=Paper=
{{Paper
|id=Vol-2518/paper-CAOS3
|storemode=property
|title=Analyzing the Imagistic Foundation of
Framality via Prepositions
|pdfUrl=https://ceur-ws.org/Vol-2518/paper-CAOS3.pdf
|volume=Vol-2518
|authors=Aldo Gangemi,Dagmar Gromann
|dblpUrl=https://dblp.org/rec/conf/jowo/GangemiG19
}}
==Analyzing the Imagistic Foundation of
Framality via Prepositions==
https://ceur-ws.org/Vol-2518/paper-CAOS3.pdf
Analyzing the Imagistic Foundation of
Framality via Prepositions
Aldo GANGEMI b and Dagmar GROMANN a,1
a Center for Translation Studies, University of Vienna, Austria
b University of Bologna and ISTC-CNR, Italy
Abstract. Natural language understanding is a vibrant research area in Artificial In-
telligence that requires linguistic and commonsense knowledge. To unite both types
of knowledge, FrameNet associates words with semantic frames, conceptual struc-
tures that describe a type of object, event or situation. Frames are interrelated and
feature some image schematic foundations. However, the resource’s usefulness is
limited by non-standard semantics. Framester, lying on a solid formal frame seman-
tics, reengineers and links FrameNet to lexical and ontological resources to create
one joint, powerful knowledge base. In this paper, we use Framester of FrameNet
and of the Preposition Project (TPP) to systematically analyze the image-schematic
foundation of frames via preposition senses. Framal knowledge is extracted from
TPP, which contains senses for each English preposition, and frame interrelations
are analyzed for the imagistic foundation of framality via preposition senses.
Keywords. frame semantics, prepositions, image schemas, ontology alignment,
Linguistic Linked Data, knowledge graphs
1. Introduction
Automatically understanding natural language has been one of the most central chal-
lenges in Artificial Intelligence over the past centuries. In Natural Language Processing
(NLP) research on extracting commonsense knowledge from text mainly follows two
strands: action and situational knowledge, or structured relational knowledge. A third
stream is provided by FrameNet [1], which uses frame semantics [8] to map word mean-
ing to semantic frames, also establishing semantic roles and inter-frame relations. For in-
stance, the Spatial contact frame typically involves roles such as figure (an entity
or event located with contact to a ground), ground (the basis for describing the location
of the figure), temporal profile, direction, etc. Those roles are often evoked by
prepositions: on, against, on top of, upon, off, by verbs: contact, touch, by adjectives:
tangent, or by nouns, e.g. contact.
FrameNet has an analytic approach to base framality in imagism. Imagism denotes
that our experiences derive from sensory experiences with the external world [7], and
1 Corresponding Author: Center of Translation Studies, University of Vienna, Gymnasiumstrae 50, A-1190
Vienna, Austria; E-mail: dagmar.gromann@univie.ac.at
Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution
4.0 International (CC BY 4.0).
in this paper it refers to the theories of image schemas, which are generally described
as spatio-temporal relationships that indicate a sense of motion, and are abstract pat-
terns of sensori-motor experiences [13,14]. The inventory of image schemas in this pa-
per is taken from [13,14]. The previous example frame is associated with the frame
Contact image schema, which by definition relates to the C ONTACT image schema
[13], and is one of five subframes of the Image schema frame. A multitude of frames
relate by definition, lexical unit, and naming to image schemas, however, no explicit re-
lation to the abstract, non-lexical Image schema frame is established. The complexity
of uncovering embodied schemas in the structure of FrameNet has been analyzed before
[2,6], however, without the utilization of formal means.
Since FrameNet lacks a formal semantics, and it is hardly comparable to other lex-
ical and factual resources, Framester [9] provides a formal representation, while simul-
taneously establishing relations to other linguistic linked data, factual resources, and on-
tology schemas. Framester can be used to jointly query all the resources aligned to its
formal frame ontology.
In this paper, we extract framal knowledge from preposition senses, and analyze
their image-schematic grounding. The proposed method, using the Framester SPARQL
endpoint, retrieves frames associated with preposition senses and their lexical units, as
well as relations to other frames, and analyzes the resulting frame sets in order to recon-
struct their image-schematic grounding.
Prepositions represent an important vehicle for semantic roles in natural language.
Evidence from cognitive linguistics research has theoretically [15,23] and practically [5,
16] proven their importance in defining spatio-temporal relationships in natural language.
For instance, the expression the cat is on the mat experiences a drastic alteration of
spatio-temporal relationships when replacing the preposition on with above, below or
all over. Due to the multiplicity of meanings a single preposition potentially expresses,
the image-schematic analysis of this word class has fascinated researchers for decades
[4,5,12] and across languages [11].
Starting with preposition senses from The Preposition Project (TPP) [16], we ana-
lyze all explicit or implicit image-schematic foundations in FrameNet. By “explicit” we
refer to frames related to the Image schema frame, or stating “image schema” in their
definition, while “implicit” refers to frames that feature a substantial definitional over-
lap with image schemas, but have no direct explicit association. Our contributions are as
follows:
• three methods to detect image-schematic frames based on preposition senses an-
notated with frames:
∗ extracting all frames explicitly linked to image-schematic frames by inter-
frame relation utilizing the Framester SPARQL endpoint and frames relating
to “image schema” in their definition
∗ extracting all frames used to annotate preposition senses and manually analyz-
ing their definitions for their image-schematic content
∗ extracting all frames related to the lexical units of prepositions and manually
analyzing their definitions for their image-schematic quality
• a repository of frames with image-schematic grounding in order to in the future
obtain a complete image-schematic grounding of FrameNet
Given such a repository, annotating natural language also other than prepositions with
image-schemas will be facilitated. To the best of our knowledge no such comprehensive
analysis of image-schematic structures in FrameNet has been conducted so far.
The structure of our paper is as follows. In Sect. 2, we first provide preliminary
definitions of the resources utilized in this study and their relation to image schemas.
In Sect. 3, we detail approaches analyzing image-schematic grounding of prepositions
or image-schematic representations in FrameNet, a combination of which has, to the
best of our knowledge, not yet been conducted. Subsequently (Sect. 4), we detail the
three aspects of our analysis method and their results (Sect. 5). After a discussion of our
findings, the paper ends with concluding remarks.
2. Preliminaries
Ontological modeling of FrameNet in Framester is associated with image-schematic rep-
resentations by way of prepositional meaning. Three resources are used in this work,
which are briefly described here. Both FrameNet and the Preposition Project (TPP) have
been used in their RDF/OWL representation (the FrameNet reengineering procedure is
detailed in [17]) currently existing in the Framester distribution (accessible via SPARQL
endpoint or as a dump).2
2.1. FrameNet
FrameNet [1] tackles a long-standing research challenge to represent natural language
in machine- and human-readable way with a theoretical foundation in frame semantics
[8]. In this theory, word meaning is best understood as evoking a situation type, with its
participants, and props, which is called a frame, where frame elements (FE) are partici-
pant roles. A word or phrase may have one or more lexical units (LU) that is linked to a
frame, and marked with a disambiguating part-of-speech (POS) tag, e.g. “near.a” for an
adjectives and “near.prep” denoting a preposition. Associations of lemmas with frames
were accomplished in a manual annotation process3 .
Frames are interrelated through inheritance, subframe, using, and perspectival re-
lations [21]. Inheritance denotes a subsumption relation, e.g. Fluidic motion is sub-
sumed by Motion. Subframe denotes an (intensional) part of a frame, e.g. Halt is a sub-
frame of Motion. Using denotes a presupposition or entailment, e.g. Bringing presup-
poses Motion. Perspective means that there are at least two point of view for a same
situation, e.g. Giving is a perspective on Transfer.
Semantic types are used as selectional constraints over frame elements, e.g. the
Sentient type constrains the Agent role in the Activity frame.
Finally, some frames are marked as non-lexical when they are not supposed to bear
a direct lexicalisation. Examples include all frames subsumed by the Image schema
frame, and a few others, e.g. Source path goal.
2 https://github.com/framester/Framester
3 Frames can be explored from the online platform https://framenet.icsi.berkeley.edu/
fndrupal/
2.1.1. Image Schemas in FrameNet
A top-level frame called Image schema is semantically typed as non-lexical frame
and defined as “A Profiled region is picked out relative to a Ground”4 . It is equipped
with two FEs, Ground and Profiled region, and subsumes five more specific
frames as summarised in Table 1. The Alignment image schema could be inter-
preted as V ERTICALITY, Bounded region clearly relates to C ONTAINMENT, and the
Contact image schema is reminiscent of C ONTACT. The Proximity image schema
could potentially relate to N EAR -FAR but is too vaguely defined by differentiating it
from two other vaguely defined schemas, which makes it difficult to interpret. The most
difficult to map to image schemas is the Collocation image schema, which could
potentially be argued as representing C OVERING [4,5]. Further specifications are pro-
vided by frames inheriting from those image-schematic frames, such as differentiating
gradable and non-gradable proximity.
Image Schema Frame Definition
Alignment image schema An Alignment match region has the same orientation as the relatively lin-
ear Ground. The Alignment mismatch region is oriented perpendicularly to
the Ground.
Bounded region An Exterior, Surface, Boundary, and Interior are picked out relative to the
Ground. This Ground may either occupy only the Boundary (in which case
the Interior is negative space) or the Ground may fill the Interior.
Collocation image schema A Profiled region is specified as entirely or largely coinciding with that of a
Ground.
Contact image schema A Profiled region occupies the space in contact with the Ground.
Proximity image schema A Profiled region is picked out which is near to the Ground to a certain
Degree. [...] The Profiled region cannot contact or overlap the location of
the Ground, as contact, in English, is a categorically different situation–see
Contact image schema and Collocation image schema.
Table 1. Explicitly image-schematic frames in FrameNet
As evident from Table 1, the set of image schema frames in FrameNet is vague and
incomplete. An image-schema savvy reader will immediately notice a substantial omis-
sion of crucial image schemas, such as S OURCE PATH G OAL, S URFACE, S UPPORT, and
C ENTER -P ERIPHERY. Some partially or fully corresponding frames exist without a di-
rect relation or association to their potentially image-schematic basis. For instance, the
frame Path traveled is defined as “A Path, a series of connected locations, is traversed
by a Theme, moving under its own power or under the influence of a physical force.
The Path may be described in various terms depending on whether it is bounded or not.
If it is bounded, the Path may be identified by its Endpoints, which may be presented
separately as Source and Goal”5 . This definition aligns well with the definition of the
S OURCE PATH G OAL image schema.
FrameNet also explicitly represents metaphoric use of expressions, by providing
sentence-level tags such as “Metaphor” [21]. However, a sentence-level mapping pro-
4 Source https://framenet2.icsi.berkeley.edu/fnReports/data/frameIndex.xml?frame=
Image\_schema
5 Source https://framenet2.icsi.berkeley.edu/fnReports/data/frameIndex.xml?frame=
Path_traveled
vides little usage for the annotation of prepositions. In fact, this mapping is explicitly
described as vague in FrameNet documentation [21].
This incomplete and partly vague representation of imagism in framality has moti-
vated the approach in this paper to analyze the image-schematic grounding of a particular
word category, prepositions, in order to identify a more complete set of image-schematic
frames beyond the explicit modeling in FrameNet.
2.2. The Preposition Project
The Preposition Project (TPP) [16] aims at providing a gold standard resource of seman-
tic roles for each English preposition. In addition to semantic roles, the project character-
izes preposition behaviour in terms of complement and attachment points to their syntac-
tic behavior, which results in a rich semantic resource for prepositions. Prepositions are
grouped into senses grouped into WordNet-like synsets. 847 preposition senses for 373
prepositions, including phrasal prepositions, are associated with FrameNet frames and
frame elements. This explicit framal modeling, and the semantic function of a preposi-
tion in language to be a spatial indicator, render this resource an ideal component of our
analysis.
2.3. Framester
Framester [9] provides a formal semantics for frames, reengineered and curated linked
data versions of linguistic resources (WordNet, VerbNet, BabelNet, etc.), factual knowl-
edge bases (DBpedia, YAGO, etc.), and ontology schemas (e.g. DOLCE-Zero), with for-
mal links between them, resulting in a strongly connected RDF/OWL knowledge graph.
Framester can be used to jointly query (via a SPARQL endpoint6 ) all the resources
aligned to its formal frame ontology7 . Framester has been used [10,3] to formalise the
MetaNet resource of conceptual metaphors based on FrameNet frames as metaphor
sources and targets, as well as to uncover semantic puzzles emerging from a logical treat-
ment of frame-based metaphors. Yet, an image-schematic analysis of MetaNet is lacking,
and can be enabled by a refinement of FrameNet imagistic foundation
3. Related Work
Two main strands of research are related to our analysis: approaches that analyze the
image-schematic grounding of prepositions, and work on analyzing image schemas in
FrameNet. To the best of our knowledge, no combination of both has been proposed.
In the former category, Deane [5] analyzes the semantic variability of the preposition
over arguing for the necessity of an image-schematic basis in the process and building
on earlier work on polysemy in prepositions (e.g. [4]). In addition, a similar approach
is applied to interpretations of on, across, and above. In a more indirect way, Velasco et
al. [19] annotates the medical concept of pain in natural language with image schemas,
thereby implicitly annotating prepositions in use with their image-schematic grounding.
6 http://etna.istc.cnr.it/framester2/sparql
7 The Framester Schema can be visualized from: http://wit.istc.cnr.it/arco/lode/extract?
url=http://etna.istc.cnr.it/framester/framester.owl
For instance, most examples of C ONTAINMENT provide typical prepositions associated
with this image schema, that is, into, out into, through, out.
Sullivan [22] conducts a frame-based analysis of conceptual metaphors using En-
glish adjectives. She concludes that there seems to be a logic in the lexical choice of
conceptual metaphors, which we believe can potentially be modeled with an image-
schematic grounding of frames. Reed and Pease [20] construct a general cognition ontol-
ogy building on WorNet, FrameNet, and Suggested Upper Merged Ontology (SUMO),
but they do not deal with image-schemas, and focus on making psychological concepts
converge into a formal taxonomy. Petruck and Ellsworth [18] analyze spatial relations in
FrameNet, and conclude that the resource provides a solid foundation for modeling such
relations in natural language.
Bicknell and Dodge [2] analyze force-dynamics in FrameNet utilizing Embodied
Construction Grammar (ECG), and propose a set of rules to represent image schemas
in FrameNet. Later Dodge et al. [6] conduct a more detailed analysis of embodied cog-
nition in FrameNet with a view to leveraging it for ECG. In their analysis, similarly as
in Framester, they found a necessity to restructure information available in FrameNet
to make it more accessible and convert them to schemas in their grammar. In line with
our work presented here, there is a large correspondence between embodied schemas in
their grammar and frames in FrameNet, the mapping of which, however, is substantially
challenged by inconsistent interrelation of frames.
4. Method
A combination of explicit and implicit methods is utilized to uncover image-schematic
frames related to prepositions. In terms of explicit, benefiting from the formal linkages in
Framester, we use SPARQL queries on the FrameNet graph to extract frames associated
directly with image schema frames or mentioning the string “image schema” in thheir
definition. This method relies on hierarchal relations of frames annotating prepositions.
A second query-based approach extracts all frames related to preposition senses
utilizing four relations of FrameNet, that is, inheritsFrom, perspectiveOn, uses,
and subframeOf. As a more refined method, it searches for all frames of a preposition
sense. Most preposition senses are associated with one or more frame elements. For
instance, the preposition sense prepsense 000020094 2 is defined as “a place; physical
space that can be crossed”; with the lexical unit through has frame elements such as
Path.body movement, Goal.self motion, and Path.travel. In the domain of these
frame elements, more frames, such as Motion or Traversing, can be retrieved. The
latter is defined as “A Theme changes location with respect to a salient location, which
can be expressed by a Source, Path, Goal, Area, Direction, Path shape, or Distance”.
This definition clearly indicates S OURCE PATH G OAL, which is not explicitly marked as
image schema in the dataset, neither by relation nor in the definition. We follow the entire
hierarchy in the FrameNet graph of each central frame relation, that is, inheritsFrom,
perspectiveOn, uses, and subframeOf, starting from the ones obtained based on the
frame elements associated with preposition senses. Definitions of returned frames are
analyzed for their explicit overlap with image schema definitions and spatial primitives.
In terms of implicit methods, we retrieve frames associated with the lexical units of
prepositions. For instance, the lexical unit through is directly annotated with the frame
Locative relation and has a uses relation to Path shape. Our database query of
the previously annotated data [12] confirms this general notion of identifying this sense
as associated with the S OURCE PATH G OAL schema. Example data of the mentioned
database are provided in Table 2.
verb prep noun prepsense image schema
going through motions Location S OURCE PATH G OAL
get through crisis Location S OURCE PATH G OAL
see through end Location S OURCE PATH G OAL
Table 2. Example of additional annotated data to confirm image-schematic grounding of FrameNet
To check on resulting image schema relations, we utilize an image schema dataset
[12] where English prepositions from the Europarl corpus8 have been automatically role-
labelled along with their verbs and nouns, clustered, and manually annotated with image
schemas. An example of such data is provided in Table 2.
The data additionally contains senses for associated verbs and nouns, so that the
meaning of the prepositions can be mapped to the required verbs and nouns in TPP. For
instance, “travel by air” was role-labeled in that previous work: verb: verb of movement,
preposition: Journey, noun: physical object like water or atmosphere. This aligns well
to TPP’s prepsense 000121466 2, focused on locative and spatial expressions. This
can be differentiated from “granted by commission”, which is labeled with the “Assign”
verb, with “Instrument” as preposition sense, and nouns like “Council, Department”,
which correspond to prepsense 000211938 15 on action expressions, and an agent in
the ground position after the preposition. This dataset helps to determine whether frames
associated with lexical units or preposition senses could indeed be considered image-
schematic.
5. Results
We first report on results of traversing the hierarchy of basic frame relations. Ta-
ble 3 reports on relations to frames that explicitly mention “image schema” in
their definition and have an (indirect) relation to the preposition sense (prepsense)
by way of the inheritsFrom subsumption relation. All of them are not explic-
itly associated in any way with the core frame Image schema. The difference be-
tween Containment relation IS and Containment is that the former perspectivizes
the latter, but both definitions relate to C ONTAINMENT. Neither the Goal nor the
Source path goal image schemas relate to preposition senses or any of the frames that
directly relate to prepositions senses (see findings below). This corroborates the need for
a more consistent and analytic modeling of image-schematic foundations in FrameNet.
Some prepositions associated with image-schematic frames in Table 3 might not
intuitively be mapped to those frames. However, when checking the definition of the
preposition senses, the association is supported. For instance, about is hard to associate
with C ONTAINMENT, however, in the sense of around as in hills about/around the city,
the relation becomes more likely.
8 https://www.statmt.org/europarl/
frame prepsense count preposition examples
Trajector-Landmark 55 around, round, through, behind, because of,
from, on
Containment 11 around, round, alongside, up, down, with, on
the part of, about, in, for, below, underneath,
out of
Containment relation IS 14 around, round, alongside, up, down, with, on
the part of, inside, by, about, in, for, below,
underneath, out of
Goal 0 -
SourcePathGoal 0 -
Table 3. Frames with explicit mentions of image schema in their definitions
In a second query-based approach, we extract all frames related by any of the four
relations inheritsFrom, perspectiveOn, uses, and subframeOf in any number of
hops in the graph (along their combined closure). All resulting candidate frames for a
potential image-schematic grounding are presented in Table 4. Candidates without an
explicit sense of motion were excluded, such as Locative relation that defines the
location of a figure with respect to a ground. Candidates are identified based on their
motion and spatio-temporal elements in their definition. For instance, Motion noise
refers to “verbs take largely the same Source, Path and Goal expressions as other types
of Motion verbs”. Some are more specific, such as Change of state scenario relates
to movement towards the direction of an end state. Due to the multiplicity of retrieved
candidate frames per image schema, in Table 4 we group the frames, rather than ranking
them by frequency in preposition senses.
As with the previous query, some of these preposition examples might not intu-
itively be related with the corresponding image schema. However, the joint analysis of
preposition sense, frame, and image schema definition shows a clear relation, even of
“alongside” with PART-W HOLE.
In a third step, we analyzed the frames related either directly, or in one or two
hops to the lexical units in order to check on the above listing, and to potentially find
further image-schematic frames. Additionally, we compared the results thereof with
the previously annotated data to back the image-schematic foundation. Based on this
analysis we found the same and also further frames with image-schematic ground-
ing in their definitions represented in Table 5. Furthermore, we uncovered some direct
links to the main frame Image schema in the form of Bounded region, classified as
image schema itself, Spatial contact related to the Contact image schema, and
Non-gradable proximity that is in turn linked to the Proximity image schema.
One explicit image-schematic frame is that of Goal in the S OURCE PATH G OAL map-
ping, where no relations where found to prepositions senses but only to lexical units.
However, it has to be remembered here that this frame is only explicit in the sense of
mentioning the word “image schema” in the definition. For each preposition sense, all
associated lexical units are queried, and the predominant frame related to the correct
sense of the prepositions is analyzed, also in terms of its relations to other frames.
In all analysis methods but the last, S OURCE PATH G OAL is quantitatively dom-
inant. When querying lexical units, C ONTAINMENT frames are found to be more fre-
quent. By utilizing these different methods, we can see that those two image schemas
are the most common ones in annotating preposition senses. This tells us that these two
image schema frames preposition
S OURCE PATH G OAL Traversing, Motion scenario, around,
Motion noise, Motion directional, through, be-
Moving in place , hind, on top of,
Change of state scenario, for, by, till, up
Body movement, Change posture, to, off, toward,
Placing scenario, Use vehicle, past
Self motion, Operate vehicle,
Ride vehicle, Cause fluidic motion,
Fluidic motion, Mass motion,
Travel, Cause to start,
Cause to end,Cause to resume,
Cause to continue,
Cause to move in place, Removing,
Departing, Arriving, Removing scenario,
Sound movement, Light movement,
Emanating
PART-W HOLE Being included, Inclusion scenario, through, un-
Wholes and parts, Part whole, der, alongside,
Part piece, Shaped part, Grinding, within, via
Cause to fragment
C ONTAINMENT Containers, Containing, on, next to,
Container focused removing, inside, above,
Abounding with, In, atop, about,
Ingest substance, Bounded entity, underneath
Containment relation IS,
Container focused removing,
Container focused placing
B LOCKAGE Hindering, Impact, Cause impact, on the part
Thwarting of, with, over,
against
C ONTACT Attaching, Inchoative attaching, within, into, in
Being attached
S OURCE PATH G OAL, S CALING Change position on a scale, pursuant to,
Cause change of position on a scale in accordance
with
Table 4. Candidate frames related to preposition senses
might be most commonly underlying semantics of prepositions, however, further studies
are in order to confirm this assumption. One very interesting result of this last analysis
is that preposition senses related to a certain semantic type are never associated with
any image-schematic frame. The following list shows those types, an example, and one
example preposition sense as identified in Framester.
• Manner (handle with care, prepsense 000564045 3)
• Topic (about image schemas, prepsense 000342956 18)
• Cause (because of her smile, prepsense 000193438 11)
• Temporal (during this hour, prepsense 000193438 11)
• verbal nouns and object relation (payment of his debts, prepsense 000342956 0)
• Beneficiary (a present for you, prepsense 000193438 0)
• Possession (decision of the Council, prepsense 000342956 2)
• Agents (done by my cousin, prepsense 000143452 16)
• Material (made of wood, prepsense 000342956 14)
image schema frames preposition examples
S OURCE PATH G OAL Goal, Distributed position, towards, to, onto, off, over
Adjacency
PART-W HOLE Partitive, Be subset of of, in, among, alongside
C ONTAINMENT Interior profile relation, about, around, outside of, in, into,
ContainmentScenario, within
Containers, Bounded region,
Surrounding
F ORCE Level of force resistance with, over, against, around
C ONTACT Spatial contact onto, into, between, upon, up
against
V ERTICALITY Directional locative relation over, above, on top of, up, down,
beneath
Table 5. Candidate frames related to the lexical units of prepositions
From the previous list, it seems that some types share a lack of motion. Since image
schemas are defined as spatio-temporal relations requiring a sense of motion, which
makes them dynamic, this is an intuitive result. However, to allow for a full annotation
of natural language with image-schematic grounding, it might be worth defining a static
type of schema that accounts for these semantic types.
6. Discussion
Our results clearly show that a more structured mapping of the imagistic foundation of
frames is in order. The multitude of frames that clearly define spatio-temporal relation-
ships but have no explicit connection to any such frame shows a high potential for a
revised top-level modeling of image schemas in FrameNet. It, at the same time, con-
firms previous studies (e.g. [5,23]) regarding the identified complexity of preposition
senses and the number of different image schemas one single preposition can potentially
express. For instance, on can denote S UPPORT (be on the mat), S OURCE PATH G OAL
(travel on foot), V ERTICALITY (on top of the house), and many more.
In terms of analysis method, we remark that little overlap exists between the results
from the three methods employed. This means that joint reasoning across heterogeneous
datasets with multiple graph traversing patterns, and possibly with additional measures
is most promising for retrieving frames with a grounding in image-schematic structures.
One major advantage of these bottom-up methods is that they bring to light prepo-
sitions that – with a top-down approach – would unlikely be associated with a particular
image-schematic pattern, as in the case of about for C ONTAINMENT.
A major challenge is the treatment of borderline cases. Frames such as Contacting,
in the sense of establishing a communication channel, could potentially be likened to the
physical C ONTACT. Also frames related to Perceive might have more image-schematic
grounding than identified in this experiment. Thus, to accomplish a more complete anal-
ysis and annotation of frames with image schemas, we intend a full annotation of frames
with several annotators to improve on the treatment of borderline cases. This is also use-
ful to find all image schemas that might relate to one specific frame, since image schema
collocations, that is, more than one image schema applying to a specific natural language
unit or statement, are common phenomena.
For now we limited the selection of frames to the ones explicitly referring to the
lexical manifestation of image-schematic structures in their definitions, including spatial
primitives such as G OAL or C ONTACT. We also included frames that might not have
such explicit mentions but a definition that clearly resonates with the definition of a par-
ticular image schema. For instance, the Impact frame is defined as “While in motion, an
Impactor makes sudden, forcible contact with the Impactee”, which very closely relates
to the B LOCKAGE image schema. As part of future endeavors, a differentiation between
single image-schematic structures and their combinations would be interesting, such as
C ONTAINMENT and S OURCE PATH G OAL in Container focused removing.
One additional interesting finding of this repository of image-schematic frames is
that several standard image schemas, such as V ERTICALITY or S URFACE are strongly
underrepresented. This might be due to our analysis methods or choice of word class,
while other approaches might bring a higher diversity of image schemas to light. It might
also be due to some gaps in FrameNet that can be uncovered with this alignment of
frames with high-level cognitive building blocks. This is one important analysis in future
work, where a consistent mapping of all frames to high-level concepts is envisioned.
7. Conclusion
Prepositions turned out to uncover a large number of frames that have a clear image-
schematic grounding without any explicit relation in the FrameNet graph. As such, this
preliminary study provided an important analysis of the current state, which paves the
way to perform a complete grounding of FrameNet frames in Framester with image-
schematic structures.
We have applied three distinct methods in this study: analyzing FrameNet defini-
tions that mention image schemas, analyzing frames resulting from graph traversals in
Framester, and analyzing frames associated with lexical units in the Preposition project.
The study has exploited the semantic homogeneity provided by the Framester schema
for all resources examined.
All three methods returned different sets of candidate image-schematic frames, with
a limited overlap. This finding points towards ensemble methods for further investigation
into the consistency of imagistic foundations of framality. Nevertheless, the multitude
of image-schematic frames discovered indicates that an analysis of such foundation is
reasonable and fosters an explicit image schema modeling in FrameNet. For this, fur-
ther analysis including generalised grammatical feature rather than prepositions, and the
employment of multiple annotators are foreseen. In the long run, such textual analytics
and the proposed methods shall serve to uncover a full mapping between image schemas
and frames in Framester and a formal representation of image-schematic grounding of
frames.
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