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Overview

Range management is a distinct discipline founded on ecological principles and dealing with the use of

 rangelands and range resources for a variety of purposes

these purposes include use as watersheds ,wildlife habitat ,grazing by livestock ,recreation ,and aesthetics ,as well as other associated uses.

Inventory

Developing a plan for using rangeland resources requires information about the productive capability of the rangelands ,current condition , intended use , and land owner ives. Assistance with inventorying resources is available through local conservation districts and natural resource conservation service offices.

Practices

When developing a plan the rangeland resources , first consideration must be given to management of the vegetation resource through the use of a prescribed grazing system.

The preon should take into account periods of grazing , deferment ,rest , animal impact , and levels of use that will bring about desired changes in the plant community.

The second consideration in developing a plan is identifying those practices necessary to implement the desired prescribed grazing system. These practices help control or influence the movement of livestock necessary for uniform distribution of grazing. These practices may include water developments , fencing ,salting, stock trails ,and herding.

 When the vegetation management resulting from the prescribed grazing does not achieve the desired changes in the plant community within a reasonable length of time , one or more supplementary practices may need to be planned and applied to help accelerate the desired change . these practices often result in dramatic changes in the plant community and should be carefully planned and applied , with special follow_up management to insure they are effective and achieve the desired change .

Some of the practices to consider are seeding , brush management , prescribed burning , fertilizing , mechanical treatment , and water spreading.

There are areas that may require special consideration in developing management plans. This may include areas of sensitive soils , unique plants , riparian areas , adjacent land uses , recreation and historical sites.

5 Successful Text Mining solutions

Text Mining solutions process text to enable better access, to extract well-defined

results, to reduce the content to the relevant parts and, in the end, to reduce the

amount of reading as the main benefit to its users. It is yet unresolved, which existing

or future solution will be the best in the end. The following are some of the parameters

relevant in the design of Text Mining solutions that either support improvements

or, if not considered, will hinder usability: Types of data searched in the literature,

types of documents available, different ways to post-process the data, interface design,

linking with other resources etc. On the other hand, every successful Text Mining

solution incorporates design principles, which help to understand how terminological

resources and user profiles and expectations fit together.

Therefore, the third day covered talks presenting ingredients and pitfalls of successful

Text Mining systems. Opportunities for getting Text Mining involved in every day

curation work were explained in detail by Judith Blake (Jackson Lab), using the experience

from the Mouse Genome Database as an example, including relevance

classification, topic-based routing, gene name tagging and information extraction.

Anna Divoli (University of Chicago, U.S.A.) presented results from two user surveys

which were conducted in conjunction with the BioText project to explore on the priorities

in the design of user interfaces for biological users. There was a general agreement

that it is important to keep end users involved in the development phase. HM

Müller (Caltech, California, U.S.A.) presented the design principles of TextPresso,

which is being used by at least 20 curation teams around the world. J?rg Hakenberg

and Martin Krallinger (CNIO, Madrid, Spain) reported on the development of a meta

service for Text Mining tools that emerged from the second BioCreative competition,

which was acknowledged as having the potential of a high impact in the field by giving

access to advanced Text Mining solutions. Services were also the focus of the

presentation of Dietrich Rebholz-Schuhmann, highlighting a suite of Text Mining tools

hosted at the European Bioinformatics Institute. Commercial tools were presented by

Dagstuhl seminar proposal „ Ontologies and Text Mining for Life Science“ 5/5

Michael Schr?der (GoPubMed, University of Dresden, D) and David Milward (Linguamatics,

Cambridge, U.K.). An example for a very innovative application of Text

Mining was shown by Nigel Collier (University of Tokyo, Jp): The BioCastor system

gathers and analyses news for their relevance to indicate disease outbreaks, thus

building an early warning or “rumor surveillance” system.

6 Ongoing work in the development of phenotype resources

A topic that emerged in the course of the seminar was the increasing demand and

importance to manage, represent and integrate conceptual representation of phenotypes.

As an immediate action, present experts in this topic reported on ongoing work

and progress in this domain. Judith Blake (Jackson Laboratory, Maine, U.S.A.) presented

ongoing work in the design and development of the Mammalian Phenotype

Ontology at the Mouse Informatics Centre. This ontology was, among many other

textual resources, used by Ulf Leser and colleagues to infer predictions of protein

functions through the association of concept profiles composed of phenotypic features.

Suzanna Lewis (Berkeley Drosophila Genome Project, U.S.A.) reported on the

development of phenote.org, a novel resource for describing phenotype data in a

very generic data format. The format reduces all representations to tuples that are

formed by an ontological concept and a qualifier from a special qualifier ontology, an

approach which nicely leverages existing ontologies for a new purpose. Finally,

Robert H?hndorf (MPI, Leipzig, D) showed the involved logical consequences of representing

“phenotypes” as derivations from a wildtype which calls for the use of nonmonotonic

or default logics.

7 Conclusions

The seminar brought together researchers from different research fields that are

linked to Text Mining and ontologies in the life sciences and gave them a plenum to

discuss their shared and disparate views. It became clear that there could be better

collaborative research and that truly interdisciplinary approaches should give better

results over research restricted to only one domain, but such collaborative research

first of all increases the overhead and are probably not easy to sustain. It also became

clear, that there are difficulties attached to collaborative work which are linked

to cultural or social differences in the research work, like the question of where and

what to publish to sustain individual careers. Furthermore, finding research funding

for developing mature systems, ready to be used by biologists, instead of research

prototypes supporting “only” a publication is difficult. This situation results in many

interesting approaches that are never made available for real life applications. However,

the participants clearly acknowledged that seminars such as this one are exactly

the right way to overcome those problems.

4 Advanced NLP and Text Mining

Text Mining makes use of techniques from “pure”, domain-independent machine

learning and natural language processing. However, many current systems in the

Life Sciences use only very little linguistic information, i.e., typically only word stems

or part-of-speech tags. This may lead to misinterpretations of generated evidence,

since, for instance, negations and subject– relationships are ignored. Using

more linguistic information is therefore an obvious possibility to improve systems,

especially as tools for generating such information in principle are available in the

NLP community. However, such attempts sometimes report disappointing results.

The reasons for this finding are diverse, including parsers lacking accuracy or insuffi-

Dagstuhl seminar proposal „ Ontologies and Text Mining for Life Science“ 4/5

cient adaptation of the extraction techniques to the representation of information in

the text.

The second day of the seminar gave room to presentations on reports on technical

advances in Text Mining systems and applications. Named Entity Recognition, a hot

topic in the core of Text Mining for years now, was in the focus of talks by Ted

Briscoe (ComputerLab, Cambridge, U.K.), Peter Murry-Rost (University of Cambridge,

U.K.) and Martin Hofmann-Apitius (Fraunhofer SCAI, Bonn, D).

Ted Briscoe reported promising results on improving the accuracy of recognizing

names of fly genes in text, a notoriously difficult task. The other two speakers presented

latest results from applying Text Mining to chemical entities, which, in particular,

include the analysis of images in text to recover chemical structures. Advances in

systems for relationship extraction were presented by Goran Nenadic (University of

Manchester, U.K.) and Jung-Jae Kim (EBI, Hinxton, Cambridge, U.K.). A system covering

a particular important area, the resolution of anaphora in text, was shown by Su

Jiang (Infocomm, Singapore). Notably, this system is also available as web service to

be included in world-wide distributed Text Mining pipelines.

. From the Text

Mining perspective, they fall short to cover a significant part of the domain knowledge,

i.e. they are still sparsely populated, and they do not incorporate morphological and

syntactical variability (again, not the purpose of an ontological resource). On the

other hand biological researchers put significant effort into the development of more

and more complete ontological resources.

This topic was in the center of repeated and fruitful discussions throughout the whole

meeting. Only to pick a few examples, the following presentations in the seminar

showcased ongoing work spanning from Text Mining to ontologies. Paul Bruitelaar

(DFKI, Saarbrücken, D) focused on the ontology life cycle, highlighting on the need to

constantly adapt ontologies to the changing needs of the community. This topic was

also covered by Jong C. Park (KAIST, Korea) who demonstrated his analyses on

tracking changes in the gene ontology. Robert Stephens (University of Manchester,

U.K.), Christopher Brewster (University of Sheffield, U.K.) and David Shotton (University

of Oxford, U.K.) reported on an ongoing project for bootstrapping an ontology

for animal behavior using Text Mining; initial results from similar projects for phenotype

data and lipid metabolism were presented by Ulf Leser (Humboldt University,

Berlin, D) and Thomas W?chter (University of Dresden, D), respectively. All three

presentations highlighted the problem that despite the many papers on ontology

learning, actually very few methods are readily available. Stephan Schultz (University

of Freiburg, D) explained the latest developments in the BioTOP ontology which intends

to bridge from top-level ontologies like BOF to domain specific ontologies such

as the Gene Ontology. Studies are underway to use BioTop for word sense disambiguation,

an essential step in Text Mining.

Robert Stephens pointed out in his summary of the day, that there is a certain danger

that “ontologists” are disappointed by lack of perfection when using results from Text

Mining for ontology development, and that Text Mining researchers are disappointed

by deficiencies of existing ontologies, such as incompleteness and inadequate modeling

of lexical variation in the terminology used to express the labels of the concepts.

However, the seminar was successful in showing up the borderlines and the crossovers

between both research domains giving inspiration to novel approaches using

the best of both breeds.