哪位好心人帮我翻译一下英文谢谢,不要用机器翻译可以吗

哪位好心人帮我翻译一下英文谢谢,不要用机器翻译可以吗
Finding all patents relevant to a particular invention in
the vast amount of documents available in the many existing
patent databases is a difficult task. Moreover, missing just a single relevant patent, and thus violating intellectual
property (IP) rights of others, can be very expensive for
a company when introducing a new product on the market
that uses an already patented technology. Thus, professional
patent searchers are forced to read (or at least skim
through) all retrieved documents, because the relevant one
could be at the bottom of the search result list. This clearly
contrasts Web users posing ad-hoc queries to Web search
engines who hardly ever look further than at the ten topranked
results. Patent classification systems such as the
International Patent Classification (IPC) maintained by the
World Intellectual Property (WIPO) are important structural
instruments for organizing patents into taxonomies of
technology domains. These taxonomies allow searchers to
constrain search queries to particular technological fields in
order to reduce the amount of documents to be read.
However, these classification schemes have been built by
experts for experts. A more general taxonomy would make
it easier for non-expert users of patent information systems
to access and navigate through the information space. Such
non-experts might not be familiar with the definitions of the
section, classes and subclasses, groups and subgroups contained
in the IPC, but usually are familiar with the subject
matter of the invention or an idea itself, i.e. the scientific
background. To this end, we have created such a taxonomy,
the Wikipedia Science Ontology (WikiSCION), based
on the Wikipedia Science Portal, which is a well-maintained
starting point for a top-down discovery of Science as topic of
interest and its many subtopics. The Wikipedia can be seen
as a source of knowledge crafted, shaped and maintained
by a large community agreeing on a language that is supposed
to be easier to comprehend by non-experts. We have
developed a method for automatically assigning patent documents
to the classes of this taxonomy, and thus enriching
the patents with additional meta-information.
长了点,不过对我很重要

Finding all patents relevant to a particular invention in
the vast amount of documents available in the many existing
patent databases is a difficult task.Moreover,missing just a single relevant patent,and thus violating intellectual
property (IP) rights of others,can be very expensive for
a company when introducing a new product on the market
that uses an already patented technology.Thus,professional
patent searchers are forced to read (or at least skim
through) all retrieved documents,because the relevant one
could be at the bottom of the search result list.This clearly
contrasts Web users posing ad-hoc queries to Web search
engines who hardly ever look further than at the ten topranked
results.Patent classification systems such as the
International Patent Classification (IPC) maintained by the
World Intellectual Property (WIPO) are important structural
instruments for organizing patents into taxonomies of
technology domains.These taxonomies allow searchers to
constrain search queries to particular technological fields in
order to reduce the amount of documents to be read.
However,these classification schemes have been built by
experts for experts.A more general taxonomy would make
it easier for non-expert users of patent information systems
to access and navigate through the information space.Such
non-experts might not be familiar with the definitions of the
section,classes and subclasses,groups and subgroups contained
in the IPC,but usually are familiar with the subject
matter of the invention or an idea itself,i.e.the scientific
background.To this end,we have created such a taxonomy,
the Wikipedia Science Ontology (WikiSCION),based
on the Wikipedia Science Portal,which is a well-maintained
starting point for a top-down discovery of Science as topic of
interest and its many subtopics.The Wikipedia can be seen
as a source of knowledge crafted,shaped and maintained
by a large community agreeing on a language that is supposed
to be easier to comprehend by non-experts.We have
developed a method for automatically assigning patent documents
to the classes of this taxonomy,and thus enriching
the patents with additional meta-information.