https://www.bibsonomy.org/user/hkorte/knowledge_base_populationBibSonomy RSS Feed for /user/hkorte/knowledge_base_populationSource code to repeat the paper evaluation: We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, recursively induces lambda forms from these, and clusters them to abstract away syntactic variations of the same meaning. The MAP semantic parse of a sentence is obtained by recursively assigning its parts to lambda-form clusters and composing them. We evaluate our approach by using it to extract a knowledge base from biomedical abstracts and answer questions. USP substantially outperforms TextRunner, DIRT and an informed baseline on both precision and recall on this task.http://alchemy.cs.washington.edu/papers/poon09/hkorte2011-06-10T12:31:15+02:00dependency_trees knowledge_base_population markov_logic nlp tools unsupervised <span itemprop="description">Source code to repeat the paper evaluation: We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, recursively induces lambda forms from these, and clusters them to abstract away syntactic variations of the same meaning. The MAP semantic parse of a sentence is obtained by recursively assigning its parts to lambda-form clusters and composing them. We evaluate our approach by using it to extract a knowledge base from biomedical abstracts and answer questions. USP substantially outperforms TextRunner, DIRT and an informed baseline on both precision and recall on this task.</span>The OntoLT approach aims at a more direct connection between ontology engineering and linguistic analysis. OntoLT is a Protégé plug-in, with which concepts (Protégé classes) and relations (Protégé slots) can be extracted automatically from linguistically annotated text collections. It provides mapping rules, defined by use of a precondition language that allow for a mapping between linguistic entities in text and class/slot candidates in Protégé.http://olp.dfki.de/OntoLT/OntoLT.htmhkorte2010-03-11T23:20:54+01:00java knowledge_base_population ontology opensource tools <span itemprop="description">The OntoLT approach aims at a more direct connection between ontology engineering and linguistic analysis. OntoLT is a Protégé plug-in, with which concepts (Protégé classes) and relations (Protégé slots) can be extracted automatically from linguistically annotated text collections. It provides mapping rules, defined by use of a precondition language that allow for a mapping between linguistic entities in text and class/slot candidates in Protégé.</span>http://olp.dfki.de/olp3/hkorte2010-03-11T23:10:22+01:00knowledge_base_population ontology workshop <a itemprop="url" data-versiondate="2010-03-11T23:10:22+01:00" href="http://olp.dfki.de/olp3/" rel="nofollow" class="description-link">http://olp.dfki.de/olp3/</a>This workshop will gather researchers in a variety of fields that contribute to the automated construction of knowledge bases. It will be held at Xerox Research Centre Europe, near Grenoble (France), May 17-19, 2010.http://akbc.xrce.xerox.com/hkorte2010-03-09T14:08:27+01:00conference knowledge_base_population workshop <span itemprop="description">This workshop will gather researchers in a variety of fields that contribute to the automated construction of knowledge bases. It will be held at Xerox Research Centre Europe, near Grenoble (France), May 17-19, 2010.</span>Our goal is to develop a probabilistic knowledge base that mirrors the content of the web. We are developing a system that uses semi-supervised learning methods to learn to extract symbolic knowledge from unstructured text and HTML. We are exploring methods of continous learning, where our system runs 24x7, continuously learning to read better, and continuously extracting facts from the web. http://rtw.ml.cmu.edu/readtheweb.htmlhkorte2009-12-30T19:54:34+01:00information_extraction knowledge_base_population ontology project <span itemprop="description">Our goal is to develop a probabilistic knowledge base that mirrors the content of the web. We are developing a system that uses semi-supervised learning methods to learn to extract symbolic knowledge from unstructured text and HTML. We are exploring methods of continous learning, where our system runs 24x7, continuously learning to read better, and continuously extracting facts from the web. </span>Tom Mitchell (2009): self-supervised KBP, only NPs without Entity Linkinghttp://videolectures.net/iswc09_mitchell_ptsw/hkorte2009-12-30T19:50:26+01:00information_extraction knowledge_base_population ontology self-supervised semanticweb videolectures www <span itemprop="description">Tom Mitchell (2009): self-supervised KBP, only NPs without Entity Linking</span>Paul Buitelaar (2005)http://videolectures.net/sssw05_buitelaar_hltsw/hkorte2009-12-18T12:24:53+01:00knowledge_base_population ontology semanticweb to_view videolectures <span itemprop="description">Paul Buitelaar (2005)</span>