## 2 Related Work  
Semantic representation presents the meaning of sentences, and the process should be reliable and computational [18]. The alternative approaches to semantic representation can be divided into two categories: document representation [1; 19] and meaning representation. **Document Representation**: Currently, document information exchange mainly has three approaches: (1) Standardization approaches define a semantic document by combining a set of standardized document compositions: for example, EDI-based ([[http://www.edibasics.com/ediresources/document-standards/](http://www.edibasics.com/ediresources/document-standards/)]([http://www.edibasics.com/ediresources/document-standards/](http://www.edibasics.com/ediresources/document-standards/))), XML-based (ebXML. Available: [[http://www.ebxml.org](http://www.ebxml.org)]([http://www.ebxml.org](http://www.ebxml.org))) and Web service-based ([[http://www.edibasics.com/ediresources/document-standards/](http://www.edibasics.com/ediresources/document-standards/)]([http://www.edibasics.com/ediresources/document-standards/](http://www.edibasics.com/ediresources/document-standards/))). The problem with this approach is that documents are only interoperable on representation syntax and templates, and these standards are heterogeneous and incompatible with each other. (2) Ontology modeling [20; 21] approaches define a semantic document in a certain domain (e.g., RDF [22], RDFS [23] and OWL ([[http://www.edibasics.com/ediresources/document-standards/](http://www.edibasics.com/ediresources/document-standards/)]([http://www.edibasics.com/ediresources/document-standards/](http://www.edibasics.com/ediresources/document-standards/)))). They are usually used to solve the problem of semantic interoperability and realize collaboration. Generally, an ontology clearly describes the relationships of entities [18] and can be employed for knowledge representation. However, if computers in different contexts participate in user-computer interaction, it will not be easy to achieve a consistent understanding, because an ontology is domain-dependent, preventing it from being understood between heterogeneous document descriptions. (3) Collaborative approaches [17; 24] allow participants from different contexts to construct document terms and solve the cross-domain problem, but the document is constrained by a template and lacks flexibility. One issue is that the user still needs a user template to construct the document. Current subjects of research on document representations are rule format [25; 26], ontology [20; 24], XML+Ontology [21], tree/graph [27], and collaborative approach [15; 17; 28]. First, it is not easy to embed and extract meanings to/from a document automatically. For example, it is not easy for a document written in natural languages to be automatically converted to a machine-processable format (e.g., RuleML [25; 26]). Second, constructing semantic documents needs intensive work. For example, [5] proposes a semantic disambiguation solution by using a machine-readable semantic network (e.g., WordNet) as a common knowledge base. However, it is time-consuming and sometimes unnecessary because it also disambiguates unambiguous terms. To acquire accurate semantic concept representation for a document, [20] requires learning a concept border from a particular document collection based on a particular ontology in the same domain. However, there is a heavy workload and enormous data redundancy to construct and store concept borders for different domains. Third, it is not easy to maintain semantic consistency between heterogeneous document systems. For example, [24] claims accurate mapping between different ontologies' entities, and [20] requires the similarity computation between keywords in a received document and equivalent terms in a domain-wide ontology. Both approaches hardly reach a trade-off between low computational demand and semantic interoperability.