Erik Faessler

SYNTACTIC SIMPLIFICATION AND SEMANTIC ENRICHMENT—TRIMMING DEPENDENCY GRAPHS FOR EVENT EXTRACTION

In our approach to event extraction, dependency graphs constitute the fundamental data structure for knowledge capture. Two types of trimming operations pave the way to more effective relation extraction. First, we simplify the syntactic representation structures resulting from parsing by pruning informationally irrelevant lexical material from dependency graphs. Second, we enrich informationally relevant lexical material in the simplified dependency graphs with additional semantic meta data at several layers of conceptual granularity. These two aggregation operations on linguistic representation structures are intended to avoid overfitting of machine learning‐based classifiers which we use for event extraction (besides manually curated dictionaries). Given this methodological framework, the corresponding JReX system developed by the JulieLab Team from Friedrich‐Schiller‐Universität Jena (Germany) scored on 2nd rank among 24 competing teams for Task 1 in the “BioNLP’09 Shared Task on Event Extraction,” with 45.8% recall, 47.5% precision and 46.7% F1‐score on all 3,182 events. In more recent experiments, based on slight modifications of JReX and using the same data sets, we were able to achieve 45.9% recall, 57.7% precision, and 51.1% F1‐score.

Semedico: A Comprehensive Semantic Search Engine for the Life Sciences

SEMEDICO is a semantic search engine designed to support literature search in the life sciences by integrating the semantics of the domain at all stages of the search process—from query formulation via query processing up to the presentation of results. SEMEDICO excels with an ad-hoc search approach which directly reflects relevance in terms of information density of entities and relations among them (events) and, a truly unique feature, ranks interaction events by certainty information reflecting the degree of factuality of the encountered event.

Selecting and Tailoring Ontologies with JOYCE

We present Joyce, a scalable tool for identifying and assembling relevant (pieces of) ontologies from a repository of source ontologies, thus enabling the effective and efficient reuse of formalized domain knowledge. Joyce includes a conceptual filter to identify relevant classes, minimizes unintended redundancies, i.e. concept duplicates, and excludes knowledge considered irrelevant for the specific conceptual design task.

Annotation Data Management with JeDIS

This paper introduces the Jena Document Information System (JeDIS). The focus lies on its capability to partition annotation graphs into modules. Annotation modules are defined in terms of types from the annotation schema. Modules allow easy manipulation of their annotations (deletion or update) and the creation of alternative annotations of individual documents even for annotation formalisms that by design do not support this feature.