Using Natural Language and SBVR to Author Unambiguous Business Governance Documents

Donald Chapin John Hall Business Semantics Ltd Model Systems Flat 21, Dovedale Cottages 17 Melcombe Court 240a Battersea Park Road Dorset Square London SW 11 4LN London NW1 5EP United Kingdom United Kingdom [email protected] [email protected]

term and use it consistently within a document for Abstract each distinct audience. An organization (a semantic community) has The Object Management Group standard “Se- speech communities (audience that shares terms) mantics of Business Vocabulary and Business that each use a given natural language and, typi- Rules” (SBVR) was designed to enable natural cally, at least three speech communities that use language sentences to be written so they can the same natural language, each with a distinct vo- be read unambiguously by business people, cabulary with its own preferred terms for the same and interpreted unambiguously in formal logic by computers. This paper discusses the key concepts: factors that need to be present in addition to • Employees: jargon, abbreviations, transac- SBVR to realize this SBVR design goal. tion codes, form numbers, etc. But much of the vocabulary would be in understandable 1 Introduction business language. It would usually be the most comprehensive vocabulary, providing Ambiguity in business communication, especially default terms for the others. in business governance documents, introduces • Legal, for contracts, product and service avoidable business risks. Sometimes these busi- specifications, compliance reporting, etc. ness risks are very costly, even catastrophic, to the The vocabulary would be formal, include organization involved. standard legal and industry terminology, and The key challenge is to remove ambiguity in be strictly policed. governance documents without business authors • Public, for advertisements, public-facing having to learn new grammar rules. web sites, scripts for helpdesks, etc. The vo- cabulary would be everyday language - and 2 Business Audience – Not IT Audience probably also be strictly policed. SBVR Terminological Dictionaries and Rule- There would probably also be smaller, special- books “document the meaning of terms and other ized speech communities, such as accountancy representations that business authors intend when and finance. Their vocabularies would usually be they use them in their business communications, drawn from the employees’ and legal vocabular- as evidenced in their written documentation, such ies, supplemented by terms adopted from their as contracts, product/service specifications, and practices. governance and regulatory compliance docu- 2.1 Different Terminological Dictionaries for ments.”1 Different Audiences SBVR “is conceptualized optimally for busi- ness people rather than automated processing. It is Each speech community within the business designed to be used for business purposes, inde- would have a terminological dictionary, in its own pendent of information systems designs.”2 language, that would be a view of the terminolog- A key aspect of natural language simplification ical database – a structured subset delivered as a is to choose one of the synonyms as the preferred

1 SBVR Clause 1.4 “Terminological Dictionaries and 2 SBVR Clause 1.2 “Applicability” Rulebooks” report, or the output from a canned query, or a live • Subject concept view via a custom interface. • Period of time In a given language within a given business: In terminological dictionaries the context • Each concept must have a preferred designa- within which the preferred term and its synonyms tion have exactly one meaning is explicitly stated in • Preferred designations may have synonyms: the terminological entry. • A synonym for a given concept in one ter- When authoring business documents, there are minological dictionary may be a preferred a number of techniques to make the context ex- term for that concept in another termino- plicit, thus minimizing the likelihood of linguistic logical dictionary. analysis engines getting it wrong: • A synonym might not be a preferred term • Including the intended audience (speech in any terminological dictionary – but community), the subject field(s), and docu- may be a synonym in more than one ter- ment applicability dates as document proper- minological dictionary. ties • Including a subject field and/or context con- 2.2 Terminological Dictionaries are for Peo- ple; Data Models are for IT Systems cept as metadata in the document’s outline headings Terminological dictionaries document the mean- • Noting the subject field and/or context con- ings intended by business authors for words and cept in a (xxxx, yyyy) notation after the word phrases they use in their business documents. or phrase. These documents are used by business people to operate the business. 3 Subset of Natural Language Grammar For example, ISO 1087-1_2000 Terminology – Not Artificial Grammar work - Vocabulary - Part 1: Theory and applica- tion defines the meaning of the terms used in ISO The approach this paper advocates is to use a se- 704:2009 Terminology work – Principles and lected subset of natural language grammar struc- methods. tures & terms defined in a terminological diction- Data Models and their data definitions docu- ary. It is not to define a new artificial language or ment the data maintained in IT systems. These artificial extensions to natural language. models are used by IT professionals for design of This means not changing the natural language IT systems. syntax of sentences in any way that requires busi- For example, ISO 30042 Systems to manage ness users to learn new syntax or different inter- terminology, knowledge, and content – TermBase pretations of syntax from what they already know eXchange (TBX) is a data model that documents or could know from natural language grammar. XML data structures for exchanging terminologi- 3.1 Keeping Natural Language Grammar cal database content. Natural Terminological dictionaries and data models are both important but serve very different audi- While there is a continuum from: ences and purposes. Neither is an adequate substi- 1. “sloppily, even wrongly, used natural lan- tute for the other. guage grammar” through 2. “good quality simple, plain natural 2.3 Importance of Context language” through (and across the bound- Dealing with homonyms is essential to removing ary to) ambiguity. At the heart of terminology science is 3. “additional artificial grammar having to be the principle that there is a one-to-one relation, in learned and remembered” through a given context, between a given word or phrase 4. a “fully formal language that looks as much and the concept that designates it. like natural language as possible” to (but is ISO TC 37 Terminology standards and SBVR deceiving like COBOL was) together can support several kinds of context for 5. “formal logic programming languages” disambiguating part of speech words and phrases: (like Prolog and Datalog), the transition from stage 2 to stage 3 is a clearly • Subject field identifiable boundary that, when crossed, moves • Part of speech from pure natural language to some form of arti- • Speech community ficial language. • Context concept (disambiguation context) Business people need to be able to use natural The document author is always the final author- language with the help of tools to express business ity for intended meaning, when linguistic analysis definitions and sentences unambiguously – with- can’t do the job correctly alone. out being required to learn something that is not An example of a sentence where a software tool part of natural language grammar. should ask for clarification is London Under- Of course, natural language grammar can be ground’s rule: supplemented with good practices for which sub- “Dogs must be carried on escalators”. sets of natural language grammar structures and This could be interpreted either as: patterns are least ambiguous. The “Plain English” “A person who is accompanied by a dog must requirement of the US Government is an example carry the dog when riding an escalator” of this.3 or as Once one crosses this boundary, the whole ap- “A person may ride an escalator only if the per- proach is on a slippery slope from making it easy son is carrying a dog” for business people to communicate unambigu- Compare this with “A hard hat must be worn ously to making it easy for IT developers. when visiting a contruction site”. If business people have to learn artificial gram- mar / syntax / notation, there is a shift of respon- 4 Unambiguous Words/Phrases sibility – and effort – for unambiguous communi- cation. Rather than business people working with Ambiguous words and phrases are one of the two good semantic authoring tools in their own natural major sources of ambiguity in business documen- language, they have to speak the language of IT tation. Removing ambiguity from part-of-speech professionals. The more that happens, the greater words and phrases is the focus of the discipline of the risk to the clarity of the documents that people terminology science. in the business use. Terminology work is standardized in the ISO TC 37 terminology standards with ISO 704:2000 3.2 “Plain Language” as Basis for Least Am- and ISO 1087-1 being the core standards. SBVR biguous Subset of Natural Language builds on the foundation of these standards and There is a large international “Plain Language” adds: community that is rich with good practice materi- • Semantic features to terminological diction- als, training, tools and practical experience that aries so that the definitions of concepts can support writing using plain language.4 5 6 7 be grounded in formal logic. The knowledge, know-how and involvement in • The ability to define the skeleton of a sen- unambiguous business document authoring of the tence clause; i.e. sentence clauses without Plain Language community fits exactly with the their quantifications -- typically “subject business audience of this approach. This is in verb object [preposition object]”. These sharp contrast to an audience of logicians and IT skeleton clauses are known as “verb concept professionals. wordings” in SBVR. In formal logic they are As such, the Plain Language community be- propositions with at least one variable (sub- comes both the starting point for, and context of ject or object) unquantified. use of, the work envisioned in this paper. 4.1 Importance of Defining Verbs / Verb 3.3 Using Interactive Software Tools to Re- Phrases and Prepositions move Ambiguity An important application of natural language is in Clearly, the best of linguistic analysis know-how formulating policies, rules and advice to guide the is needed to optimize resolving indirect references behaviour of organizations and the people in via pronouns, etc.; for determining context for dis- them. ambiguating parts of speech to a single meaning Verbs are the key, but they are often the poor in the terminological dictionary; and similar relations in terminology. Governance documents sources of ambiguity. all too often reveal definitions – almost all of nouns – and rules, with nothing connecting them but the assumption that use of the nouns around

3 http://www.plainlanguage.gov/whatisPL/ 6 http://plain2015.ie/ 4 http://www.plainlanguagenetwork.org/ 7 http://www.intelligentcontentconference.com/ 5 http://centerforplainlanguage.org/ the verbs in the rules will be commonly under- around 100 in English) but many prepositions stood. have several meanings. A single vocabulary for Verbs provide the infrastructure - they connect prepositions could be adopted into all terminolog- the nouns with the rules. Nouns have subject and ical dictionaries for a given natural language. object semantic roles with respect to verbs in sen- tence clauses. Nouns in these semantic roles de- 4.2 Importance of Defining Adjectives / Ad- note the roles played in the real world behaviour, jectival Phrases represented by the verb, by the real world things Characteristics play a very important role in both in the extensions of the concepts represented by ISO TC 37 and SBVR in removing ambiguity. the nouns. They are the meanings, in natural language gram- Verbs are modified - with ‘must’, ‘should’, mar, of adjectives and adjectival phrases – just as ‘may’ and their negations – to create rules and ad- general concepts are the meanings of common vice. For example, a car rental business’s termi- nouns. nology might include the SBVR verb concept Each concept is made up of a set of character- ‘credit card guarantees open rental’ (where an istics: its intension. Characteristics are qualifiers open rental is one for which the customer has pos- or conditions that narrow the scope of the exten- session of the car). sion of the concept. A ‘must’ modifier and quantifications added to Each intensional definition is composed of a su- a single verb concept would create a behavioral perordinate concept and one or more delimiting rule: ‘a credit card must guarantee each open characteristics. A set of essential (necessary and rental’. This is too general. Which credit card has sufficient) characteristics determines the concept. to guarantee which open rental? Other clauses can All other characteristics in the concept’s intension qualify the nouns to develop the practicable rule are implied from the set of essential characteris- the business needs: ‘An open rental must be guar- tics and the other concepts in the terminological anteed by a credit card that is in the name of the dictionary. The set of essential characteristics is customer who is responsible for the rental.’ the set of all the delimiting characteristics in defi- Structuring verbs into skeleton clauses (SBVR nitions all the way to the top of the superordinate verb concept wordings) allows software tools to concept hierarchy. report on coherence and completeness of bodies Sets of essential characteristics have an inter- of guidance – identifying rules that use undefined pretation in formal logic and map directly to “nec- verb concepts and verb concept wordings that use essary and sufficient conditions” in OWL. As undefined nouns. It also enables checking con- such they create a direct bridge from normal nat- sistency of use of verb concepts across guidance ural language intensional definitions to reasoning propositions. engine models. Another aspect of formalizing use of verbs is Sets of essential characteristics are key to re- managing different meanings of a verb phrase in moving ambiguity, not between words/phrases different contexts. For example, the rental car and meanings, but from the meaning of each con- company of the example above sells its cars at the cept. They also provide a means to determine ob- end of their useful rental life. In a rental context, jectively whether two intensional definitions are ‘car is handed over to customer’ means ‘the car is semantic equivalents for the same concept, or def- given to the customer for use for an agreed time initions of two different concepts. This capability and return to an agreed drop-off location’. In a comes from the semantic formulation of the defi- sales context it means ‘ownership of the car is nitions of the characteristics, which gives each transferred to the customer’. characteristic an unambiguous meaning (see next This could be handled by defining narrower section). categories of the concepts represented by the Characteristics are also powerful as they define nouns: ‘the rental car is handed over to the rental conditions which can be used in governance doc- customer’ and ‘the sold car is handed over to the umentation. The adjectives and adjectival phrases purchasing customer’. But the people in the busi- serve as condition names for the (usually) longer ness do not talk or write this way and should not definitions of the characteristic. be forced to change their vocabulary. They know what they mean within their context. Prepositions also have objects and are also part of skeleton clauses (verb concept wordings). There are a limited number of prepositions (only 5 Unambiguous Definitions & Sentences • Add no new syntax rules that would need to be learned by business people. The ability to write sentences in business docu- • A “Simplified Natural Language” version for ments and definitions in terminological dictionar- the natural language(s) to be used in business ies that are unambiguous both to business people documents, preferably US English as the first and in formal logic is the key overall capability one. The simplified natural languages should that SBVR adds to the ISO TC 37 Terminology be of the kind, and documented according to standards. the approach, specified in standard. SBVR defines a very abstract syntax for speci- • An authoring software tool that: 8 fying the logic structure (semantic formulation) • Takes text from business documents, of sentences and definitions that is semantically preferably as it is being written, equivalent to the natural language sentence or def- • Uses the proposed standard and the se- inition. mantic/logical layer of linguistic analysis SBVR Semantic Formulations were designed to: to be easily mappable to natural language gram- • Identify ambiguous grammar situations, mar. • Ask the author for clarification from sug- There are a number of cross-language natural gested options, language grammar metamodel standards or de- • Record all author decisions and/or com- facto standards that could be mapped to the SBVR puter decisions. Semantic Formulation metamodel, such as: • Uses the above proposed standard to gen- • ISO TC 37/SC 4 Linguistic Annotation erate SBVR semantic formulations from 9 standards the definitions and sentences. • Penn TreeBank10 and PropBank11 12 • Adoption and/or creation of terminological • NOOJ Text Annotation Structure (TAS) dictionaries whose concepts cover the con- Software tools that support these natural lan- tent of the documents to be authored. guage metamodels are increasingly being made available as low cost Cloud Services. 5.1 SBVR Semantic Formulations: Unambig- Serializations of these models for data inter- uous Formal Logic Sentence Equivalents change are usually specific to a given linguistic SBVR does not provide a logic language for re- analysis tool, but that is a concern for implement- stating business rules in some artificial language ers – not of the standard proposed in this paper. that business people don’t use. Instead, its seman- The SBVR approach to writing unambiguous tic formulations provide a means for describing natural language sentences and definitions in- the structure of the meaning of sentences as ex- cludes these components, in addition to existing pressed in the natural language that business peo- SBVR terminological dictionary and rulebook ple do use. tools: SBVR Semantic formulations are not represen- • A standard that specifies a cross-language tations or expressions of meaning. They represent approach to documenting a subset of natural the logical composition of meaning. They are used language grammar. This standard should: to specify the formal semantic structures underly- • Select a cross-language linguistic annota- ing business communications that comprise con- tion metamodel, cepts, propositions and questions. • Identify the subset of its cross-language There are two kinds of semantic formulation: grammar structures that can mapped to SBVR semantic formulations in a way • Logical formulations: they structure proposi- that leaves the fewest opportunities for tions, both simple and complex. There are ambiguity. specializations for various logical operations, • Provide a mapping of the chosen natural quantifications, atomic formulations based language grammar structures to SBVR se- on verb concepts and other formulations for mantic formulation constructs.

8 SBVR Clause 21 “Logical Formulation of Semantics” 11http://clear.colorado.edu/compsem/documents/prop- 9http://www.iso.org/iso/home/store/catalogue_tc/cata- bank_guidelines.pdf logue_tc_browse.htm?commid=297592&pub- 12https://hal.inria.fr/file/index/docid/498045/filename/Buda- lished=on&development=on pest_2008_Disambiguation_Tools_for_NooJ.pdf 10 http://www.seas.upenn.edu/~pdtb/PDTBAPI/pdtb-anno- tation-manual.pdf special purposes such as objectifications and which a semantic formulation at one level oper- nominalizations. ates on, applies a modality to, or quantifies over • Projections: they structure intensions as sets one or more semantic formulations at the next of things that satisfy constraints. Projections lower level. formulate definitions, aggregations, and Each kind of logical formulation, including questions. modal formulations, quantifications, and logical Semantic formulations are recursive. Several operations, can be embedded in other semantic kinds of semantic formulation embed other se- formulations to any depth and in almost any com- mantic formulations. Logic variables are intro- bination. duced by quantifications and projections so that Different semantic formulations are possible embedded formulations can refer to instances of for the same meaning. Two semantic formulations concepts. can be determined to have the same meaning ei- The following is a simple business rule - one ther by logical analysis or by assertion (as a matter rule, with one meaning, stated in different ways. of definition). Other statements are also possible Designations like ‘rental’ and ‘additional • It is obligatory that each rental has at most driver’ represent concepts. The semantic formula- three additional drivers. tions involve the concepts themselves, so identi- • A rental must not have more than three addi- fying the concept ‘rental’ by another designation tional drivers. (such as one from another language) does not • No rental may have more than three addi- change the formulation. tional drivers Semantic formulations are structures, identified Figure 1 is a representation of a semantic for- structurally as finite directed graphs. The refer- mulation of the rule, as sentences that convey the ence schemes for semantic formulations and their rule’s full structure. parts take into account their entire structure. In The rule is a proposition meant by an obligation for- some cases, a transitive closure of a reference mulation. scheme shows partial loops (partial in the sense . That obligation formulation embeds a universal that only a part of a reference scheme loops back, quantification. never all of it). . . The universal quantification introduces a first The main categories of semantic structure of variable. . . . The first variable ranges over the concept SBVR semantic formulations are: ‘rental’. • Variables and Bindings . . The universal quantification scopes over an at- • Quantifications most-n quantification. • Logical Operations . . . The at-most-n quantification has the maximum • Atomic Formulations cardinality 3. . . . The at-most-n quantification introduces a sec- • Instantiation Formulations ond variable. • Model Formulations . . . . The second variable ranges over the concept • Projecting Formulations ‘additional driver’. • Objectifications . . . The at-most-n quantification scopes over an • Nominalizations of Propositions and Ques- atomic formulation. tions . . . . The atomic formulation is based on the verb • Projections concept ‘rental has additional driver’...... The atomic formulation has a role binding. 5.2 Mapping to SBVR Semantic Formulations ...... The role binding is of the role ‘rental’ of the verb concept. to remove Remaining Ambiguity ...... The role binding binds to the first varia- The ISO TC 37 Linguistic Annotation standards ble. fall into these four main categories of annotation: . . . . . The atomic formulation has a second role binding. • Morpho-syntactic annotation ...... The second role binding is of the role ‘ad- • Linguistic annotation ditional driver’ of the verb concept. • Syntactic annotation ...... The second role binding binds to the sec- • Semantic (logical) annotation ond variable. Other linguistic annotation standards and de-facto Figure 1: Structure of a Semantic Formulation standards approximate these same categories. The indentation illustrates the composition of a semantic formulation: a hierarchical structure in Table 1 provides some examples of mappings “EU-Rent must appoint at least three officers” from Linguistic Annotation structures to SBVR 1. Word-form Annotation (ISO 24611) semantic formulation structures: DECL NP1 NOUN1* “EU-Rent” Natural Language Mapped to SBVR AUXP1 VERB1* “must” Annotation Fea- Metamodel Construct VERB2* “appoint” ture NP2 QUANP1 AVP1 ADV1* “at least” common noun part signifier of a general concept ADJ1* “three” of speech CHAR1 “.” NOUN2* “officers” common noun in placeholder in verb concept word- object/subject se- ing representing the general noun 2. Sentence Syntax Annotation (ISO 24615-1) mantic role concept playing a verb concept appoint1 (proposition, present tense) role in the verb concept repre- sented by the verb concept word- subject EU-Rent1 (proper name) ing object officer1 (indefinite, singular) proper noun part of signifier of an individual noun con- speech cept operator three1 (quantifier) proper noun in ob- quantifier for the general noun modal must1 intensifier at_least1 ject/subject seman- concept playing a verb concept tic role role in the verb concept being 3. Semantic Formulation (SBVR) used in a sentence clause it is obligatory that verb or verb phrase (ISO TBX term of partOfSpeech: exists 3..* v : ‘officer’ (part of speech) verb) verb phrase in rela- (part of) verb symbol that is part of ‘company appoints officer’ (‘EU-Rent’, v) tion to object/sub- verb concept wording that repre- ject semantic roles sents a verb concept Figure 2: Linguistic Analysis leading to SBVR preposition in rela- (part of) verb symbol that is part of Semantic Formulations tion to object/sub- verb concept wording that repre- ject semantic roles sents a verb concept Lévy and Nazarenko describe a software-sup- “each’, “at least universal quantification ported approach for building sets of business rules one”, “a given” from regulatory documents, developed by the La- “some” existential quantification “a”, “an” universal quantification or existen- boratoire d’Informatique de Paris-Nord (LIPN). It tial quantification depending on uses a three-step process, in which SBVR Struc- use tured English stands in an intermediate position “at least …n…” at-least-n quantification “must” obligation formulation between the natural language of the regulatory “always” necessity formulation documents and the formal language of the rules. “that” 1. when preceding a designa- This development originated in OntoRule, a tion for a noun concept, this is a binding to a variable (as with ‘the’). large-scale integrating project partially funded by 2. when after a designation for a the European Union's 7th Framework Pro- noun concept and before a desig- gramme. LIPN and Audi AG were two of the part- nation for a verb concept, this is used to ners, and developed the first version of the ap- introduce a restriction on things proach using the EU regulations for car safety sys- denoted by the previous designa- tion based on facts about them. tems (brakes, seat belts, air bags). Audi required 3. when followed by a proposi- the business rules as part of its compliance with tional statement, this is used to in- the regulations. troduce a nominalization of the proposition or an objectification, depending on 5.3 The Power of HTML 5 to Bring the Au- whether the expected result is a thor’s Meaning to Readers proposition or a state of affairs. Table 1: Mappings to SBVR Semantic HTML 5 enables a whole new level of semantic Formulation Structures markup of the text in business documents, ena- Since SBVR semantic formulations are recursive bling readers to know exactly what meanings the and provide features like objectification and nom- author intended. inalization, they are able to support the most com- HTML 5 semantic markup of part of speech plex sentences. words/phrases, in conjunction with the Unicode Figure 2 illustrates how the meaning of a rule character set, can support the following software can be structured into an SBVR semantic formu- features: lation using two stages of linguistic analysis: syn- • An HTML 5 ( … ) based tactic and semantic annotations. mark-up for SBVR Structured English text styles for common nouns, proper nouns, ad- jectives, verbs, prepositions, and SBVR key- words, both as single words and as phrases. This mark-up includes the definition, the meaning identifier, and all of the contexts re- As part of the generation of SBVR semantic quired for a unique connection between the formulations, these software tools can work inter- word/phrase and the meaning. actively with business authors while they are writ- • A corresponding MS Word style sheet. ing their documents. They can ask the authors for • An AutoComplete feature that inserts the se- clarifications, record them, and use them in the mantic markup behind the word/phrase from generation process. This enables the authors to the terminological dictionary into the docu- work with natural language grammar, as is, and ment. frees them from having to learn a new, artificial • Mouse-over tooltips in a document that show syntax to ensure unambiguous business govern- the concept definition for terms/names or the ance documents. statement for rule names, along with the con- The key components that still need to be devel- text in which the term/name is uniquely con- oped to make this scenario a reality are: nected to this concept or rule. 1. A standard that specifies a cross-language • Ability to choose which natural language the approach to documenting a simplified nat- definitions are displayed in. ural language that adds no new syntax rules • On-the-fly replacement, on simple refresh, in to be learned and that is as good as practical any screen, report or document of all uses of to be the basis for generating SBVR seman- a semantically marked-up term or name tic formulations. when the preferred term/name for that con- 2. A simplified natural language, preferably cept/rule is changed. English as the first one, of the kind speci- • On-the-fly redisplay of semantically marked- fied in item 1, documented according to the up terms/names in any screen, report or doc- approach in item 1. ument with the preferred term/name in the 3. A software tool that takes text from busi- language of the new Speech Community, ness documents, preferably as it is being when the Current Speech Community is written, and uses the semantic/logical layer changed, whether in the same or a different of linguistic analysis to clarify the meaning natural language. of ambiguous sentences and generate • Optional display of visual font styling for SBVR semantic formulations. words/phrases that have semantic markup. The driver is to reduce business risk by provid- • Validation of each definition or rule state- ing the people in a business with unambiguous ment with semantic markup and its SBVR governance documentation, in natural language, Semantic Formulation against each other. using familiar terminology. Requiring them to use These capabilities can support multilingual an artificial language designed to enable pro- speech communities. Most of them have already cessing by software will work against this. been implemented at least once. The language needs an underlying formality to enable bridging to software, but this ought to be 6 Conclusion ‘under the covers’, not visible to the business us- This paper proposes an approach to using poten- ers. tially any natural language grammar as a notation The technology, knowledge and know-how to for SBVR formally understood definitions and meet both requirements – natural language for the sentences. users, underlying formality for the software – al- The semantic formulation capability of SBVR ready exists, and practice is beginning to grow. provides the metamodel of storing and exchang- ing the semantic structure of the meaning of sen- Reference tences and definitions in a way that has an inter- [Lévy and Nazarenko] Formalization of Natural Lan- pretation in formal logic. guage Regulations through SBVR Structured Eng- Linguistic analysis software has now matured lish (http://link.springer.com/chapter/10.1007/978- to the point where the “semantic/logical” layer of 3-642-39617-5_5) linguistic analysis and annotation enables soft- [Ontorule] OntoRule Project (http://www.ontorule- ware tools to generate SBVR semantic formula- project.eu) tions from sentences in business documents. The increasing availability of linguistic analysis as [SBVR] Semantics of Business Vocabulary and Busi- ness Rules v1.3, Object Management Group low-cost cloud API services makes creating such (OMG), http://www.omg.org/spec/SBVR/1.3/ software tools increasingly feasible.