systems that know and understand and think and learn
In preparation for generating RIF and SBVR from the Linguist, we have produced an OWL ontology for the pertinent aspects of the SBVR specification. We hope that this is helpful to others and would sincerely appreciate any corrections or comments on how to improve it.
Paul
Benjamin Grosof and I will be presenting the following review of recent work at Vulcan towards Digital Aristotle as part of Project Halo at SemTechBiz in San Francisco the first week of June.
We show how users can rapidly specify large bodies of deep logical knowledge starting from practically unconstrained natural language text.
English sentences are semi-automatically interpreted into predicate calculus formulas, and logic programs in SILK, an expressive knowledge representation (KR) and reasoning system which tolerates practically inevitable logical inconsistencies arising in large knowledge bases acquired from and maintained by distributed users possessing varying linguistic and semantic skill sets who collaboratively disambiguate grammar, logical quantification and scope, co-references, and word senses.
The resulting logic is generated as Rulelog, a draft standard under W3C Rule Interchange Format’s Framework for Logical Dialects, and relies on SILK’s support for FOL-like formulas, polynomial-time inference, and exceptions to answer questions such as those found in advanced placement exams.
We present a case study in understanding cell biology based on a first-year college level textbook.
Our efforts at acquiring deep knowledge from a college biology text have enabled us to answer a number of questions that are beyond what has been previously demonstrated.
For example, we’re answering questions like:
A couple of these are at higher levels on the Bloom scale of cognitive skills than Watson can reach (which is significantly higher than search engines).
As some other posts have shown in images, we can translate completely natural sentences into formal logic. We actually do the reasoning using Vulcan’s SILK, which has great capabilities, including defeasibility. We can also output to RIF or SBVR, but the temporal aspects and various things such as modality and the need for defeasibility favor SILK or Cyc for the best reasoning and QA performance.
One thing in particular is worth noting: this approach does better with causality and temporal logic than is typically considered by most controlled natural language systems, whether they are translating to a business rules engine or a logic formalism, such as first order or description logic. The approach promises better application development and knowledge management capabilities for more of the business process management and complex event processing markets.
We’re working with the English Resource Grammar (ERG), OWL, and Vulcan’s SILK to educate the machine by translating textbooks into defeasible logic. Part of this involves an ontology that models semantics more deeply than the ERG, which is based on head-driven phrase structure grammar (HPSG), which provides deeper parsing and, with the ERG and the DELPH-IN infrastructure, also provides a simple under-specified semantic representation called minimal recursion semantics (MRS).
We’re having a great time using OWL to clarify and enrich the semantics of the rich model underlying the ERG. Here’s an example, FYI. If you’d like to know more (or help), please drop us a line! Overall the project will demonstrate our capabilities for transforming everyday sentences into RIF and business rule languages using SBVR extended with defeasibility and other capabilities, all modeled in the same OWL ontology.
What triggered this blog entry was a bit of a surprise in seeing that whether or not an adjective could be used depictively is sometimes encoded in the lexicon. This is one of the problems of TDL versus a description-logic based model with more expressiveness. It results in more lexical entries than necessary, which has been discussed by others when contrasted with the attributed logic engine (ALE), for example.
In trying to model the semantics of words like ‘same’ and ‘different’, we are scratching our heads about these lines from the ERG’s lexicon:
One of the interesting things about lexicalized grammars is that lexical entries (i.e., ‘words’) are described with almost arbitrary combinations of their lexical, syntactic, and semantic characteristics.
The preceding code is expressed in a type description language (TDL) used by the Lisp-based LKB (and its C++ counterpart, PET, which are unification-based parsers that produce a chart of plausible parses with some efficiency. What is given above is already deeper than what you can expect from a statistical parser (but richer descriptions of lexical entries promises to make statistical parsing much better, too).
Unfortunately, there is no available documentation on why the ERG was designed as it is, so the meaning of the above is difficult to interpret. For example, the types of lexical entries (the symbols ending in ‘_le’) referenced above are defined as follows:
Needless to say, that’s a mouthful! Chasing this down, the following ‘informs’ us that “the same”, which uses type #2 above, is defined using the following lexical types:
Which is to say that it is non-conjunctive, complements a head to form a phrase, can’t be affixed, cannot constitute a main clause, and is a word.
The fact that the lexical entry for “the same” is adjectival is given the definition of the following type(s) used in the SYNSEM feature:
Which is to say that it is a comparative or superlative adjectival word (even though it consists of two lexemes in its ‘orthography’) that involves two semantic arguments including one complement which may be unexpressed prepositional phrase. A comparative or superlative adjective, in turn, is non-conjunctive, complements a head to form a phrase, is non-affix bearing (?), and non-clausal, as defined by the type ‘nonc-hm-nab’ above.
The types used in the syntax and semantic (i.e., SYNSEM) feature of the two lexical types are defined as follows (none of which is documented):
In a moment, we’ll discuss the types used in the second of these, but first, some basics on the semantics that are mixed with the syntax above.
In effect, the above indicates that a new ‘elementary predication’ will be needed in the MRS to represent the adjectival relationship in the logic derived in the course of parsing (i.e., that’s what ‘unsp_ind’ means, although it’s not documented, which I will try not to bemoan much further.)
The following indicates that the newly formed elementary predicate is not (initially) within any scope and that it has two arguments whose semantics (i.e., their RELations) are concatenated for propagation into the list of elementary predications that will constitute the MRS for any parses found.
The last of these expresses that the constuction is lexical rather than phrasal (which includes clausal in the ERG).
Continuing with the definition of “the same” as an adjective, the following finally clarifies what it means to be a basic adjective:
Well, ‘clarifies’ might not have been the right word! Essentially, it indicates that the adjective may have an optional degree specifier (which semantically modifies the predicate of the adjective) and that the predicate specified in the lexical entry becomes the predicate used in the MRS. The rest is defined below:
Which ends with a bunch of basic setup types except for constraining for relation for an adjective to be ‘basically adjectivally’ on the first two lines. Also on these first two lines, it specifies that its subject and its specifier, if any, must be completed (i.e., empty) and agree with its non-conjunctive argument (which is not to say that it cannot be conjunctive, but that it modifies the conjunction as a whole, if so.) Whether or not it is expressed will determine if there are any further predicates about its arguments or if its unexpressed argument is identified by an otherwise unreferenced variable in any resulting MRS.
The lexical grounding of this type specification is given below, indicating that it may (or not) have phonology (e.g., pronunciation, such as whether its onset is voiced) and if and how and with what punctuation it may appear, if any. In general, a semantic argument may be lexical or phrasal and optional but if it appears it corresponds to some semantic index (think variable) in sort of predicate in any resulting MRS. (The *_min types do not constrain the values of their features any further).
Note that an adjective is not possess-able and that it modifies something nominal (or a title) and that if it has a specifier that it is a quantifier or degree (e.g., ‘very’). Again, an adjective cannot function as a main clause or be conjunctive (in and of itself).
Finally, if you look far above you will see that the basic semantics of an adjective with an additional semantic argument is ‘intersective’, as in:
Here, the length 0 difference list and the following definitions indicate that intersective semantics do not accept anything but local modification:
AVM stands for ‘attribute value matrix’, which is the structure by which types and their features are defined (with nesting and unification constraints using # to indicate equality).
By now you’re probably getting the idea that there is fairly significant model of the English language, including its lexical and syntactic aspects, but if you look there is a lot about semantics here, too.
If you are considering the use of any of the following business rules management systems (BRMS):
you can learn a lot by carefully examining this video on decisions using scoring in Ilog. (The video is also worth considering with respect to Corticon since it authors and renders conditions, actions, and if-then rules within a table format.)
This article is a detailed walk through that stands completely independently of the video (I recommend skipping the first 50 seconds and watching for 3 minutes or so). You will find detailed commentary and insights here, sometimes fairly critical but in places complimentary. JRules is a mature and successful product. (This is not to say to a CIO that it is an appropriate or low risk alternative, however. I would hold on that assessment pending an understanding of strategy.)
The video starts by creating a decision table using this dialog:
Note that the decision reached by the resulting table is labeled but not defined, nor is the information needed to consult the table specified. As it turns out, this table will take an action rather than make a decision. As we will see it will “set the score of result to a number”. As we will also see, it references an application. Given an application, it follows references to related concepts, such as borrowers (which it errantly considers synonomous with applicants), concerning which it further pursues employment information.
Continue reading “IBM Ilog JRules for business modeling and rule authoring”
I had the pleasure of visiting with some fine folks at Cycorp in Austin, Texas recently. Cycorp is interesting for many reasons, but chiefly because they have expended more effort developing a deeper model of common world knowledge than any other group on the planet. They are different from current semantic web startups. Unlike Metaweb‘s Freebase, for example, Cycorp is defining the common sense logic of the world, not just populating databases (which is an unjust simplification of what Freebase is doing, but is proportionally fair when comparing their ontological schemata to Cyc’s knowledge). Not only does Cyc have the largest and most practical ontology on earth, they have almost incomprehensible numbers of formulas[1] describing the world. Continue reading “Cyc is more than encyclopedic”
Have you heard the one about how to drive BPM people crazy?
Ask them the question that drives CEP people crazy!
Last fall, at the RuleML conference in Orlando, was the first time I heard a consensus that a standard ontology of events and processes was sorely needed. I’ve had a number of discussions with others on this over the interim until today’s posts by Paul Vincent, summing up an OMG meeting in Washington, DC, and Sandy Kelmsley’s comments on a survey of 590 business process modeling notation users. Continue reading “In the names of CEP and BPM”
James Taylor’s blog today on rules being core to BPM and SOA in which he discussed reuse had a particularly strong impact on me following a trip yesterday. During a meeting with the insurance and retail banking practice leaders at a large consulting firm, we looked for synnergies between applications related to investment and applications related to risk. Of course, during that conversation, we discussed whether operational rules could be usefully shared across these currently siloed areas, but we landed up discussing what they had in common in terms of business concepts, definitions, and fundamental truths or enterprise wide governance. It was clear to us that this was the most fruitful area to develop core, reusable knowledge assets.
In his post, James agrees with the Butler Group’s statement:
Possibly the most important aspect of a rules repository, certainly in respect of the stated promise of BPM, Service Oriented Architecture (SOA), and BRMS, is the ability for the developer to re-use rules within multiple process deployments.
I have several problems with this statement: Continue reading “Rules are not enough. Knowledge is core to reuse.”
Work on acquiring knowledge about science has estimated the cost of encoding knowledge in question answering or problem solving systems at $10,000 per page of relevant textbooks. Regrettably, such estimates are also consistent with the commercial experience of many business rules adopters. The cost of capturing and automating hundreds or thousands of business rules is typically several hundred dollars per rule. The labor costs alone for a implementing several hundred rules too often exceed $100,000.
The fact that most rule adopters face costs exceeding $200 per rule is even more discouraging when this cost does not include the cost of eliciting or harvesting functional requirements or policies but is just the cost of translating such content into the more technical expressions understood by business rules management systems (BRMS) or business rule engines (BRE).
I recommend against adopting any business rule approach that cannot limit the cost of automating elicited or harvested content to less than $100 per rule given a few hundred rules. In fact, Automata provides fixed price services consistent with the following graph using an approach similar to the one I developed at Haley Systems.
A client recently asked me for guidance in establishing a center of excellence concerning business rules within their organization. Their objectives included:
I was quickly surprised to find myself struggling to write down recommendations for the skill set required to seed the core staff. My recommendations were less technical than the client may have expected. After further consideration, it became clear than any discrepancy in expectations arose from differences in our unvoiced strategic assumptions. Objectives, such as those listed above, are no substitute for a clearly articulated mission and strategy.
Continue reading “Managing Semantics, Vocabulary and Business Rules as Knowledge”
