=Paper=
{{Paper
|id=Vol-2106/preface
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2106/preface.pdf
|volume=Vol-2106
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2106/preface.pdf</pdf>
<pre>
                                   Preface
First Workshop on Deep Learning for Knowledge Graphs
       and Semantic Technologies (DL4KGS 2018)



    Semantic Web technologies and deep learning share the goal of creating in-
telligent artifacts that emulate human capacities such as reasoning, validating,
and predicting. There are notable examples of contributions leveraging either
deep neural architectures or distributed representations learned via deep neural
networks in the broad area of Semantic Web technologies. Knowledge Graphs
(KG) are one of the most well-known outcomes from the Semantic Web commu-
nity, with wide use in web search, text classification, entity linking etc. KGs are
large networks of real-world entities described in terms of their semantic types
and their relationships to each other.
    A challenging but paramount task for problems ranging from entity classi-
fication to entity recommendation or entity linking is that of learning features
representing entities in the knowledge graph (building knowledge graph embed-
dings) that can be fed into machine learning algorithms. The feature learning
process ought to be able to effectively capture the relational structure of the
graph (i.e. connectivity patterns) as well as the semantics of its properties and
classes, either in an unsupervised way and/or in a supervised way to optimize a
downstream prediction task. In the past years, Deep Learning (DL) algorithms
have been used to learn features from knowledge graphs, resulting in enhance-
ments of the state-of-the-art in entity relatedness measures, entity recommenda-
tion systems and entity classification. DL algorithms have equally been applied
to classic problems in semantic applications, such as (semi-automated) ontology
learning, ontology alignment, duplicate recognition, ontology prediction, relation
extraction, and semantically grounded inference.
    These proceedings present the nine accepted papers of the first DL4KGS
workshop held in conjunction with the Extended Semantic Web Conference
(ESWC 2018) in Heraklion, Greece. These papers have been selected from a
total of twelve submissions. Each submission was peer-reviewed by at least two
experts regarding its relevance for the workshop, scientific quality, originality,
and technical adequacy. Topics range from knowledge graph embeddings for
recommender systems, biomedical knowledge, and scientific text extraction, en-
tity relatedness detection, embedding-based classification of ontology alignment
changes to type prediction and representation, domain categorization and visual
semantic similarity inferences. The program of the workshop also includes an in-
vited talk by Pascal Hitzler on “Neural-Symbolic Integration and Its Relevance
to Deep Learning and the Semantic Web”.
    We would like to thank all the authors for their submissions and careful
preparation of the camera-ready versions, taking all provided comments into
consideration. We would also like to thank our Program Committee for pro-
viding detailed and knowledgeable reviews for all submissions. A list organizing
2

committee members and PC members has been included in the proceedings and
can also be found on our website at http://usc-isi-i2.github.io/DL4KGS/.




Besnik Fetahu, Dagmar Gromann, Maria Koutraki, Enrico Palumbo
Core Organizing Committee DL4KGS

</pre>