the query. And nothing more, if we want the communication between User and documentation pool be effective. Then, it is User’s task to gain new knowledge from this extra context. A searching machine mediating such communication between the User and the “documentation pool” is more then semantic searching engine. It can be called a hermeneutic searching engine.
Like in many known semantic search projects, our hermeneutic searching engine is not for Everything. User has to choose “a domain” before submitting query. The query submitted is in fact a boolean expression of the query term and a code of a domain. And here is the trick – “domain” represents some preconfigured context. More precisely – a “piece od context” small enough to avoid User’s confusion and overloading machine with computing tasks, and big enough to “probe” documentation pool and find all meaning documentation and nothing more (i. e. complete documentation).
In practice, there is a margin of precision and unambiguity and the margin of completeness of documentation harvested. However, in most practical cases we examined up to date, the margin is narrower than that in other searching engines, esp. those striving for “relevance”, for example Google.
How to build “a domain” is our secret and a heart of the solution based on the invention we made in the early 1980-ties. The ontology used is based on rigorous philosophical analysis of human “knowledge systems”. Of course, it is not a tree-like hierarchy and only few relations are subject to some formal requirements. We extended this original ontology appropriately and decomposed it into a set of categories. Unlike in classical philosophy, these categories are not the “highest genera of entities” nor the “millions of terms defining Everything”. They are a little bit more abstract than entities and relations we used to talk about in everyday life. We can call it a “weak abstraction”. There exist a limited number of these categories because they are abstractions. Out of about 120 categories possible in our ontology only about 35 are sufficiently meaningful. These 35 categories form a kind of “alphabet”. Using this alphabet we construct the working contexts. Then we translate them into particular language, since the meaning is language independent. Finally, we adjust the semantic representation of the context and get “the domain”.
It is easy to imagine how powerful can be the hermeneutic machine having the inverted index with not only words but also with the codes of “domains” as the entries.
Finally, it is worth to note the big difference between our hermeneutic searching engine and both the “relevance” searching engines and “artificial reasoning” solutions. The latter are based on the assumption, that the angel is in the details. “Be more specific” is the usual advice of the “relevance” searching engine. “Narrow your searching terms” appeal the “reasoning machines”. Our hermeneutic searching engine is doing something partially opposite: using specific vocabulary it is narrowing the search but, at the same time, it attempts to EXTEND the query with some more general context. For example in order to learn “who gained and who lose in relation to Hurricane Katrina” one should submit a query consisting of a term “Hurricane Katrina” and the codes of 3 contexts with “weak abstraction”: people, organisations and economic values. We believe that a little bit of abstraction in the context is the essence of understanding.
The example of hermeneutic searching engine illustrates some particularities of our approach to the system engineering. Let’s summarize:
- Clear distinction between the tasks of the user and the technical (and organisational) device enables us to avoid both unjustified automation and unnecessary human work;
- Of big importance is the proper compromise between benefits of the precision of control over the course of events and the “cost” of control; in the case of searching engine the question is of the sufficient unambiguity and completeness of the documentation of particular context;
- Our ideal is the rule of economy: minimum measures causing maximum effect; in the Intellectual Communication Systems the challenge is to find rich-content expressions in order to communicate more with less words; the “weak abstraction” applied in our searching engine makes the “domains” specific and general at the same time.
 Berners-Lee, T., Hendler, J., Lassila, O., The Semantic Web.
A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American, May 17, 2001, http://www.sciam.com/print_version.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21
 Wittgenstein, L., Tractatus Logico-Philosophicus
 Wilson, S., Comment & Analysis: Why Context Is King, http://zope.cetis.ac.uk/content/20010827123828
 Clark, K. G., Identity Crisis, XML.com, September 11, 2002, http://www.xml.com/pub/a/2002/09/11/deviant.html
 Berners-Lee, T., What HTTP URIs Identify, Dresign Issues for the World Wide Web, June 9, 2002, rev. October 29, 2006, http://www.w3.org/DesignIssues/HTTP-URI2
 Pepper, S., Schwab, S., Curing the Web’s Identity Crisis. Subject Indicators for RDF., Ontopia, http://www.ontopia.net/topicmaps/materials/identitycrisis.html
 Knowlege Zone – One Stop Shop for Ontologies, http://smi-protege.stanford.edu:8080/KnowledgeZone/
 DAML Ontology Library, http://www.daml.org/ontologies/
September 2nd, 2007
Learning public opinion (or sentiment) about Your brand in traditional way is expensive because surveys or focus groups take much time and human work. Probably in near future alternative solution will gain recognition as some technology vendors launched tools for brand monitoring using text analytics. Initial review of these attempts appeared yesterday on SmartData Collective.
Monitoring brand using discourse analysis differs, to some extent, from the approach based on text analysis. I have very fresh example – a tool for monitoring opinion about retail nets (supermarkets). And now some words how it is made and how it works.
Building Monitor. Analysis of the discourse in the corpus of Internet discussions related to supermarkets gave a collection of subjects interesting for interlocutors, and a collection of expressions of their attitudes. Using these results the complex queries for semantic search were built for the learning research. It is the crucial stage – we should learn very details of the discourse, and get its math at the same time, as the basis for justification and calibration of the Monitor. The final task is relatively easy – to implement the results and build a “machine” using accessible technology.
How Monitor works. The data for each retail brand is collected using semantic search. Monitor makes all the calculus according to calibrating formulas and provides figures ready for presentation. Please see the pictures made for presentation only (not production version).
First are the “profile” – how the brand is perceived, i. e. how it is distinguished vs the average Internet discourse. The result of such kind is often astonishing because the picture dramatically differs from that of Customer’s (user of monitor) wishes, from official image and marketing buzz. Moreover, the interlocutors’ categories (vertical in the charts) also differ.
Then there is a comparison of the brands monitored. The charts show how people value each brand with regard to the same categories. 2 charts with negative opinions (general index only) are presented as the example.
The third important group of results regards monitoring itself, i. e. presentation of the changes. It depends on the Customer needs. Some customers want to observe the effects of promotional campaigns, and for such purpose day-to-day monitoring is appropriate. Some want to know the general trends… etc.
Last Sunday I submitted my comment to the people vs machine debate in Research Magazine. Some readers of this comment asked me how I get 97% accuracy of sentiment changes’ measurement in the Web Mining.
Web text analytics is rather new field of research and everybody is using its own approach. So, I would only advice – don’t want to be too quick. If you collect millions of records and focus on thousands of specific sentiment-rich expressions, first look at this data. Make some basic descriptive statistics (Yes!), make some charts of the frequency distributions etc. Try to find proper way of stratification, using your best proven approaches and tools. Don’t avoid this basic examination – I write this because I see many freshmen in analytic business who want to cut corners.
If you find good way of data stratification you will undoubtedly notice, that some expressions occur most frequently in one or two or three specific contexts or specific subject domains. Follow this clue, and limit further research to these expressions. This is the first step to the discourse mining (not simply text mining).
Next steps are obvious. Look for relations between various characteristics of the contexts, subject domains, and these “good” expressions. Make clustering in order to select subjects domains and texts you need. Make the selection from your corpus of texts.
There are a lot of tools to extrude rich and accurate information from data selected in this way.
Limiting the scope of study is the first and very basic way to streamline any research process. It is also a basic step used in Industrial Engineering in streamlining any manufacturing or business process.