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Biomedical subjects

M Romacker

Publications and source records attributed to M Romacker.

8 recordsLinked to original sources
PubMed1999

How knowledge drives understanding--matching medical ontologies with the needs of medical language processing.

In this article, we introduce a knowledge-based approach to medical text understanding. From an in-depth consideration of deep sentence and text understanding we distill basic requirements for an adequate knowledge representation framework. These requirements are then matched with currently available medical ontologies (thesauri, terminologies, etc.). A fundamental trade-off is recognized between large-scale conceptual coverage on the one hand, and formal mechanisms for integrity preservation and conceptual expressiveness on the other hand. We discuss various shortcomings of the most wide-spread ontologies to capture medical knowledge in-the-large. As a result, we argue for the need of a formally sound and expressive model along the lines of KL-ONE-style terminological representation systems in the format of description logics. These provide an adequate methodology for designing more sophisticated, flexible medical ontologies serving the needs of 'deep' knowledge applications which are by no means restricted to medical language processing.

Artificial Intelligence↗
PubMed1999

Discourse structures in medical reports--watch out! The generation of referentially coherent and valid text knowledge bases in the MEDSYNDIKATE system.

The automatic analysis of medical narratives currently suffers from neglecting text structure phenomena such as referential relations between discourse units. This has unwarranted effects on the descriptional adequacy of medical knowledge bases automatically generated from texts. The resulting representation bias can be characterized in terms of incomplete, artificially fragmented and referentially invalid knowledge structures. We focus here on four basic types of textual reference relations, viz. pronominal and nominal anaphora, textual ellipsis and metonymy and show how to deal with them in an adequate text parsing device. Since the types of reference relations we discuss show an increasing dependence on conceptual background knowledge, we stress the need for formally grounded, expressive conceptual representation systems for medical knowledge. Our suggestions are based on experience with MEDSYNDIKATE, a medical text knowledge acquisition system designed to properly deal with various sorts of discourse structure phenomena.

Artificial Intelligence↗
PubMed1999

Streamlining semantic interpretation for medical narratives.

We introduce two abstraction mechanisms by which the process of semantic interpretation of medical narratives can be simplified and further optimized. One relates to generalized triggering conditions, the other to inheritance-based specifications of semantic rules. The proposed methodology leads to a parsimonious inventory of abstract, simple and domain-independent semantic interpretation schemata whose effectiveness has been evaluated on a medical text corpus.

Evaluation Studies as Topic↗
PubMed1998

Why discourse structures in medical reports matter for the validity of automatically generated text knowledge bases.

The automatic analysis of medical full-texts currently suffers from neglecting text coherence phenomena such as reference relations between discourse units. This has unwarranted effects on the description adequacy of medical knowledge bases automatically generated from texts. The resulting representation bias can be characterized in terms of artificially fragmented, incomplete and invalid knowledge structures. We discuss three types of textual phenomena (pronominal and nominal anaphora, as well as textual ellipsis) and outline basic methodologies how to deal with them.

Artificial Intelligence↗
PubMed1998

Part-whole reasoning in medical ontologies revisited--introducing SEP triplets into classification-based description logics.

The development of powerful and comprehensive medical ontologies that support formal reasoning on a large scale is one of the key requirements for clinical computing in the next millennium. Taxonomic medical knowledge, a major portion of these ontologies, is mainly characterized by generalization and part-whole relations between concepts. While reasoning in generalization hierarchies is quite well understood, no fully conclusive mechanism as yet exists for part-whole reasoning. The approach we take emulates part-whole reasoning via classification-based reasoning using SEP triplets, a special data structure for encoding part-whole relations that is fully embedded in the formal framework of standard description logics.

Artificial Intelligence↗
PubMed1997

Text structures in medical text processing: empirical evidence and a text understanding prototype.

We consider the role of textual structures in medical texts. In particular, we examine the impact the lacking recognition of text phenomena has on the validity of medical knowledge bases fed by a natural language understanding front-end. First, we review the results from an empirical study on a sample of medical texts considering, in various forms of local coherence phenomena (anaphora and textual ellipses). We then discuss the representation bias emerging in the text knowledge base that is likely to occur when these phenomena are not dealt with--mainly the emergence of referentially incoherent and invalid representations. We then turn to a medical text understanding system designed to account for local text coherence.

Hospital Information Systems↗
PubMed1996

Automatic knowledge acquisition from medical texts.

An approach to knowledge-based understanding of realistic texts from the medical domain (viz. findings of gastro-intestinal diseases) is presented. We survey major methodological features of an object-oriented, fully lexicalized, dependency-based grammar model which is tightly linked to domain knowledge representations based on description logics. The parser adheres to the principles of robustness, incrementality and concurrency. The substrate of automatic knowledge acquisition are text knowledge bases generated by the parser from medical narratives, which represent major portions of the content of these documents.

Artificial Intelligence↗