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https://ceur-ws.org/Vol-2065/paper01.pdf
Workshops and Tutorials at K-CAP2017
Proceedings Preface
Giuseppe Rizzo
ISMB
Turin, Italy
giuseppe.rizzo@ismb.it
The International Conference on Knowledge Capture (K-CAP) provides a
forum that brings together members of diverse research communities who are
interested in efficiently capturing knowledge from a vast range of sources and in
creating representations that can be useful for building knowledge-intensive au-
tonomous applications. Numerous research fields are investigating and applying
these aforementioned research lines and they include natural language process-
ing, machine learning, knowledge management, and semantic web. Besides the
traditional research track, K-CAP usually hosts workshops and tutorials on
topics related to the theme of the conference. In particular, workshops aim
to provide opportunities for exchanging views, advancing ideas, and discussing
preliminary results in an atmosphere that fosters the active exchange of ideas.
Workshops are usually held before the conference and prepare the attendees to
the discussions during the conference. Tutorials enable attendees to fully appre-
ciate current research trends, main schools of thoughts, and possible application
areas.
The 2017 conference, also known as the Ninth International Conference on
Knowledge Capture,1 aimed at attracting researchers from diverse areas of Ar-
tificial Intelligence, including knowledge representation, knowledge acquisition,
intelligent user interfaces, problem-solving and reasoning, planning, agents, text
extraction, and machine learning, information enrichment and visualization, as
well as researchers interested in cyber-infrastructures to foster the publication,
retrieval, reuse, and integration of data. Today these data come from an increas-
ingly heterogeneous set of resources that differ with regards to their domain,
media format, quality, coverage, viewpoint, bias. More than the sheer amount
of these data, their heterogeneity allows us to arrive at better models and an-
swer complex questions that cannot be addressed in isolation but require the
interaction of different scientific fields or perspectives. In most cases, knowledge
is not captured as a means to an end but to, for instance, enable better user in-
terfaces, improve retrieval beyond simple keyword search. For K-CAP 2017, we
1 http://k-cap2017.org
1
focused on the creation, enrichment, querying, and maintenance of knowledge
graphs out of heterogeneous data sources.
The 2017 conference welcomed in total two workshops and thee tutorials
scheduled the day before the conference started. Workshops and tutorials
opened the discussions: the workshops covered the crucial task of capturing
knowledge from scientific content and by investigating the need to go beyond
the traditional macro-reading processes for extracting knowledge from docu-
ments. In detail:
Second International Workshop on Capturing Scientific Knowledge 2
From the early days of Artificial Intelligence, researchers have been in-
terested in capturing scientific knowledge to develop intelligent systems.
There are a variety of formalisms used today in different areas of science.
Ontologies are widely used for organizing knowledge, particularly in bi-
ology and medicine. Process representations are used to do qualitative
reasoning in areas such as physics and chemistry. Probabilistic graphical
models are used by machine learning researchers, e.g., in climate modeling.
In addition to enabling more advanced capabilities for intelligent systems
in science, capturing scientific knowledge enables knowledge dissemination
and open science practices. This is increasingly more important to enable
the reuse of scientific knowledge across scientific disciplines, businesses and
the public. Although great advances have been made, scientific knowledge
is complex and poses great challenges for knowledge capture. This work-
shop provided a forum to discuss existing forms of scientific knowledge
representation and existing systems that use them, and to envision major
areas to augment and expand this important field of research. The in-
creasing emphasis in open science has had a major focus on data sharing
but it needs to encompass knowledge as well. There are many research
challenges in open sharing and reuse of scientific knowledge that need to
be addressed in future research. The workshop had as opening an invited
talk by Suzanne Pierce and seven papers presented.
Machine Reading 3 Machine reading holds significant potential for automat-
ing knowledge capture, especially given the continuing improvements in
natural language processing technologies. Macro-reading techniques (skim-
ming many documents) now enable collecting large databases of facts,
while modern micro-reading techniques (comprehension of individual para-
graphs) have proven effective at factoid question answering. In this work-
shop, participants will discuss ways to develop new capabilities in macro-
and micro-reading to take these to the next level, in particular to ex-
tract useful representations of text (be they symbolic, neural, or a hybrid)
that enable, for example, automated reasoning to answer non-trivial ques-
tions. This workshop provided a forum to researchers in discussing themes
related to knowledge-based approaches applied to deep processing of con-
2 https://sciknow.github.io/sciknow2017
3 http://www.cs.utexas.edu/users/porter/kcap-machinereadingworkshop.php
2
tent. It also addressed the topic of assessing at large scale the quality of
knowledge graphs. Five papers were presented at the workshop.
In addition to these workshops, three tutorials were included in the program.
Also the tutorials attracted a lot of interest, they all shared the same format
alternating depth analyses of topics with practical demonstrations. Three main
topics were covered: representation learning, knowledge graphs, and deep learn-
ing. In detail:
Semantic data mining for knowledge acquisition 4 The tutorial provided
a synthetic, unifying view on semantic data mining and its application to
knowledge acquisition. Semantic data mining is a data mining approach
where domain ontologies are used as background knowledge. The challenge
is to mine knowledge encoded in domain ontologies and knowledge graphs
in addition to purely empirical data. The tutorial aimed to present major
research challenges arising from peculiarities of semantic data mining such
as proper consideration of the semantics of background knowledge, deal-
ing with Open World Assumption, and semantic similarity measures. In
addition, it covered also some of the recent advances in the area, namely
semantic embeddings (embedding ontological background knowledge into
neural networks).
DOing REusable MUSical data 5 This tutorial firstly provided an in-depth
explanations of the DOREMUS model (and its underlying foundations,
CIDOC-CRM and FRBRoo) as well as the necessary controlled vocab-
ularies. It then discussed and demonstrated the process to that lead to
create a knowledge base of musical content starting from real data coming
from musical libraries and be transformed to be compliant to Schema.org
for various consumption scenarios. The entire DOREMUS tools chain were
presented (e.g. tools for reconciling large multilingual knowledge graphs);
the workshop covered also how the DOREMUS data can be consumed
through various applications including an exploratory search engine and
music recommender systems.
Hybrid techniques for knowledge-based NLP. Knowledge graphs meet
machine learning and all their friends6 Many different artificial in-
telligence techniques can be used to explore and exploit large document
corpora that are available inside organizations and on the Web. While
natural language is symbolic in nature and first approaches were based on
symbolic and rule-based methods (e.g., ontologies and knowledge bases),
most widely used methods have been based on statistical approaches (e.g.,
linear methods such as support vectors machines, probabilistic topic mod-
els, and non-linear methods such as neural networks). These two ap-
proaches, knowledge-based and statistical methods, have their limitations
4 http://www.cs.put.poznan.pl/alawrynowicz/wordpress/?page_id=662
5 https://doremus-anr.github.io/kcap17_tutorial
6 http://expertsystemlab.com/kcap2017
3
and strengths; there is an increasing trend that seeks to combine them
to get the best of both worlds. This tutorial covered the foundations and
modern practical applications of knowledge-based and statistical methods,
techniques and models and their combination for exploiting large docu-
ment corpora. This tutorial firstly focused on the foundations of many
of the techniques that can be used for this purpose, including knowledge
graphs, word embeddings, neural network methods, probabilistic topic
models, and then demonstrated how a combination of these techniques is
being used in practical applications and commercial projects where the
instructors are currently involved.
These five co-located events attracted a large audience, who shared insights
and fostered discussions with instructors and organizers. The overall take home
message was in line with the conference scope, i.e. better understanding and
framing the research of knowledge-based approaches to created autonomous and
intelligent systems.
4