-
Proposal
Corruption is inf ecting our society:
, the source domain is infection (i.e., a Disease) which is mapped to the target
domain Corruption (i.e., a Criminal Activity).
A published resource is available on-line called MetaNet [
1
], which de nes
a list of such metaphors and each metaphor consists of a source and a
target domain. In case of the above example, it evokes the metaphor Crime is a
disease. Each of these domains are represented as a linguistic frame called as
source frame and target frame respectively. These frames resembles the frames as
introduced in FrameNet [
2
], however, there are only few exact matches between
the frames in both the resources, meaning that MetaNet contains its own speci c
frames. For the running example, the source frame is a Disease and the
target frame is a Criminal Activity, where each of the roles of source frame i.e.,
disease and patient map to the roles in the target frame criminal activtiy
and victim respectively.
Can Knowledge Graphs Capture such kind of Semantics. While thinking
in terms of Knowledge Graphs, can this information about cross-domain
mapping be represented in the form of a Knowledge Graph? Amnestic Forgery [
5,
6
] is one of the attempts to integrate the metaphors from MetaNet to the
existing linguistic linked data cloud based on Frame Semantics, Framester [
4
]. In
this resource each metaphor is represented following the theory of Description
& Situation (D&S) [
7
]. According to this, a metaphor is a description and its
occurrence in the text is a situation. One of the drawbacks of this resource is
that it keeps very general metaphors. There is a need to nd a middle ground
between the cross-domain mappings as represented by frames and mappings
occurring in the text. In order to nd such kind of mappings we need to process
the textual resources rich in metaphors such as poems or corpora speci cally
created for metaphors.
One of the solutions is to use previously designed deep learning methods [
8
]
for distinguishing between metaphoric and literal expressions. Then nally
learning from these metaphoric expressions their speci c domains and enrich the
Knowledge Graph with this kind of information. Another solution would be to
create such kind of mappings in the existing Knowledge Graphs such as DBpedia
which contain those domains and are represented based on their literal meanings
but are not connected to the other domains based on their possible metaphoric
relation. This can help in better Identi cation/interpretation of metaphors or
generation of new metaphors.
Metaphors in Di erent Mediums Metaphors not only occur in language
but they also occur in di erent mediums such as visual metaphors (occurring
in images which can be related to political comics, advertisement or art work).
A metaphor can also be expressed in multiple mediums such as text with
image or gestures which can be found in videos. The last kind of metaphors are
referred to as multi-modal metaphors [
3
]. Tensors can help in dealing with
multidimensionality in such kind of metaphors. Following these lines many other
tasks come into play such as: (i) Metaphor identi cation along with their
interpretation by combining the information present in di erent mediums, (ii)
Capturing/Modeling cultural biases, meaning that the metaphor is interpreted
di erently based on cultural background.