<?xml version='1.0' encoding='UTF-8'  ?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns="http://purl.org/rss/1.0/" xmlns:dc="http://purl.org/dc/elements/1.1/">	<channel rdf:about="http://www.semanlink.net/tag/text_to_kg">		<title>Text to KG</title>		<link>http://www.semanlink.net/tag/text_to_kg</link>		<description>Documents tagged with Text to KG</description>		<items>			<rdf:Seq>							<rdf:li resource="http://www.semanlink.net/doc/2025/02/2502_09956_kggen_extracting_"/>				<rdf:li resource="http://www.semanlink.net/doc/2025/02/github_stair_lab_kg_gen_know"/>				<rdf:li resource="http://www.semanlink.net/doc/2022/11/2210_13952_knowgl_knowledge_"/>				<rdf:li resource="http://www.semanlink.net/doc/2022/05/international_workshop_on_knowl"/>				<rdf:li resource="http://www.semanlink.net/doc/2022/05/databorg_knowledge_management"/>			</rdf:Seq>		</items>	</channel>		<item rdf:about="http://www.semanlink.net/doc/2025/02/2502_09956_kggen_extracting_">		<title>[2502.09956&#93; KGGen: Extracting Knowledge Graphs from Plain Text with Language Models</title>		<link>http://www.semanlink.net/doc/2025/02/2502_09956_kggen_extracting_</link>		<description>including MINE, a benchmark to evaluate how well a text-to-KG extractor captures information.		</description>		<dc:date>2025-02-18T15:07:20Z</dc:date>	</item>	<item rdf:about="http://www.semanlink.net/doc/2025/02/github_stair_lab_kg_gen_know">		<title>GitHub - stair-lab/kg-gen: Knowledge Graph Generation from Any Text</title>		<link>http://www.semanlink.net/doc/2025/02/github_stair_lab_kg_gen_know</link>		<description>&gt; KGGen uses an LLM-driven, multi-stage pipeline to improve graph sparsity issues: 
&gt; 1. Extract entities &amp; relations 
&gt; 2. Aggregate info across multiple docs 
&gt; 3. Cluster entities and relations based on semantic similarity (e.g. &quot;Supplier A LLC&quot; and &quot;Supplier-A&quot; are 1 node) 
&gt;
&gt;  we cluster similar nodes and edges respectively, which helps with curating a denser, richer graph. ([tweet&#93;(https://x.com/belindmo/status/1891621779073831171))		</description>		<dc:date>2025-02-18T15:05:08Z</dc:date>	</item>	<item rdf:about="http://www.semanlink.net/doc/2022/11/2210_13952_knowgl_knowledge_">		<title>[2210.13952&#93; KnowGL: Knowledge Generation and Linking from Text</title>		<link>http://www.semanlink.net/doc/2022/11/2210_13952_knowgl_knowledge_</link>		<description>How to fine-tune PLMs to read a sentence and
generate the corresponding full set of semantic annotations
that are compliant with the terminology of a KG?

&gt; we propose a framework able to convert text into
a set of Wikidata statements		</description>		<dc:date>2022-11-13T10:48:17Z</dc:date>	</item>	<item rdf:about="http://www.semanlink.net/doc/2022/05/international_workshop_on_knowl">		<title>International Workshop on Knowledge Graph Generation from Text (Text2KG) 2022</title>		<link>http://www.semanlink.net/doc/2022/05/international_workshop_on_knowl</link>		<dc:date>2022-05-30T09:49:09Z</dc:date>	</item>	<item rdf:about="http://www.semanlink.net/doc/2022/05/databorg_knowledge_management">		<title>DataBorg - Knowledge management simplified</title>		<link>http://www.semanlink.net/doc/2022/05/databorg_knowledge_management</link>		<description>&gt; DataBorg provides an all-in-one AI-powered platform for consumers and businesses that allows them to improve data understanding through knowledge extraction, integration and analysis.

includes text -&gt; knowledge graph conversion.		</description>		<dc:date>2022-05-14T10:22:34Z</dc:date>	</item></rdf:RDF>