CrossBee: Cross-Context Bisociation Explorer
Matjaž Juršič1,2, Bojan Cestnik3,1
, Tanja Urbančič4,1, Nada Lavrač1,4
1 Jožef Stefan Institute, Ljubljana, Slovenia 2 International Postgraduate School Jožef Stefan, Ljubljana, Slovenia 3 Temida d.o.o., Ljubljana, Slovenia 4 University of Nova Gorica, Nova Gorica, Slovenia
{matjaz.jursic, bojan.cestnik, tanja.urbancic, nada.lavrac}@ijs.si
CrossBee is an exploration engine for text mining and
cross-context link discovery, implemented as a Web application
with a user-friendly interface. The system supports
the expert in advanced document exploration supporting
document retrieval, analysis and visualization. It enables
document retrieval from public databases like PubMed, as
well as by querying the Web, followed by document cleaning
and filtering through several filtering criteria. Document
analysis includes document presentation in terms of
statistical and similarity-based properties, topic ontology
construction through document clustering. A distinguishing
feature of CrossBee is its powerful cross-context and crossdomain
document exploration facility and bisociative
(Koestler 1964) term discovery aimed at finding potential
cross-domain linking terms/concepts. Term ranking based
on an ensemble heuristic (Juršič et al. 2012) enables the
expert to focus on cross-context links with high potential
for cross-context link discovery. CrossBee’s document
visualization and user interface customization additionally
support the expert in finding relevant documents and terms
through similarity graph visualization, a color-based domain
separation scheme and highlighted top-ranked bisociative
terms.
A typical user scenario starts by inputting two sets of
documents of interest and by regulating the parameters of
the system. The required input is a file with documents
from two domains. Each line of the file contains exactly
three tab-separated entries: (a) document identification
number, (b) domain acronym, and (c) the document text.
The other options available to the user include specifying
the exact preprocessing options, specifying the base heuristics
to be used in the ensemble, specifying outlier documents
identified by external outlier detection software,
defining the already known bisociative terms (b terms),
and others. Next, CrossBee starts a computationally very
intensive step in which it prepares all the data needed for
the fast subsequent exploration phase. During this step the
actual text preprocessing, base heuristics, ensemble, bisociation
scores and rankings are computed in the way
presented in the previous section. This step does not require
any user intervention. After this computation, the
user is presented with a ranked list of b term candidates.
The list provides the user with some additional information
including the ensemble’s individual base heuristics votes
and term’s domain occurrence statistics in both domains.
The user then browses through the list and chooses the
term(s) he believes to be promising b terms, i.e. terms for
finding meaningful connections between the two domains.
At this point, the user can start inspecting the actual appearances
of the selected term in both domains, using the
efficient side-by-side document inspection. In this way, he
can verify whether his rationale behind selecting this term.
CrossBee is available at website: http://crossbee.ijs.si/.
The system’s home page is shown below.
<references_biblio/>
References
Juršič, M.; Sluban, B.; Cestnik, B.; Grčar, M.; and Lavrač,
N. 2012. Bridging concept identification for constructing
information networks from text documents. In: Berthold,
M.R. ed., Bisociative Knowledge Discovery. Springer
LNAI 7250 (in press).
Koestler, A. 1964. The Act of Creation. New York: MacMillan.
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