ITA Controlled English and its applications
David Mott1, Dave Braines1, Ping Xue2, Stephen Poteet2
1Emerging
2Boeing
Technology Services, IBM UK, Hursley, Winchester, UK
Research and Technology, The Boeing Company, Seattle, US
Abstract. This paper describes our research into ITA Controlled English (CE)
to represent knowledge and reasoning in different domains. A basic core of restricted English allowing expression of concepts, entities, and relationships has
been extended to express negation, assumptions, uncertainty values, and metalevel reasoning about concepts. We describe a reasoning capability based on CE
and provide examples of its application to cognitive tasks such as intelligence
analysis and fact extraction from Natural Language text, with provision of rationale to users for conclusions derived.
1
Introduction
We describe our research into a Controlled Natural Language (CNL), ITA
Controlled English (CE), supporting users in reasoning and representing knowledge
for collaborative problem solving and communication between human and machine in
a wide range of domains, including planning, allocation of resources, intelligence
analysis, fact extraction from Natural Language, and the provision of conversational
interfaces. The work is undertaken within the International Technology Alliance
(ITA) programme, a 10 year fundamental science research programme by US/UK
military research organisations, academic institutions and industry [1], to support the
collaborative operation of military coalitions. The work started from a dissatisfaction
with using OWL for representing knowledge of planning and intelligence analysis,
and we developed a more human understandable representation that was readable by
man and machine, ITA Controlled English. Our research expanded into more
cognitive aspects of problem solving, including more complex forms of reasoning,
use of rationale, and application of CE to language processing itself. We believe that
CE, although relatively simple compared to some other CNLs, is "curiously useful",
and we provide principles, examples and observations on the cognitive aspects of
using a CNL.
We have established some research principles to inform our work:
 "cognitive": to build applications that assist people in performing complex tasks,
requiring representation of knowledge, application of knowledge to perform
reasoning and analysis, and the explanation of the reasoning to others. We seek
ways for human and machine to interact in cognitive tasks.
 "linguistic": to use a representation that is sufficiently close to English for
sentences to be easily understood by native speakers without requiring detailed
training, but nevertheless providing formal precision allowing automated
reasoning; we require a syntax that is grounded in a logical semantic
representation, that will leverage human cognitive capabilities in using text-based
language for supporting communication and reasoning.
 "integrative": to use a representation applicable to many types of domain,
supporting different problem solving strategies and fusion of different types of
data; we seek a common means to represent domain models and data so that users
can rapidly construct new concepts to represent and analyse new information.
2
ITA Controlled English
This section defines the basic constructs of CE and extensions to this basic core;
see [2] for further details. We do not claim that CE is all powerful, and there are
obvious syntactic and semantic extensions that could be made; however our research
has focussed on how extensively and usefully such a language can be applied.
Nevertheless we require that all CE sentences be grammatically correct English and
have unambiguous semantics. In examples we present CE statements, and statement
fragments in italic font.
2.1
Basic CE
The basic aspects of CE were inspired by work by John Sowa [3]. We express
entities, attributes and relationships between two entities, in a syntactic form and with
an underlying basis in logic. Linguistic expressions for these structures are defined by
the user in a CE "conceptual model" as described below. At this point in the paper we
rely upon the reader's intuition to understand the CE expressions given.
An entity is defined by a simple noun phrase comprising a unique name and a
conceptual type, representing the existence of an individual that belongs to that
concept; the person John means that there is an individual in the domain named John
that belongs to the person concept. An entity may also belong to more than one
concept, for example the person John is a man.
A relationship is defined by a phrase with a "subject", an "object" and a "verb"
expression; the person John is married to the person Jane means there is an is
married to relationship between the individuals. Logically 'is married to' is defined as
a set of pairs of related individuals. There are no restrictions on words that express
the relationship, as long as the sentences are grammatically correct; we could define
the person John is not married to the person Mary. This is treated as a string of words
and does not implicitly define any semantic knowedge (e.g. opposition of is married
to and is not married to) though a user could define this with logical inference rules.
An entity attribute is defined by a phrase with the verb "has", an attribute name,
and an attribute value, e.g. the person John has the person Jane as sister. Logically
sister is defined as a set of pairs of related entity and attribute values. The user may
choose whether to conceptualise a relationship as an attribute or a relation.
2.2
Conceptual Model
The user can create linguistic expressions and rules to define concepts and their
semantics in a particular domain by a CE "conceptual model". Concepts are defined
"by example" through conceptualise statements, where tildes surround new terms. e.g:
conceptualise a ~ person ~ P that is a volitional agent and has the person P1
as ~ sister ~ and ~ is married to ~ the person P2.
We hold that the semantics of a concept is defined by the relationships that occur
between different concepts and by the set of inferences that may be made about the
entities. Relationships are defined by conceptualise statements and inferences are
defined by explicit rules in the conceptual model and by some implicit inferences,
such as sub-classing, where relationships and attributes are inherited by sub-concepts
A rule is a logical relationship between premises and conclusions; if the premises
are true then the conclusions are true; variables may also be included in the premises
and conclusions, bound by matching the premises. However, the inverse inference is
not implicitly executed (negation of the conclusion does not cause inference of the
negation of the premises). In addition, as extended below, the nature of "truth" is
more complex than just existing in a global "factbase". For example, this rule
specifies the symmetry of the relationship 'is married to':
if ( the person X is married to the person Y )
then ( the person Y is married to the person X ).
and this rule relates the type of a person to their familiy relationship:
if ( the person X is the sister of the person Y )
then ( the person X is a woman ).
2.3
Meta Model
We make extensive use of a meta model, i.e. meta-level concepts and relationships
between the concepts themselves. Each basic construct has an equivalent metastatement. For example the existence of the concept "person", in conceptual model
"m1" is stated as "the conceptual model m1 contains the entity concept 'person'."
Subsumption between relations may be stated as "the relation concept 'is located in' is
subsumed by the relation concept 'is contained in'." The meta model can represent
structural information about the conceptual model that is not yet available in the basic
CE conceptualise syntax, e.g. cardinality of attributes, and even second order rules for
reasoning about the concepts. The meta-model is also extensible by users of CE.
Application of rules to infer new CE statements is recorded in a rationale graph, an
inference from premises to conclusions being held as a "reasoning step". This defines
the support for each proposition in the factbase, as a chain of reasoning steps from
initial premises to the proposition. Reasoning steps may be expressed by "because"
between CE statements: ."the person John is married to the person Mary because the
person Mary is married to the person John". We are researching the presentation of
rationale to the user, allowing them to understand and review sources of uncertainty,
assumptions made or rules applied in the derivation of information, or to challenge
reasoning by argumentation [4]. Representing rationale as CE allows collaborating
reasoning systems to build up an integrated view of the reasoning [4].
Our research [5] uses mappings between words and concepts, with meta-level
reasoning that transforms natural language (NL) sentences to CE facts. Knowing that
"the word "|cat|" expresses the entity concept feline" we can build rules to
conceptualise things described by the word "cat" as "feline", using meta-syntax such
as "the thing T realises the entity concept EC" where EC is a variable holding a
concept (e.g. feline, this being equivalent to "the thing T is a feline"). The concept EC
assigned to T can thus be determined dynamically.
The meta model is used to embed CE sentences inside other CE structures, by use
of "statement that". For example we can reify a logical inference as a CE structure
with premise and conclusion, where these are defined as sets of CE sentences:
the logical inference #1 has the statement that ( the person X is married to the
person Y ) as premise and has the statement that ( the person Y is married to the
person X ) as conclusion.
This allows rules to construct other rules by inferring "logical inference"
structures, permitting axiom schemata that convert facts about concepts into rules
about individuals that are instances of these concepts. Meta-level constructs are very
useful for linguistic processing, as well as for designing generic rules reuseable across
different domains, representing generic "theories" about the nature of the reasoning.
2.4
Extensions to the base core CE
Basic CE allows definition of simple propositions about entities and their
relationships. We have extended CE syntax and semantics to support more advanced
reasoning, by providing information about the truth condition of a proposition. For
example, to specify that a proposition is false we may write it is false that the person
John is married to the person Anne, and we maintain a "negative factbase" to record
negated propositions. If a proposition is both "true" and "false", an "inconsistency" is
recorded which may affect the reasoning, e.g. to determine invalid assumptions (see
below). Users may write rules to assert false propositions, for example:
if ( the person X is not married to the person Y )
then ( it is false that the person X is married to the person Y ).
A negated proposition "it is false that X" may be a premise of a rule, but there are
two interpretations: the premise is "true" when "it is false that X" has explicitly been
asserted (X is in the negative factbase); the premise is true when it is NOT the case
that "X" has explictly been asserted (X is not in the factbase). These correspond to
classical negation and negation as failure, respectively. In constructing reasoning
systems, we sometimes wish to hold an "open world assumption" (absence of X does
not imply it is false that X), and sometimes a "closed world assumption" (absence of
X implies it is false that X) [6]. We have constructed systems that use negated
propositions as premises, but this has required a rule-ordering mechanism, and we are
currently developing a solution with different syntaxes for the two interpretations
together with an option for users to "close the world" on specific sets of propositions.
Negation may also be handled (without negated premises) by specifying conditions
under which inconsistencies may be inferred, and then using the inconsistencies, for
example to rule out invalid assumptions. A rule to infer an inconsistency is:
if ( the person X is not married to the person Y ) and
( the person X is married to the person Y )
then ( there is an inconsistency named I ).
CE supports assumption-based reasoning to explore and rule out alternative
hypotheses about the interpretation of information [7]. A user, or system, may state
that a proposition (an "assumed proposition") is supported by an assumption: when
the assumption is made the proposition is true; when the assumption is unmade the
proposition is not true (unless there is alternative support for its truth). Making an
assumed proposition true allows rules to infer the consequences. Unmaking an
assumption removes support from the assumed proposition and from all of its
consequences, leading to their being untrue unless they have alternative support. If an
inconsistency supported by assumptions is detected then these assumptions are
incompatible, and at least one must be unmade, making untrue all dependent facts
(that do not have alternative support). Whilst our reasoning steps are monotonic and
never retracted, we handle propositions that may have to be "retracted" by supporting
them with assumptions, which can later be unmade. An assumption is created in CE
by an "assumed that" syntax, such as: it is assumed by the agent dm that the person
John is married to the person Sophie, which also records the source of the assumption
(for knowing who can unmake assumptions). These may be made by users or
included as a rule premise allowing rules to construct assumptions dynamically.
Using assumptions requires a more complex notion of "truth", specifying
circumstances under which a proposition is true or false. We visualise this as a "truth
boxe" containing a "factbase" and a "negative factbase" of propositions, with a label
for the circumstances, such as "universal", "under assumption 27", "believed by
John". A truth box must not contain an inconsistency so may be used to partition
different sets of assumptions that are not necessarily consistent with each other.
We are exploring the use of assumptions to handle uncertainty values, where a
source of uncertainty for a proposition may be represented as an assumption (the one
needed to "believe" the proposition) together with associated numerical certainty
values., using syntax such as it is certain to degree D that X.
The rationale graph is used to calculate support for all of the propositions in a
particular truth box leading from the set of propositions that are initially true in that
truthbox. Since positive and negative versions of a proposition are independently
maintained, it is possible to determine whether a proposition has true support, false
support or neither (i.e its truth is unknown).
3
Examples
We have used CE to model reasoning in a number of different domains:
 Planning [8]: the task 'build bridge' occurs after the task 'destroy enemy'.
 Design of components: the oscillator o1 connects to the filter f1.
 Crime reporting: the antisocial behaviour #1 occurs during the month 2013-10 and
falls within the jurisdiction of the police force 'Hampshire Constabulary'.
 Sensor Mission matching [9]: the task task_1 requires the intelligence capability
detect and is looking for the detectable thing 'wheeled vehicle' and operates in the
spatial area Sector3.
 NL processing [5]: the word "|cat|" expresses the concept feline.
 Intelligence Analysis [10]: the agent Lion cannot work with the group Azure.
 Personal Digital Assistant the ibm person 'John Smith' has the email address
'js@ibm.com' as email address and has the blog 'jsblog' as online presence.
3.1
NL processing and Intelligence Analysis in the ELICIT framework
As well as using CE to perform analytic inference, we are researching into the use
of CE for fact extraction from Natural Language text: 1) as the target of fact
extraction (for further inference), 2) for configuring and guiding the Natural
Language processing, and 3) to explain the inferences generated from the extracted
facts in terms of the linguistic processing used [5].
We use the framework devised by the ELICIT laboratory for studying
collaborative decison-making [11], which requires groups of "players" to identify the
"who, what, where and when" of a terrorist attack from information contained in a set
of NL sentences. We aim to convert the NL sentences into CE facts and to
automatically reason with these facts in order to perform the identification task, in a
manner that is consistent with human reasoning. We describe these steps in reverse.
We first construct a conceptual model of the domain by manually extending
existing models of military situations [8] to include concepts found in the sentences,
(e.g. agents, targets). To explore reasoning, we initially extracted a set of CE facts
from the ELICIT sentences by hand, using the conceptual model [10], leading to CE
sentences such as: 1) the operative Lion does not operate in the country Chiland, and
2) there is a financial institution named TaulandFI that is owned by the country Tauland and is located in the country Tauland.
Some ELICIT sentences do not state facts, but rules of "habitual behaviour" such
as "the Lion will not risk working with locals", which we converted into CE rules:
if ( there is an agent named Lion ) and
( the group G is a group using locals )
then ( the agent Lion cannot work with the group G ).
Some sentences contained terms that required interpretation, such as "coalition
partners" and we recorded our interpretations (e.g. that a coalition partner is one who
hosts your embassy) in the rules (in this case called "coalition_embassy"):
if ( there is an embassy E that is owned by the country OC and
is located in the country HC ) and
(the interpretation coalitionembassy can be made by the rule coalition_embassy)
then ( the country OC is in coalition with the country HC ).
together with an assumption: it is assumed by the agent dm that the interpretation
coalitionembassy can be made by the rule coalition_embassy. Should inferences from
this interpretation lead to an inconsistency then the assumption may be unmade, lead-
ing to the retraction of all of its consequences. Via rationale, the user may be made
aware of this assumption, review its effects and consider consequences of its removal.
A problem solving strategy was defined using CE rules, based upon the intuition
that the problem is constraint-based, where identification of the "who" and "what"
involves elimination of inconsistent candidates. The rules lead to a solution, with
justification for each inferred fact. For example, the "who" is stated by the attack
situation elicitsituation involves the operative 'Lion' and involves the group 'VioletGroup'. Reasoning that eliminated the Gold group as a "who" is given below,
where rows are facts, the first three rows are premises, and a column represents a rule
linking premises (light/salmon) with a conclusion (dark/red). The proof is based
around the fact that the Lion cannot work with the group and the Lion is involved.
Our second aim is to extract precise CE facts from the NL sentences automatically.
However the sentences exhibit semantic ambiguities that require "common-sense" to
disambiguate [12], so we have initially converted the sentences into simpler English.
Our NL processing is based on the DELPH-IN English Resource Grammar (ERG)
[13] a high-precision grammar for English, the PET parser [14], and Minimal
Recursion Semantics [15] to express sentence semantics as logical predicates on
entities mentioned in the sentence. Our research focuses on transforming this
linguistically-focussed semantics into domain semantics based on the CE conceptual
model, resulting in CE facts. The MRS is itself represented as CE sentences
describing the predicates and arguments at a low level together with their rationale.
The sentence "The Lion is involved" generates MRS predicates that represent a situation where a thing x27 (the Lion) is "involved", expressed as "the mrs elementary
predication #ep2 is an instance of the mrs predicate '_involved_a_1_rel' and has the
situation e3 as zeroth argument and has the thing x7 as first argument". To view all
of the MRS predicates, a table is generated where rows represent MRS predicates and
their arguments ("involved_a_1_rel") and columns represent entities (Lion):
We convert MRS into a general model based on "situation" and roles of entities;
thus "The Lion works with the Azuregroup" is turned into the situation #1 has the
agent Lion as first role and has the agent Azuregroup as second role. Given the metamapping "the mrs predicate '_works_v_n_rel' expresses the situation concept
'operating situation'", we can map a generic situation into an "operating situation":
if ( the mrs elementary predication EP is an instance of the mrs predicate MRS and
has the situation S as zeroth argument and has the thing T as first argument ) and
( the mrs predicate MRS expresses the situation concept C )
then (the situation S has the thing T as first role and realises the situation concept C).
where "situation concept" represents a type of situation,
Additional CE rules handle prepositional structures and negations. Once a situation
is created, rules can generate more readable CE, such as the operative Lion works
with the group Azuregroup, mapped in part by the following rule:
if ( the operating situation S has the agent T1 as first role and
has the agent T2 as second role )
then ( the agent T1 works with the agent T2 ).
Our research focuses on how a domain model can assist the transformation of MRS
linguistic semantics to domain semantics, and how assumption-based reasoning can
extend NL techniques, e.g. into resolving ambiguities [16].
Our approach to the extraction of rules from sentences involves CE meta-language
to transform situations into rules. In the sentence "the Lion only works with the
Azuregroup", rules create CE sentences, not in the form of the underlying proposition
(that the Lion works with the Azuregroup) but as a "logical inference" by negating the
proposition (given the relation concept 'cannot work with' is the opposite to the
relation concept 'works with') that interprets the sentence as:
if ( the agent A is different from the agent Azuregroup )
then ( the agent Lion cannot work with the group A ).
Thus our NL processing occurs in a number of steps, a deep parse of the sentence
(the ERG), extraction of linguistically-based semantics (MRS), mapping of MRS into
generic semantics (situations and roles), and then into domain specific semantics,
based upon the CE domain model. CE helps us to quickly build a reasoning system
for the ELICIT domain, to explore the making of different assumptions, and also to
gain insight in the transformation of linguistic semantics into domain semantics.
3.2
Context Aware Personal Environment (CAPE) a personal digital assistant
The CAPE application works in the domain of personal electronic information including email, calendar, instant message history, twitter, browser history, profile,
photos, files and academic publications. We aim to create a dynamic CE-based environment where casual users could create their own high-level set of concepts to which
these low-level data sources could be easily integrated, and devise ways to analyse the
information by application of rules. CAPE uses a "CE Store" [17] providing a factbase and inferencing with agents that automatically convert data sources into CE
facts, based upon a conceptual model of the data. Reasoning is required to convert
between concepts in the data sources and the user's model of higher-level concepts
(e.g. people, projects, organisations and tasks), and to infer new high-level information via the user's CE rules. For example the following rule analyses a person's
email address (looking for a specific marker in the email) and infers the organisation
to which the person belongs:
if ( there is an organisation named O that
has the value EM as email address marker ) and
( there is an email address named EA ) and ( the value EA contains EM ) and
( there is a person named P that has the email address EA as email address )
then ( the person P works for the organisation O ).
CAPE uses small pieces of information and simple rules in a human-friendly way
to enable the end user to build a complex model in a domain of interest to them. We
also modeled "goals" in CE, which were used to drive the user interface dynamically,
triggering presentation of CE "templates" that could be completed by the user with the
relevant information. The power of the system lies in the fact that the higher-level
model is entirely within the control of the domain user so if they wish to change or
extend it they can do so just by writing CE sentences, leading to new conceptualisations, inferences, and changes to the user interface. In our experiments we found it to
be a powerful fusion of automated information extraction (using agents), semiautomated information linkage (through rules) and “local knowledge capture” from
the user for information not stored or easily extracted from an existing system.
4
What we have learnt in using CE
We offer some observations about what we have learnt which may be applicable to
CNLs in general; we do not claim that these are scientifically validated. CE seems to
be "curiously useful". Some projects are finding CE useful, though benefits are
somewhat difficult to quantify and explain, where human "conceptual thinking" is
required (e.g. hypothetical reasoning), where man and machine collaborate in reasoning by exchanging information in a common language, where explanation must be
provided, where the domain model must be easily changed by users, and where multiple sources of information must be integrated. Potential benefits seem rooted in cognitive aspects of reasoning, problem solving and communication.
Taking time to define how a sentence reads, in the syntax for entities, attributes and
relations, is key to domain conceptualisation; if it reads right then it probably is right.
If CE is correctly designed then sentences can flow in parallel to the "thoughts in the
mind", with a feeling of a communication with the machine. It is not always possible
to achieve this, due to limitations in CE syntax or when working with data such as
points on a map, but we still aim to conceptualise the data in CE to allow formal representation, integrated inference, and sharing of conceptual models.
CE seems to facilitate thinking about how a problem may be represented and
solved, by improving understanding of logical linkages between concepts. More complex logics such as assumption-based reasoning, allow the user to imagine more sophisticated problem solving strategies. Use of rationale offers new opportunities for
collaboration between man and machine.
We feel there is link between conceptualisation and linguistic understanding. In using CE we try to "lift" concepts out of the community's use of words, in the spirit of
Ordinary Language Philosophy [18]; though analysis may be required to add precision and avoid inconsistency. The relationship between language and cognition is
brought out when we undertake NL processing guided by CE, forcing us to consider
philosophical issues such as identity vs description, and how linguistic structures
encode situations, and this informs our understanding of how CNLs could operate.
However, there is much to do to improve CE's syntax and semantics, to provide
tools, and to apply CE to further tasks. We continue this work in the ITA programme.
This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence
and was accomplished under Agreement Number W911NF-06-3-0001. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official
policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the
U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to
reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
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