Proceedings of the 12th European Conference on Knowledge ...

Benedikt Schmidt, Todor Stoitsev and Max Mühlhäuser
sentences in natural language) and unstructured textual elements (e.g. single words in natural
language). The text has been separated in two sets. On <strong>the</strong> one hand, application related text, like
menu labels, help texts etc. On <strong>the</strong> o<strong>the</strong>r hand, text that is part <strong>of</strong> <strong>the</strong> work process, e.g. <strong>the</strong> content <strong>of</strong>
a text file viewed during work executions. In <strong>the</strong> following, we will focus on textual information that is
part <strong>of</strong> <strong>the</strong> work process.
Generally, document-centric views on textual content dominate <strong>the</strong> literature (Steyvers & Griffiths,
2010). In a work process textual information is displayed based on passages <strong>of</strong> resources from
different repositories: <strong>the</strong> private system, shared repositories <strong>of</strong> a group/community/company or <strong>the</strong>
internet. Generally, it is unforeseeable which kind <strong>of</strong> content might occur in a work process. As <strong>the</strong>
textual content <strong>of</strong> a work process is a complex mixture <strong>of</strong> different, unconnected information, <strong>the</strong>
document-centric view is not applicable. One difference is especially important: <strong>the</strong> temporalization <strong>of</strong>
text in <strong>the</strong> work process. A piece <strong>of</strong> text gets associated with <strong>the</strong> time-span, during which it was in <strong>the</strong>
focus <strong>of</strong> an application (e.g. Micros<strong>of</strong>t Word displaying <strong>the</strong> text in a focused window). This duration
can be interpreted as an identifier <strong>of</strong> <strong>the</strong> relevance <strong>of</strong> text (assuming that <strong>the</strong> user was actively
working with <strong>the</strong> textual content).
3.3 Topics and Text similarity
In <strong>the</strong> following, two types <strong>of</strong> regularities <strong>of</strong> textual content that occur within work processes are
examined. The first regularity addresses <strong>the</strong> similarity <strong>of</strong> textual content <strong>of</strong> different users, when <strong>the</strong>y
are executing <strong>the</strong> same task. As <strong>the</strong>re are no restrictions to <strong>the</strong> information that users may use during
task execution, it is an important question, whe<strong>the</strong>r people, working in similar environment and having
comparable backgrounds, use <strong>the</strong> same type <strong>of</strong> information. The second regularity addresses topics
that are hidden in <strong>the</strong> textual content. Topic could be a guiding <strong>the</strong>me which structures a task
execution process.
3.3.1 Similarity <strong>of</strong> textual content
To examine <strong>the</strong> similarity <strong>of</strong> <strong>the</strong> text involved in <strong>the</strong> task execution processes, we modeled <strong>the</strong> textual
content <strong>of</strong> tasks in a vector space, using Latent Semantic Analysis (LSA). LSA realizes a bag-<strong>of</strong>words
approach by using a matrix with columns representing task instances and a word count for all
different words given in <strong>the</strong> row vectors. Time was considered as relevance factor, i.e. text presented
for a longer period <strong>of</strong> time to a user was considered more important than text presented for a short
period <strong>of</strong> time. Text display duration was used to weight <strong>the</strong> word count in <strong>the</strong> rows. Based on <strong>the</strong>
matrix a semantic space <strong>of</strong> lower dimension is created with a vector for each task. Task instances are
compared by calculating <strong>the</strong> cosine similarity <strong>of</strong> <strong>the</strong> respective vectors (with 1 denoting total similarity
and 0 denoting no similarity). The idea is that instances <strong>of</strong> <strong>the</strong> same tasks should have a high
similarity value, compared to instances <strong>of</strong> different tasks.
Each task instance has been compared with o<strong>the</strong>r existing task instances. Resulting similarities are
shown in Figure 5 as 40x40 matrix with an entry for each task comparison. The red boxes in <strong>the</strong> figure
stand for <strong>the</strong> comparisons <strong>of</strong> a task instance with o<strong>the</strong>r instances <strong>of</strong> <strong>the</strong> same task. From left to right
and top to down <strong>the</strong> eight instances <strong>of</strong> task 1 are shown, followed by eight instances <strong>of</strong> task 2 etc.
The black diagonal shows <strong>the</strong> comparison <strong>of</strong> a task with itself, resulting in a similarity <strong>of</strong> 1.
The figure shows that for <strong>the</strong> tasks 1, 3, 4 and 5 <strong>the</strong> mean similarity <strong>of</strong> instances <strong>of</strong> <strong>the</strong> same task is
notably higher than <strong>the</strong> mean similarity <strong>of</strong> a task instance with instances <strong>of</strong> o<strong>the</strong>r tasks. For tasks 1, 3
and 5 <strong>the</strong> topic was precisely defined in <strong>the</strong> task execution processes and a set <strong>of</strong> initial resources
was highlighted (e.g. application forms). Task 4 instances have smaller similarity value than task 1, 3
and 5 as no resources are proposed and <strong>the</strong> individuals only have a similar goal. Task 2 does not
have a high similarity among <strong>the</strong> task instances. As task 2 asked <strong>the</strong> participants to collect papers <strong>of</strong>
<strong>the</strong>ir research domain very different types <strong>of</strong> papers were searched, reviewed and selected.
Overall, instances <strong>of</strong> <strong>the</strong> same task have a high similarity value, in spite <strong>of</strong> different executors. The
detail <strong>of</strong> task description influences <strong>the</strong> similarity.
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