Merge branch 'main' of github.com:UNIMIBInside/sti-website

unimib-datAI · Jul 19, 2024 · f5b8493 · f5b8493
2 parents 50b1d93 + 133de2f
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diff --git a/src/assets/check.svg b/src/assets/check.svg
diff --git a/src/assets/x.svg b/src/assets/x.svg
diff --git a/src/components/Header.astro b/src/components/Header.astro
@@ -40,6 +40,7 @@ import MenuItem from "@components/MenuItem.astro";
  <MenuItem href="tutorial" title="Tutorial" />
  <MenuItem href="approaches" title="Approaches" />
  <MenuItem href="datasets" title="Datasets" />
+ <MenuItem href="tools" title="Tools" />
  <MenuItem href="mammotab" title="MammoTab" />
  </ul>
  </div>

diff --git a/src/pages/datasets-temp.astro b/src/pages/datasets-temp.astro
diff --git a/src/pages/datasets.astro b/src/pages/datasets.astro
diff --git a/src/pages/index.astro b/src/pages/index.astro
@@ -22,91 +22,95 @@ import HorizontalTimeline from "@components/HorizontalTimeline.astro";
  What's Semantic Table Interpretation?
  </h2>
  <p class="mt-4 mb-6 md:text-base lg:text-base">
- Semantic Table Interpretation, as defined by the the <a href="https://sem-tab-challenge.github.io/2023/">SemTab challenge</a>, involves annotating relational
- tables with information from a Knowledge Graph (KG). This process
- includes associating each column in a table with one or more KG types,
- known as Column Type Annotation (CTA). Additionally, Cell Entity
- Annotation (CEA) is applied to annotate each cell in named entity columns
- with a KG entity or mark it as Not In Lexicon (NIL) if it does not exist in the KG.
- Columns Property Annotation (CPA) involves annotating pairs of columns
- with a KG property. The result of this annotation process is a
- table enriched with semantic information
- <p class="mt-4 mb-4 md:text-base lg:text-base"></p>
- <Button href="/sti-website/description" variant="secondary">
- Learn more about STI
- </Button>
- <section>
+ Semantic Table Interpretation, as defined by the the <a
+ href="https://sem-tab-challenge.github.io/2023/">SemTab challenge</a
+ >, involves annotating relational tables with information from a
+ Knowledge Graph (KG). This process includes associating each column in a
+ table with one or more KG types, known as Column Type Annotation (CTA).
+ Additionally, Cell Entity Annotation (CEA) is applied to annotate each
+ cell in named entity columns with a KG entity or mark it as Not In
+ Lexicon (NIL) if it does not exist in the KG. Columns Property
+ Annotation (CPA) involves annotating pairs of columns with a KG
+ property. The result of this annotation process is a table enriched with
+ semantic information
+ <p class="mt-4 mb-4 md:text-base lg:text-base"></p>
+ <Button href="/sti-website/description" variant="secondary">
+ Learn more about STI
+ </Button>
+ <section>
+ <h2
+ class="text-3xl font-semibold bg-gradient-to-r from-[#9E0974] to-[#6665E9] inline-block text-transparent bg-clip-text mt-10"
+ >
+ TUTSTI @ISWC2024
+ </h2>
+ <p class="mb-6 mt-4 md:text-base lg:text-base">
+ Discover the comprehensive world of Semantic Table Interpretation
+ (STI) in this tutorial, which covers both theoretical and practical
+ aspects, and trace the evolution of STI from heuristic-based methods
+ to machine learning (ML) techniques and the latest large language
+ model (LLM) innovations. By examining the unique characteristics,
+ advantages, and limitations of each approach you will understand
+ their optimal contexts of use
+ </p>
+ <div class="flex flex-wrap gap-2">
+ <Button href="/sti-website/tutorial" variant="secondary">
+ Learn more about TUTSTI @ ISWC2024
+ </Button>
+ <Button
+ href="https://iswc2024.semanticweb.org/event/3715c6fc-e2d7-47eb-8c01-5fe4ac589a52/summary"
+ variant="secondary"
+ className="md:ml-4"
+ >
+ ISWC 2024
+ <img src={logoISWC.src} class="ms-2 w-6 h-6" />
+ </Button>
+ </div>
+ </section>
+ </p>
+
+ <Section className="pt-20">
  <h2
  class="text-3xl font-semibold bg-gradient-to-r from-[#9E0974] to-[#6665E9] inline-block text-transparent bg-clip-text mt-10"
  >
- TUTSTI @ISWC2024
+ Our Approaches, Datasets, Tools and UIs
  </h2>
- <p class="mb-6 mt-4 md:text-base lg:text-base">
- Discover the comprehensive world of Semantic Table Interpretation
- (STI) in this tutorial, which covers both theoretical and practical
- aspects, and trace the evolution of STI from heuristic-based methods
- to machine learning (ML) techniques and the latest large language
- model (LLM) innovations. By examining the unique characteristics,
- advantages, and limitations of each approach you will understand their
- optimal contexts of use
- </p>
- <div class="flex flex-wrap gap-2">
- <Button href="/sti-website/tutorial" variant="secondary">
- Learn more about TUTSTI @ ISWC2024 
- </Button>
- <Button
- href="https://iswc2024.semanticweb.org/event/3715c6fc-e2d7-47eb-8c01-5fe4ac589a52/summary"
- variant="secondary"
- className="md:ml-4"
- >
- ISWC 2024
- <img src={logoISWC.src} class="ms-2 w-6 h-6" />
- </Button>
- </div>
- </section>
- </Section>
-
- <Section className="pt-20">
- <h2
- class="text-3xl font-semibold bg-gradient-to-r from-[#9E0974] to-[#6665E9] inline-block text-transparent bg-clip-text mt-10"
- >
- Our Approaches, Dataset, Tools and UI
- </h2>
- <HorizontalTimeline />
- </Section>
+ <HorizontalTimeline />
+ </Section>
 
- <Section title="" className="pt-20">
- <h2
- class="text-3xl font-semibold bg-gradient-to-r from-[#9E0974] to-[#6665E9] inline-block text-transparent bg-clip-text mt-10"
- >
- Our Datasets
- </h2>
- <div class="flex flex-wrap lg:flex-nowrap mt-8">
- <div>
- <img
- src={mammoth.src}
- alt="table illustration"
- class="lg:mt-0 lg:col-span-4 max-w-48 object-fit-cover lg:mr-8"
- />
- </div>
- <div>
- <h3 class="mt-2 text-2xl font-semibold text-orange-">Mammotab</h3>
- <p class="mb-6 mt-4 md:text-base lg:text-base">
- MammoTab is a unique dataset consisting of 1 million Wikipedia
- tables, extracted from over 20 million Wikipedia pages, and
- annotated using Wikidata. This dataset fills a gap in the current
- state-of-the-art resources, making it an excellent tool for testing
- and training Semantic Table Interpretation approaches. MammoTab is
- specifically designed to address several key challenges, including
- disambiguation, homonymy, and NIL-mentions, providing a
- comprehensive resource for advancing STI research and applications
- </p>
- <p class="mt-4 mb-6 md:text-base lg:text-base"></p>
- <Button href="/sti-website/mammotab" variant="secondary">
- Check out Mammotab
- </Button>
+ <Section title="" className="pt-20">
+ <h2
+ class="text-3xl font-semibold bg-gradient-to-r from-[#9E0974] to-[#6665E9] inline-block text-transparent bg-clip-text mt-10"
+ >
+ Our Datasets
+ </h2>
+ <div class="flex flex-wrap lg:flex-nowrap mt-8">
+ <div>
+ <img
+ src={mammoth.src}
+ alt="table illustration"
+ class="lg:mt-0 lg:col-span-4 max-w-48 object-fit-cover lg:mr-8"
+ />
+ </div>
+ <div>
+ <h3 class="mt-2 text-2xl font-semibold text-orange-">Mammotab</h3>
+ <p class="mb-6 mt-4 md:text-base lg:text-base">
+ MammoTab is a unique dataset consisting of 1 million Wikipedia
+ tables, extracted from over 20 million Wikipedia pages, and
+ annotated using Wikidata. This dataset fills a gap in the current
+ state-of-the-art resources, making it an excellent tool for
+ testing and training Semantic Table Interpretation approaches.
+ MammoTab is specifically designed to address several key
+ challenges, including disambiguation, homonymy, and NIL-mentions,
+ providing a comprehensive resource for advancing STI research and
+ applications
+ </p>
+ <p class="mt-4 mb-6 md:text-base lg:text-base"></p>
+ <Button href="/sti-website/mammotab" variant="secondary">
+ Check out Mammotab
+ </Button>
+ </div>
  </div>
- </div>
+ </Section>
  </Section>
  </main>
 </Layout>