In this paper, the semantic relationships between a predicate and its arguments in terms of semantic roles are employed to improve lexical-based named entity recognition (NER) in the molecular biology domain. The semantic roles were realized in various sets of syntactic features used by a machine learning model to explore what should be the efficient way in allowing this knowledge to provide the highest positive effect on the NER. The empirical results show that the best feature set consists of predicate’s surface form, predicate’s lemma, voice, and the united feature of subject-object head’s lemma and transitive-intransitive sense. The performance improvement from using these features indicates the advantage of the predicate-argument semantic knowledge on NER. There are still rooms to enhance NER by using this semantic knowledge (e.g. to employ other semantic roles besides agent and theme and to extend the rules for efficient identification of an argument’s boundary).

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The Semantic Web initiative has enabled a common infrastructure in knowledge representation for sharing knowledge of ontologies and instances through languages such as Resource Description Framework (RDF) and the Web Ontology Language (OWL). In this paper we present a framework for combining the shallow levels of semantic description commonly used in information extraction with the deeper semantic structures available in such ontology representation languages. In this approach named entities are considered as instances of concepts in a defined taxonomy, coreference relations as identity pools containing named entities and coreferring expressions, and events as predicateargument frames. The framework is being implemented within the Open Ontology Forge software for annotating media, currently text and images, in Web pages and locally stored document collections. We discuss the knowledge framework, some features of the system, and a road map for future development.

In this paper, we propose a scheme for event frame annotation integrated into the Open Ontology Forge (OOF) annotation tool. This is a key requirement for realization of knowledge description on the Semantic Web. Semantic information contained in each event frame is a set of relationships between a predicate and its arguments. As our aim is to keep OOF flexible for various types of annotation projects, the scheme proposed in this paper is designed based on the specialization three popular schemes: MUC-7’s template relation, PropBank’s predicate-argument structure and FrameNet’s semantic frame.

BACKGROUND:The exploitation of information extraction (IE), a technology aiming to provide instances of structured representations from free-form text, has been rapidly growing within the molecular biology (MB) research community to keep track of the latest results reported in literature. IE systems have traditionally used shallow syntactic patterns for matching facts in sentences but such approaches appear inadequate to achieve high accuracy in MB event extraction due to complex sentence structure. A consensus in the IE community is emerging on the necessity for exploiting deeper knowledge structures such as through the relations between a verb and its arguments shown by predicate-argument structure (PAS). PAS is of interest as structures typically correspond to events of interest and their participating entities. For this to be realized within IE a key knowledge component is the definition of PAS frames. PAS frames for non-technical domains such as newswire are already being constructed in several projects such as PropBank, VerbNet, and FrameNet. Knowledge from PAS should enable more accurate applications in several areas where sentence understanding is required like machine translation and text summarization. In this article, we explore the need to adapt PAS for the MB domain and specify PAS frames to support IE, as well as outlining the major issues that require consideration in their construction.RESULTS:We introduce PASBio by extending a model based on PropBank to the MB domain. The hypothesis we explore is that PAS holds the key for understanding relationships describing the roles of genes and gene products in mediating their biological functions. We chose predicates describing gene expression, molecular interactions and signal transduction events with the aim of covering a number of research areas in MB. Analysis was performed on sentences containing a set of verbal predicates from MEDLINE and full text journals. Results confirm the necessity to analyze PAS specifically for MB domain.CONCLUSIONS:At present PASBio contains the analyzed PAS of over 30 verbs, publicly available on the Internet for use in advanced applications. In the future we aim to expand the knowledge base to cover more verbs and the nominal form of each predicate.

Recent work in knowledge representation undertaken as part of the Semantic Web initiative has enabled a common infrastructure (Resource Description Framework (RDF) and RDF Schema) for sharing knowledge of ontologies and instances. In this paper we present a framework for combining the shallow levels of semantic description commonly used in MUC-style information extraction with the deeper semantic structures available in such ontologies. The framework is implemented within the PIA project software called Ontology Forge. Ontology Forge offers a server-based hosting environment for ontologies, a server-side information extraction system for reducing the effort of writing annotations and a many-featured ontology/annotation editor. We discuss the knowledge framework, some features of the system and summarize results from extended named entity experiments designed to capture instances in texts using support vector machine software.

In this paper, we will introduce Open Ontology Forge (OOF), a software tool for ontology creation, terminology annotation, and coreference annotation by experts applied to the biomedical domain. Encoding expert’s knowledge of this domain in a consistent and machine understandable way is important in order to make the knowledge publicly available and improve the quality of information extraction from the vast and growing amount of texts such as online journals. OOF provides a convenient environment for knowledge-encoding by biomedical experts, reducing much effort and maintaining the consistency of encoding at the same time.