Li, Weichen ; Abels, Patrick ; Ahmadi, Zahra ; Burkhardt, Sophie ; Schiller, Benjamin ; Gurevych, Iryna ; Kramer, Stefan (2021)
Topic-Guided Knowledge Graph Construction for Argument Mining.
12th IEEE International Conference on Big Knowledge. Auckland, New Zealand (07.-08.12.2021)
doi: 10.1109/ICKG52313.2021.00049
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Decision-making tasks usually follow five steps: identifying the problem, collecting data, extracting evidence, iden-tifying arguments, and making the decision. This paper focuses on two steps of decision-making: extracting evidence by building knowledge graphs (KGs) of specialized topics and identifying sentences' arguments through sentence-level argument mining. We present a hybrid model that combines topic modeling using latent Dirichlet allocation (LDA) and word embeddings to obtain external knowledge from structured and unstructured data. We use a topic model to extract topic- and sentence-specific evidence from the structured knowledge base Wikidata. A knowledge graph is constructed based on the cosine similarity between the entity word vectors of Wikidata and the vector of the given sentence. A second graph based on topic-specific articles found via Google supplements the general incompleteness of the structured knowledge base. Combining these graphs, we obtain a graph-based model that, as our evaluation shows, successfully capitalizes on both structured and unstructured data.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Li, Weichen ; Abels, Patrick ; Ahmadi, Zahra ; Burkhardt, Sophie ; Schiller, Benjamin ; Gurevych, Iryna ; Kramer, Stefan |
| Art des Eintrags: | Bibliographie |
| Titel: | Topic-Guided Knowledge Graph Construction for Argument Mining |
| Sprache: | Englisch |
| Publikationsjahr: | 9 Dezember 2021 |
| Verlag: | IEEE |
| Buchtitel: | Proceedings: 12th IEEE International Conference on Big Knowledge: ICBK 2021 |
| Veranstaltungstitel: | 12th IEEE International Conference on Big Knowledge |
| Veranstaltungsort: | Auckland, New Zealand |
| Veranstaltungsdatum: | 07.-08.12.2021 |
| DOI: | 10.1109/ICKG52313.2021.00049 |
| Kurzbeschreibung (Abstract): | Decision-making tasks usually follow five steps: identifying the problem, collecting data, extracting evidence, iden-tifying arguments, and making the decision. This paper focuses on two steps of decision-making: extracting evidence by building knowledge graphs (KGs) of specialized topics and identifying sentences' arguments through sentence-level argument mining. We present a hybrid model that combines topic modeling using latent Dirichlet allocation (LDA) and word embeddings to obtain external knowledge from structured and unstructured data. We use a topic model to extract topic- and sentence-specific evidence from the structured knowledge base Wikidata. A knowledge graph is constructed based on the cosine similarity between the entity word vectors of Wikidata and the vector of the given sentence. A second graph based on topic-specific articles found via Google supplements the general incompleteness of the structured knowledge base. Combining these graphs, we obtain a graph-based model that, as our evaluation shows, successfully capitalizes on both structured and unstructured data. |
| Freie Schlagworte: | UKP_p_OAM |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 16 Jul 2021 11:05 |
| Letzte Änderung: | 18 Apr 2024 13:37 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum