=Paper=
{{Paper
|id=Vol-2465/profiles_short1
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2465/profiles_short1.pdf
|volume=Vol-2465
|dblpUrl=https://dblp.org/rec/conf/semweb/Yu19
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2465/profiles_short1.pdf</pdf>
<pre>
  Mining Machine-Readable Knowledge from
          Structured Web Markup

                                    Ran Yu

               GESIS - Leibniz Institute for the Social Sciences
                           50676 Köln, Germany
                             ran.yu@gesis.org



    Abstract. The World Wide Web constitutes the largest collection of
    knowledge and is accessed by billions of users in their daily lives through
    applications such as search engines and smart assistants. However, most
    of the knowledge available on the Web is unstructured and is difficult
    for machines to process which leads to the lowered performance of such
    smart applications. Hence improving the accessibility of knowledge on
    the Web for machines is a prerequisite for improving the performance of
    such applications.
         Knowledge bases (KBs) here refers to RDF datasets contains machine-
    readable knowledge collections. While KBs capture large amounts of fac-
    tual knowledge, their coverage and completeness vary heavily across dif-
    ferent types of domains. In particular, there is a large percentage of less
    popular (long-tail) entities and properties that are under-represented.
         Recent efforts in knowledge mining aim at exploiting data extracted
    from the Web to construct new KBs or to fill in missing statements of
    existing KBs. These approaches extract triples from Web documents, or
    exploit semi-structured data from Web tables. Although the extraction
    of structured data from Web documents is costly and error-prone, the
    recent emergence of structured Web markup has provided an unprece-
    dented source of explicit entity-centric data, describing factual knowledge
    about entities contained in Web documents. Building on standards such
    as RDFa, Microdata and Microformats, and driven by initiatives such
    as schema.org, a joint effort led by Google, Yahoo!, Bing and Yandex,
    markup data has become prevalent on the Web. Through its wide avail-
    ability, markup lends itself as a diverse source of input data for KBA.
    However, the specific characteristics of facts extracted from embedded
    markup pose particular challenges.
         This work gives a brief overview of the existing works on min-
    ing machine-readable knowledge from both structured and unstructured
    data on the Web, and introduces the KnowMore approach for augment-
    ing knowledge bases using structured Web markup data.




Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).

</pre>