We present an approach for ontology population from natural language English texts that extracts RDF triples according to FrameBase, a Semantic Web ontology derived from FrameNet. Processing is decoupled in two independently-tunable phases. First, text is processed by several NLP tasks, including Semantic Role Labeling (SRL), whose results are integrated in an RDF graph of mentions, i.e., snippets of text denoting some entity/fact. Then, the mention graph is processed with SPARQL-like rules using a specifically created mapping resource from NomBank/PropBank/FrameNet annotations to FrameBase concepts, producing a knowledge graph whose content is linked to DBpedia and organized around semantic frames, i.e., prototypical descriptions of events and situations. A single RDF/OWL representation is used where each triple is related to the mentions/tools it comes from. We implemented the approach in PIKES, an open source tool that combines two complementary SRL systems and provides a working online demo. We evaluated PIKES on a manually annotated gold standard, assessing precision/recall in (i) populating FrameBase ontology, and (ii) extracting semantic frames modeled after standard predicate models, for comparison with state-of-the-art tools for the Semantic Web. We also evaluated (iii) sampled precision and execution times on a large corpus of 110 K Wikipedia-like pages.

We present an approach for extracting knowledge from natural language English texts where processing is decoupled in two phases. The first phase comprises several standard NLP tasks whose results are integrated in a single RDF graph of mentions. The second phase processes the mention graph with SPARQL-like mapping rules to produce a knowledge graph organized around semantic frames (i.e., prototypical descriptions of events and situations). The decoupling allows: (i) choosing different tools for the NLP tasks without affecting the remaining computation; (ii) combining the outputs of different NLP tasks in non-trivial ways, leveraging their integrated and coherent representation in a mention graph; and (iii) relating each piece of extracted knowledge to the mention(s) it comes from, leveraging the single RDF representation. We evaluate precision and recall of our approach on a gold standard, showing its competitiveness w.r.t. the state of the art. We also evaluate execution times and (sampled) accuracy on a corpus of 110K Wikipedia pages, showing the applicability of the approach on large corpora.

Although the quantity of structured information on the Web and within organizations is increasing, the majority of information remains available only in unstructured form. While different in form, both unstructured and structured information sources provide information about entities in the world and their properties and relations; still, frameworks for their seamless integration have not been deeply investigated. In this paper the authors describe the KnowledgeStore, a scalable, fault-tolerant, and Semantic Web grounded open-source storage system for interlinking structured and unstructured data. They present the concept, design, function and implementation of the system, and report on its concrete usage in three application scenarios within the NewsReader EU project, where it stores and supports the querying of millions of news articles interlinked with millions of RDF triples extracted from text and imported from Linked Open Data sources. The authors report on data population and data retrieval performances of the system measured through a number of experiments, and they also discuss the practical issues and lessons learned from these experiences.