The AQUA Question Answering System uses two separate ontologically based systems in its operation. The first system, a knowledge-based information extraction system, derives the content from text documents (and queries) and converts them into an internal text meaning representation form (TMR). The second ontologically based system is the answer formulation unit, which maintains a separate ontology in a different form from the first. Answers produced by the answer formulation system are in Knowledge Interchange Format (KIF).

1. The AQUA Question-Answering System:
Bi-Directional Dynamic Semantic Alignment
in a Multiple-Ontology Environment
Maureen Caudill
SAIC
Enterprise Information Systems Division
10260 Campus Point Drive, MS C-2
San Diego, CA 92121
Barbara Starr
SAIC
Enterprise Information Systems Division
10260 Campus Point Drive, MS C-2
San Diego, CA 92121
Teresa Buss
SAIC
Enterprise Information Systems Division
10260 Campus Point Drive, MS C-2
San Diego, CA 92121
ABSTRACT the solution do not determine the nature of the semantic
alignment process.
The AQUA Question Answering System uses two
separate ontologically based systems in its operation. Keywords: Semantic Alignment, Knowledge-Based
The first system, a knowledge-based information System Development, Question-Answering Systems,
extraction system, derives the content from text Event extraction, Event understanding, Automatic Event
documents (and queries) and converts them into an Extraction.
internal text meaning representation form (TMR). The
second ontologically based system is the answer 1. BACKGROUND
formulation unit, which maintains a separate ontology in
a different form from the first. Answers produced by the The AQUA Question Answering System is based on two
answer formulation system are in Knowledge separate ontologically based systems using
Interchange Format (KIF). These answers must be independently developed ontologies. The process of
converted back into the text meaning representation ontological development is developer-specific in that it
where they can be translated by an answer generation depends strongly on the perspective or view of the
unit into natural language responses to the user query. person or persons developing the ontology. This means
The key technical challenge of this system is bi- that two ontologies developed by different people
directionally translating between the two ontologies independently of each other are unlikely to have direct
when each is dynamically changing in the course of correspondences.
operations. This paper presents a description of our
technical solution to this challenging problem. Yet at the same time, ontologies reflect the reality of the
Furthermore, the approach we have taken is one that can world. Thus there typically must be some ontological
be extended to other ontologically based systems, or to term that reflects basic concepts such as “seeing,”
systems with more than two ontologies. It is generic in “saying,” “feeling,” “eating,” and so on. The challenge
its approach, in that the specific ontologies involved in is to find ways of aligning these multiple world views
into a single method—without requiring changes in

2. either ontology in the system. The system itself must be based on a narrow-but-deep ontology concept. Figure 1
able to adapt to the ontologies used in its subparts. illustrates how this process works.
In developing the AQUA system, we have tried to A set of documents relevant to a specific knowledge
achieve exactly this type of semantic alignment in a domain is collected. Each of these documents is
flexible, semi-automated way that enables independent processed by the information extraction system, which
development of the text extraction unit, the reasoner unit, generates an intermediate, Interlingua representation of
and the answer generation unit. the knowledge. This extraction requires the use of the
first ontology in the system.
In the sections that follow, we describe the dynamic
semantic alignment system developed for AQUA. First, As with other ontologically based systems, the
the conversion of text data sources into knowledge will “ontology” actually consists of three interconnected sets
be described; next, the question-answering process will of definitions. The first is the conceptual ontology—the
be presented to illustrate the operation of the dynamic class structure—of the concepts known to the system.
semantic alignment system; finally, the architecture of For example, a “sedan” is a subclass of the ontological
the dynamic semantic alignment system will be concept “automobile” and so on. The second set of
presented, along with commentary on how it can be constructs is the lexicon—the specific words that are
extended for use with more than two ontologies. understood by the system and classified as lexical terms
in the ontological system. Thus, “Ford Taurus” might be
2. CONVERTING TEXT INTO KNOWLEDGE a lexical term that is an instance of the class “sedan.”
Finally, the system has relations that can be used to
A knowledge-based question-answering system such as define the linkages between two or more lexical
AQUA bases its answers on the knowledge contained constructs. Thus the relation “owned-by” connects the
within its knowledge bases. These answers are lexical terms “Ford-Taurus” and “Tom,” and would be
developed by encoding the contents of text documents derived from a sentence such as Tom owns a Ford
and other sources into knowledge representations that Taurus.” To correctly translate between two ontological
can be used to infer answers for queries. Thus, before systems requires semantically aligning all three of these
any query can be answered, the knowledge bases must be types of constructs: ontological classes, lexical terms,
populated with significant, relevant data. This process is and lexical relations.
commonly identified as an “information extraction”
process, though in fact it is much more complex than The text-to-knowledge conversion process first considers
simply extracting nouns and verbs from sentences. the relations established by the information extraction
system. Within those relations, slots are filled by various
In the case of AQUA, we make use of a sophisticated lexical terms that are instances of various ontological
knowledge-based information extraction system that is classes. Each term must independently be considered
and translated into the corresponding term in the other
Ontology
Translator
Clkjv. Andl a
Hlskdj LSk
(defobject EVENT
LSd lsd
Sdll ald sldkj (instance-of EVENT attack)
Lskdj aslkd (performed-by EVENT I ran)
Dskf aldjf (is-defender EVENT Iraq)
Df;la;akfj
…
Large Number (takes-place-when EVENT
Of Possible Select Relevant Retrieve Data (month May) (year 1993))
:documentation “CI A World
Data Sources Data Sources From Sources Fact Book”)
Convert Data
I nto Knowledge
(defobject EVENT
(instance-of EVENT attack)
(performed-by EVENT I ran)
(is-defender EVENT Iraq)
…
Domain
(takes-place-when EVENT Knowledge
(month May) (year 1993))
:documentation “CI A World KBs
Fact Book”)
Knowledge
KI F Stored into
OKS Knowledge
I nterface Knowledge Bases
Representations
Figur e 1 . Con ver t i ng t ex t int o k n ow ledge

3. ontology. If no such lexical term exists, one must be processor translates the query into an Interlingua form,
created for it in the other ontological class structure. which is then translated into KIF. The KIF query is
This process requires considering the ontological presented to the Answer Formulation system, which
inheritance of the term in the first ontology, and infers the correct answer based on the knowledge content
comparing that to the ontological class structure in the of the documents it has processed combined with the
second ontology. When the most specific possible match world-knowledge contained within its knowledge bases.
is found, that becomes the basis for generating a new That answer is provided in KIF, which is then translated
lexical term for the missing item. More details of this back into the Interlingua form. The Interlingua response
knowledge conversion process are presented in the subsequently goes to an answer generation system that
companion paper to this one, [12] “Event Templates for generates a natural language reply to the original
Improved Narrative Understanding in Question question.
Answering Systems,” also presented in these
proceedings. This query response process thus requires two
translations, one from the Interlingua representation to
Once the information extracted from the documents has KIF (the query) and one from KIF to the Interlingua
been translated into the new ontological system and representation (the answer).
stored as knowledge representations, the system is ready
to answer questions about the documents it has As with translating between natural languages, reversing
processed. the direction of the translation process (i.e., KIF to
Interlingua or Interlingua to KIF) is not as simple as
3. PROCESSING A QUERY merely substituting the reverse concepts. The answer to
a complex query may easily contain concepts that do not
Query processing in AQUA is performed according to appear in any previously processed document or query.
the dataflow shown in Figure 2. The query processing To complete these translations accurately, it is necessary
requires two translations: The first is the translation of to separately map Interlingua  KIF and KIF 
the query from the information extraction system’s Interlingua translations. Also, each of these ontological
Interlingua to KIF, while the second is the translation of systems are dynamic and mutable. Thus, the process of
the answer from KIF into Interlingua (from which, in a mapping between them must be similarly dynamic.
separate process, it will be converted into a natural
language response to the user). A user enters a natural The following section describes the dynamic semantic
language query. alignment system.
4. DYNAMIC SEMANTIC ALIGNMENT SYSTEM
The user begins the question-answering operation by
entering a natural language question. The query
I nterlingua
NL Query
Query
Query I nterlingua  KI F
Processor Translation
Answer
KI F
Query
NL
Answer
I nterlingua
KI F Answer
Answer
Answer Generation KI F  I nterlingua
Translation Answer Formulation
System System
Figu r e 2 . Pr ocessin g a Quer y

4. The dynamic semantic alignment system uses databases encountered allows the system to quickly identify what
to keep track of the correct translations across ontologies. documents are relevant to a specific query using that
Five key databases are used in this process, illustrated in term. For example, if a document defines a term “us-
Figure 3. special-forces-in-philippines” and a later query asks
about the purpose of having U.S. Special Forces troops
The first database, the Ontology Definition database, is in the Philippines, it will be easy to identify exactly
one that keeps track of which ontological systems are which document is relevant to that query. This
known. It contains key information about the ontology significantly reduces the burden on the inferencing
tables and other data needed to access the ontological system by providing automatic dynamic partitioning of
information. the knowledge base.
Each of the known ontologies are stored separately in The dynamic semantic alignment process consists of a
corresponding Ontology Databases, which are multi-stage comparison between the two ontological
partitioned into ontological classes, lexical terms, and systems. A step through this process will illuminate the
relations. Other data residing in the Ontology Databases methodology. The actions in the example below assume
include the immediate parent class of the term, and the that the semantic alignment process has just begun and
documentation for those terms. This information is the system has few mappings between ontologies to
critical since processing new documents will often work with.
generate new lexical instances. When new terms are
added to the lexicon (or to the ontology), they are When a term must be translated from Interlingua to KIF,
annotated with the document identification number in the first step is to look up the term in the Interlingua-to-
which the terms were first encountered. See [12] for KIF Ontology Mapping table. If it has previously been
several examples of lexical terms defined in the process translated, the results of that translation are stored in this
of representing the knowledge of a document table. For example, suppose the term is “speech-act” in
Interlingua. What is the corresponding term in KIF for
The key translation database is the Ontology Mapping that concept?
database. As mappings from one ontological system to
another are discovered, they are stored here. This allows If we assume that the Interlingua term has never before
much more efficient processing of the same term if it is been encountered, the first step is to consider the class
encountered again. structure for “speech-act” in the Interlingua ontology.
Since each ontological term includes information on its
The final database is the Processed Documents database. immediate parent(s), this is easy to look up in the
This keeps track of those documents that have been Interlingua ontology tables. The parent of “speech-act”
translated from one system to another. This database might be, for example, “communication-act.”
also keeps track of what new terms, if any, are defined in
each document. While not essential for the translation Again, these are compared to the mapping tables to see if
process, keeping track of where new terms are a correlation has been discovered between
Defines the ontologies that the
Ontology
dynamic semantic alignment
Definition
system knows about.
Tables
Ontology Ontology I nclude separate tables for
Tables Tables ontological classes, lexical terms,
I nterlingua KI F and relations.
Contains mappings from Maintains information
one ontology to the other; about which lexical
Ontology Processed
mappings are not and ontological terms
Mapping Document
necessarily bi-directional. were defined in which
Tables Tables
documents.
Figu r e 3 . Dyn am i c Sem an t ic Ali gn m en t Syst em

5. “communication-act” and some concept in the KIF semantic alignment system will generate a new lexical
ontology. This comparison might discover a link term based on the definition of the Interlingua term. The
between the Interlingua “communication-act” and the term will be an instance of some other ontological class,
KIF “communicating” action. This implies that the and a mapping will first be made between the parental
proper link to KIF would be some subclass or instance of ontological class in Interlingua and KIF. Once that
“communicating.” Under “communicating” there are mapping is ascertained, a new lexical entry
several possible correlations, including one of “saying.” corresponding to the Interlingua entry will be made and
An alternative discovery may also identify the correct the mapping table updated to reflect that new entry. This
linkage as an “inform-communication-act.” also updates the lexical tables for KIF, as well as the
processed document tables to note that this new term has
If there are no mappings discovered after backtracking been defined in this file.
through the inheritance tree for a specified number of
generations, or until a specific ontological level is Because of the generic methods used to identify
achieved, a message is flagged to a knowledge engineer, mappings, it would be entirely possible to implement a
who must enter an appropriate mapping by hand. This third mapping system if it were deemed appropriate to do
permits oversight of the ontological mapping method, so. For example, if the answer generation system used a
and need be done only once for any specified mapping. separate ontology from the answer formulation system
and from the information extraction system, the dynamic
While this is a semi-automated process, allowing semantic alignment system could easily be extended to
knowledge engineers to oversee the mapping process handle that set of translations.
ensures that the correct mappings are made at
fundamental levels. Preliminary mappings can also be The system could equally be extended to do mappings to
accomplished in a basic batch mode when the ontologies more than one other ontological system at any stage of
are first entered. It is not at all necessary to map every the effort. This would permit, for example, a direct
concept to every other concept—only the most basic comparison between the answers produced by two or
concepts and actions need be mapped by hand to give the more separate answer formulation systems, or a
system a head start in mapping between ontologies. comparison of the quality of the natural language
answers generated by two or more answer generation
In this example, two potential mappings exist, one to systems.
“saying” and one to “inform-speech-act.” Both mappings
would be entered into the mapping table, thus ensuring 5. CONCLUSIONS
that as little information is lost as possible from the
original document. This generates a mapping table entry The semi-automated dynamic semantic alignment system
that maps “speech-act”  ”saying” and “speech-act”  provides a methodology for translating between two
”inform-speech-act.” (In this instance, it is highly likely ontologies in a highly consistent manner. Although
that the “inform-speech-act” mapping would be knowledge engineer input is required when the system is
automatically discovered by the system without human first brought on line, fewer and fewer instances must be
intervention. Assuming no mappings exist between hand-mapped as corresponding concepts are discovered
parent ontological entries, discovery of the “saying” between the systems. In addition, the system can process
mapping is less likely without intervention.) The result changes to the ontologies because new terms and classes
of this in terms of converting the Interlingua to a are noted whenever the ontological system changes.
knowledge representation would be a double definition: This in turn erases any previous mappings so that the
new mapping process can begin again. A separate
(defobject Event-E1 mapping tool permits knowledge engineers to be
(instance-of Event-E1 inform-speech-act) apprised of any changes to the ontologies, and be
(instance-of Event-E1 saying)) presented with a list of previously existing mappings for
those terms, if any, for editorial review and alteration.
The double definition has the advantage that if a query That allows the knowledge engineer to determine if
arrives about this event and it is phrased in a manner that changes to those mappings are appropriate, or if the
it is mapped only to “inform-speech-act,” this event will ontological changes do not justify changing the
still be identified as a match. Similarly, a query that is mappings. This is much more efficient than having a
mapped only to “saying” will also be identified as a person review all ontological mappings every time there
match. is an ontology update. Only the mappings of changed
terms (in either ontology) are reviewed for editing.
If the entry is a locally defined term (i.e., defined within
this document as specified with a variable name in The AQUA Question Answering system is under
Interlingua along with a local definition of that variable), development and has shown preliminary results of being
and if no corresponding entry can be found, the dynamic

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