WO2014098560A2 - A system and method for transforming an abstract representation to a linguistic representation and vice versa - Google Patents

A system and method for transforming an abstract representation to a linguistic representation and vice versa Download PDF

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Publication number
WO2014098560A2
WO2014098560A2 PCT/MY2013/000252 MY2013000252W WO2014098560A2 WO 2014098560 A2 WO2014098560 A2 WO 2014098560A2 MY 2013000252 W MY2013000252 W MY 2013000252W WO 2014098560 A2 WO2014098560 A2 WO 2014098560A2
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component
triples
linguistic
verb
representation
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PCT/MY2013/000252
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French (fr)
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WO2014098560A3 (en
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Khalil BEN MOHAMED
Benjamin CHU MIN XIAN
Lukose Dickson
Fadzly ZAHARI
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Mimos Berhad
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Publication of WO2014098560A3 publication Critical patent/WO2014098560A3/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G06F40/35—Discourse or dialogue representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/40—Processing or translation of natural language
    • G06F40/55—Rule-based translation
    • G06F40/56—Natural language generation

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  • the present invention relates to a system and method for linguistic processing. More particularly, the present invention relates to a system and method for transforming linguistic representation to abstract representation and vice versa.
  • a system (100) for transforming an abstract representation to a linguistic representation and/or vice versa comprises a representation processor (110), a stored mappings database (140), a standard vocabularies database (150), and a linguistic ontologies database (160).
  • the representation processor (110) includes an abstractor component (120), wherein the abstractor component (120) is used to transform a linguistic representation into an abstract representation by using abstraction rules; and a specializer component (130), wherein the specializer component (120) is used to transform an abstract representation into a linguistic representation by using specialization rules.
  • the abstractor component (120) comprises a concepts and properties extractor component (121), wherein the concepts and properties extractor component (121) is used to extract all the concepts, properties and linguistic tags based on the input of a linguistic representation; a verb determinator component (122), wherein the verb determinator component (122) is used to determine whether a concept is a verb using the linguistic ontologies database (160); a schemas extractor component (123), wherein the schemas extractor component (123) is used to extract a set of schemas from the standard vocabularies (150) and the linguistic ontologies (160) databases; a schemas mapper component (124), wherein the schemas mapper component (124) is used to map a concept to the extracted schemas from the schemas extractor component (123); and a concepts and properties matcher component (125), wherein the concepts and properties matcher component (125) is used to process all the unprocessed concepts and properties based on the abstraction rules.
  • a verb determinator component 122
  • the specializer component (130) comprises a triple extractor component (131), wherein the triple extractor component (131) is used to extract all the triples from an abstract representation; a property matcher component (132), wherein the property matcher component (132) is used to match a property of a triple to a schema in the stored mappings database (140) based on the specialization rules; a verb determinator component (133), wherein the verb determinator component (133) is used to determine whether a property is a verb by using the linguistic ontologies database (160); a verb mapper component (134), wherein the verb mapper component (134) is used to map a property of a triple to a schema in the linguistic ontologies database (160); a semantic roles mapper component (135), wherein the semantic roles mapper component (135) is used to convert a triple or a triple with a schema to a set of triples of a linguistic representation by using the linguistic ontologies database (160); and a triple assemble
  • a method for transforming a linguistic representation to an abstract representation is provided.
  • the method is characterised by the steps of receiving a linguistic representation as an input; extracting all the concepts, properties, linguistic tags of the linguistic representation by a concepts and properties extractor component (121); determining whether the extracted concepts in the set are empty; sending the extracted concepts to a verb determinator component (122) if the extracted concepts in the set are not empty; determining whether the concept is a verb by the verb determinator component (122); sending the concept to a schemas mapper component (124); mapping the concept to a schema provided by a schemas extractor component (123); storing the mapped schemas in a stored mappings database (140); sending a set of triples with unprocessed concept and property from the schema mapper component (124) to a concepts and properties matcher component (125); and processing the set of triples based on an abstraction rules by the concepts and properties matcher component (125).
  • the step of determining whether the concept is a verb by the verb determinator component (122) includes the steps of determining whether the concept is a verb by using the linguistic ontologies database (160); and checking whether the concept with its linked properties maps to one of the available schemas for this verb by using the linguistic ontologies database (160).
  • a method for transforming an abstract representation to a linguistic representation is provided.
  • the method is characterised by the steps of receiving an abstract representation as an input; extracting all the triples from the abstract representation by a triple extractor component (131) to produce a set of triples; determining whether the triples in the set are empty by the triple extractor component (131); sending the triples to a property matcher component (132) if the triples in the set are not empty; matching the property of the triples with a schema in a stored mappings database (140) and specialization rules by the property matcher component (132); sending the schema and the triples to a semantic roles mapper component (135) and proceeding to step (m) if there is a match between the property of the triples with a schema in the stored mappings database (140) and the specialization rules; sending the triples to a verb determinator component (133) if there is no match between the property of the triples with a schema in the stored mappings database (140) and the specialization rules; determining whether the property of the triples is a verb by the verb determinator component (133);
  • the method includes the step of sending the triples to a triples assembler component (136) and performing maximal join of all possible linguistic representation triples by the triples assembler component (136) if the triples in the set are empty.
  • the step of converting the triples and/or the schema to a set of triples of a linguistic representation includes finding a suitable concept type hierarchy and/or schema in the linguistic ontologies database (160) and mapping the concepts of the triples with the suitable concept type hierarchy and/or schema.
  • FIGS. 1(a-c) show block diagrams of a system (100) to transform an abstract representation to a linguistic representation and vice versa according to an embodiment of the present invention.
  • FIG. 2 shows a flowchart of a method for transforming linguistic representation to abstract representation according to an embodiment of the present invention.
  • FIG. 3 shows a flowchart of a method for transforming abstract representation to linguistic representation according to an embodiment of the present invention.
  • FIG. 1a there is shown a block diagram of a system (100) to transform an abstract representation to a linguistic representation and vice versa according to an embodiment of the present invention.
  • the system (100) is able to transform a linguistic representation into an abstract representation.
  • the system (100) is able to transform an abstract representation into a linguistic representation.
  • the linguistic representation refers to a structure representing meaning of a natural language text in terms of an entity-and-relation model of a non- linguistic domain. As an example, a natural language text "John writes a book with a quilt' is provided as a linguistic representation below:
  • the abstract representation is a more high-level structure compared to the linguistic representation, containing standardized terms, easier to read by a human but still processable by a computer.
  • An example of an abstract representation of the above linguistic representation is provided below:
  • the system (100) comprises of a representation processor (110), a stored mappings database (140), a standard vocabularies database (150) and a linguistic ontologies database (160).
  • the representation processor (110) includes an abstractor component (120) and a specializer component (130).
  • the representation processor (110) is used to receive a linguistic representation and an abstract representation and transform it into an abstract representation and a linguistic representation respectively by using predefined abstraction rules and specialization rules.
  • the stored mappings database (140) is used to store mapped schemas.
  • mapped schemas As an example of a stored mapping is provided below:
  • the standard vocabularies database (150) is used to describe relationships and concepts of a term.
  • Such standard vocabularies database includes but not limited to Friend of a Friend (FOAF) knowledge base.
  • the linguistic ontologies database (160) used preferably include VerbNet and FrameNet, wherein VerbNet is lexical database of verb and FrameNet is a lexical database.
  • FIG. 1b shows a block diagram of the abstractor component (120) of the representation processor (110).
  • the abstractor component (120) is used to transform a linguistic representation into an abstract representation by using the predefined abstraction rules.
  • the abstractor component (120) comprises of a concepts and properties extractor component (121), a verb determinator component (122), a schemas extractor component (123), a schemas mapper component (124), and a concepts and properties matcher component (125).
  • the concepts and properties extractor component (121) is used to extract all the concepts and properties based on the input of a linguistic representation. Moreover, the concepts and properties extractor component (121) is also used to identify linguistic tags for the concepts identified. Thus, the concepts and properties extractor component (121) produces a set of concepts, properties and linguistic tags based on the linguistic representation provided.
  • the verb determinator component (122) is used to determine whether a concept is a verb or not by using the linguistic ontologies database (160).
  • the verb determinator component (122) uses VerbNet of the linguistic ontologies database (160) to determine whether the concept is a verb and thereon, the verb determinator component (122) uses FrameNet to check whether the concept with its linked properties maps to one of the available schemas for this verb.
  • the schemas extractor component (123) is used to extract a set of schemas from the standard vocabularies (150) and the linguistic ontologies (160) databases.
  • the schemas extractor component (123) extracts the associated schemas for a verb from FrameNet.
  • the schemas extractor component (123) keeps track of relationships between schemas from FrameNet and schemas from the standard vocabularies database (150).
  • the schemas mapper component (124) is used to map a concept to the extracted schemas from the schemas extractor component (123) to produce a set of triples with unprocessed concept and property.
  • the concept is "write” from the linguistic representation of "[Person:John] ⁇ -(agnt) ⁇ -[write]->(thme)- >[BookJ' and the extracted schema from FrameNet is provided as u [Author] ⁇ -(agnt) ⁇ - [write]->(thme)->[TextJ' while the extracted schema from the standard vocabularies database is provided as "[Author]->(dc:author)->[TextJ K , the schemas mapper component (124) maps "write” from the linguistic representation to "write” from the extracted schema from FrameNet by determining whether "Person: John” can be mapped to "Author", "Bool?
  • the schemas mapper component (124) determines whether there is a relationship between the extracted schema from FrameNet and the extracted schema from the standard vocabularies database (150), wherein the concept "write” of the linguistic representation is mapped to the extracted schema from the standard vocabularies database (150) if there is a relationship between the two extracted schemas.
  • the schemas mapper component (124) produces the triple as "Person: John dc:author Soo/ ".
  • the concepts and properties matcher component (125) is used to process all the unprocessed concepts and properties based on predefined abstraction rules. For example, the unprocessed concept and property of the linguistic representation of "[Person:John] ⁇ -(agnt) ⁇ -[write]->(thme)->[Book] ->(inst)->[QuillJ' are provided as "insf and "Quilt' as "Quiir is not a verb and the schemas of "write” do not have the property of "insf to be matched to.
  • the concepts and properties matcher component (125) processes "insf and "Quilf by using the abstraction rule of "if C1 is a verb and there exists C2 such that [C2] (agnt) [C1] AND [C1] (inst) [C3] exists, THEN C2 use C3.”
  • FIG. 1c shows a block diagram of the specializer component (130) of the representation processor (110).
  • the specializer component (120) is used to transform an abstract representation into a linguistic representation by using the predefined specialization rules.
  • the specializer component (130) includes a triple extractor component (131), a property matcher component (132), a verb determinator component (133), a verb mapper component (134), a semantic roles mapper component (135) and a triple assembler component (136).
  • the triple extractor component (131) is used to extract all the triples from an abstract representation provided as an input.
  • the property matcher component (132) is used to match a property of a triple to a schema in the stored mappings database (140) and predefined specialization rules.
  • a schema in the stored mappings database (140) is mapped to the property, wherein "[Author] ⁇ -(agnt) ⁇ -[write]->(thme)->[Textr is mapped to u [Author]->(dc:author)->rTextJ'.
  • the triple is matched to a hypothesis of the predefined specialization rules.
  • the verb determinator component (133) is used to determine whether a property is a verb or not by using the linguistic ontologies database (160).
  • the verb determinator component (133) uses VerbNet of the linguistic ontologies database (160) to determine whether the concept is a verb and thereon, the verb determinator component (133) uses FrameNet to determine whether the concept with its linked properties maps to one of the available schemas for this verb.
  • the verb determinator component (133) determines whether "write” is a verb and since it is a verb, the verb determinator component (133) determines the available schemas for "write” that resulted to a schema of "[Author] ⁇ -(agnt) ⁇ - [write]->(thme)->[T extj'.
  • the verb mapper component (134) is used to map a property of a triple to a schema in the linguistic ontologies database (160). For example, if the property of a tripe is provided as "dc:author”, the verb mapper component determine whether the label which is "author” is a verb or not, wherein the property is mapped to a schema if the label is a verb or transforming the label to the closest similar verb if the label is not a verb.
  • the semantic roles mapper component (135) is used to convert a triple or a triple with a schema to a set of triples of a linguistic representation.
  • the conversion is done by using the linguistic ontologies database (160) to find a suitable concept type hierarchy and/or schema to map with the concepts of the triples. For instance, where a triple is provided as "Person ohn write Boo ', the semantic roles mapper component (135) extracts semantic roles for the verb "write” from FrameNet which results to the semantic roles of "Animate” and “Resource”. Thereon, the semantic roles mapper component (135) maps "Animate” to "Person: John” and “Resource” to "Book'. As a result, the semantic roles mapper component (135) outputs "Person ohn agnt Write” and "Write thme Book'.
  • the triples assembler component (136) is used to perform maximal join of all possible linguistic representation triples.
  • the triples assembler component (136) performs a maximal join on an input of " [Person: JohnJ-(agnt)- [WriteJ' and "fWrite]-(thme)-[Book]' to result in the output of " [Person: John]-(agnt)- [Write]-(thme)-[BookJ
  • FIG. 2 there is shown a flowchart of a method for transforming a linguistic representation to an abstract representation by using the system (100) of FIG. 1.
  • the concepts and properties extractor component (121) receives a 000252
  • the linguistic representation as an input, wherein the linguistic representation is a conceptual graph comprising of concepts and properties.
  • the concepts and properties extractor component (121) extracts all the concepts and properties of the linguistic representation as in step 201.
  • the extracted concepts include "Person: John”, “write”, “Book' and “Quiir”
  • the extracted properties include "agnf, "thme” and "insf.
  • the concepts and properties extractor component (121) also identifies linguistic tags for the extracted concepts based on the extracted properties.
  • the concepts and properties extractor component (121) determines whether the extracted concepts in the set are empty as in decision 202. If the extracted concepts are empty, the method ends.
  • the extracted concepts are sent to the verb determinator component (122) to determine whether the concept is a verb as in step 203.
  • the verb determinator component (122) determines whether the concept is a verb by using the linguistic ontologies database (160).
  • the verb determinator component (122) uses VerbNet of the linguistic ontologies database (160) to determine whether the concept is a verb and thereon, the verb determinator component (122) uses FrameNet to check whether the concept with its linked properties maps to one of the available schemas for this verb.
  • the verb determinator component (122) determines whether "write” is a verb and since it is a verb, the verb determinator component (122) determines the available schemas for "write” that resulted to a schema of "[Author] ⁇ -(agnt) ⁇ -[write]->(thme)-> TextT.
  • the method returns to decision 202 wherein the concepts and properties extractor component (121) determines whether the extracted concepts in the set are empty. However, if the concept is a verb as in decision 204, the concept is sent to the schemas mapper component
  • the schemas mapper component (124) maps the received concept to a schema provided by the schemas extractor component (123) as in step 205. Thereon, the schema mapper component (124) stores all schemas that have been mapped with the concept received from the verb determinator component (122). The mapped schemas are stored in the stored mappings database (140). Thus, the schema mapper component (124) produces a set of triples with unprocessed concept and property which is sent to the concepts and properties matcher component (125).
  • the schemas extractor component (123) provides a schema extracted from FrameNet as "[Author] ⁇ -(agnt) ⁇ - [write]->(thme)->[Textr while a schema extracted from the standard vocabularies database as "[Author]->(dc:author)->[TextJ'
  • the schemas mapper component (124) maps "write” from the linguistic representation to "write” from the extracted schema from FrameNet by determining whether "Person: John” can be mapped to "Author”, "Book” can be mapped to "Text, "agnf can be mapped to "agnf and "thme” can be mapped to "thme” by using the concept hierarchy of the linguistic ontologies database (160).
  • the schemas mapper component (124) determines whether there is a relationship between the extracted schema from FrameNet and the extracted schema from the standard vocabularies database (150), wherein the concept "write” of the linguistic representation is mapped to the extracted schema from the standard vocabularies database (150) if there is a relationship between the two extracted schemas.
  • the schemas mapper component (124) produces the triple as "Person: John dc: author Book'.
  • the concepts and properties matcher component (125) processes the set of triples from the schema mapper component (124) by using the predefined abstraction rules.
  • the concepts and properties matcher component (125) produces an abstract representation of the linguistic representation provided as the input.
  • concepts and properties matcher component (125) processes the triple of "Person: John dc.author Book” with unprocessed concept and property of of "insf and "Quilt' by using the abstraction rule below:
  • FIG. 3 shows a flowchart of a method for transforming an abstract representation to a linguistic representation by using the system (100) of FIG. 1.
  • the triple extractor component (131) receives an abstract representation such as a Resource Description Framework (RDF) document. Thereon, the triple extractor component (131) extracts all the triples from the input as in step 301. Thus, the triple extractor component (131) produces a set of triples based on the abstract representation provided as the input.
  • decision 302 the triple extractor component (131) determines whether the triples in the set are empty. If the triples are empty, the triples are sent to the triples assembler component (136).
  • the triples assembler component (136) performs maximal join of all possible linguistic representation triples as in step 303.
  • the triples assembler component (136) performs a maximal join on a set of triples of ' [Person: John]-(agnt)-[Write ' and "[Write]-(thme)-[BookJ' that resulted to an output of " '[Person: John]-(agnt)-[Write]-(thme)-[BookJ'.
  • the triples in the set are not empty, the triples are sent to the property matcher component (132).
  • the property matcher component (132) matches the property of the triples with a schema in the stored mappings database (140) and predefined specialization rules as in step 304.
  • the triple is provided as "Person ohn dcauthor Book”
  • the property matcher component (132) matches "dc:author” to a schema in the stored mappings database (140), wherein u [Author] ⁇ - (agnt) ⁇ -[write]->(thme)->[TextJ' is mapped to "[Author]->(dc:author)->[TextJ'.
  • the triple is matched to a hypothesis of the predefined specialization rules.
  • the schema and the triples are sent to the semantic roles mapper component (135).
  • the semantic roles mapper component (135) converts the triples and the schema to a set of triples of a linguistic representation as in step 306. The conversion is done by using the linguistic ontologies database (160) to find a suitable concept type hierarchy to map with the concepts of the triples.
  • the semantic roles mapper component (135) converts a triple provided as "Person: John write Book” to a set of triples of "Person: John agnt Write” and "Write thme Book” by extracting semantic roles for the verb "write” from FrameNet that resulted to the semantic roles of "Animate” and “Resource”, and mapping "Animate” to "Person ohn” and “Resource” to "BoolC.
  • the semantic roles mapper component (135) has converted the triples and schema to a set of triples of linguistic representation, the method returns to decision 302.
  • the triples is sent to the verb determinator component (133).
  • the verb determinator component (133) determines whether the property of the triples is a verb by using the linguistic ontologies database (160) as in step 307.
  • the verb determinator sends the triples to the semantic roles mapper component (135).
  • the semantic roles mapper component (135) converts the triples to a set of triples of a linguistic representation as in step 306. The conversion is done by using the linguistic ontologies database (160) to find a suitable concept type hierarchy and schema to map with the concepts of the triples. Thereon, the method returns to decision 302. If the property of the triples is not a verb as in decision 308, the verb determinator component (133) sends the triples to the verb mapper component (134). Thereon, the verb mapper component (134) maps the property of the triple to a schema in the linguistic ontologies database (160) as in step 309. The triples and the schema are then sent to semantic roles mapper component (135).
  • the semantic roles mapper component (135) converts the triples and schema to a set of triples of a linguistic representation as in step 306. The conversion is done by using the linguistic ontologies database (160) to find a suitable concept type hierarchy to map with the concepts of the triples. As a result, the semantic roles mapper component (135) produces a set of triples based on the abstract representation provided as the input.

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Abstract

The present invention relates to a system and method for linguistic processing. The system (100) comprises a representation processor (110) having an abstractor component (120) and a specializer component (130), a stored mappings database (140), a standard vocabularies database (150), and a linguistic ontologies database (160). The abstractor component (120) transforms a linguistic representation into an abstract representation by using abstraction rules. The abstractor component (120) includes a concepts and properties extractor component (121), a verb determinator component (122), a schemas extractor component (123), a schemas mapper component (124), and a concepts and properties matcher component (125). The specializer component (120) transforms an abstract representation into a linguistic representation by using specialization rules. The specializer component (130) includes a triple extractor component (131), a property matcher component (132), a verb determinator component (133), a verb mapper component (134), a semantic roles mapper component (135) and a triple assembler component (136).

Description

A SYSTEM AND METHOD FOR TRANSFORMING AN ABSTRACT REPRESENTATION TO A LINGUISTIC REPRESENTATION AND VICE VERSA
FIELD OF INVENTION
The present invention relates to a system and method for linguistic processing. More particularly, the present invention relates to a system and method for transforming linguistic representation to abstract representation and vice versa.
BACKGROUND OF THE INVENTION
Current computational approaches to text processing are able to produce complex linguistic structures. These linguistic structures represent the meaning of the natural language text; but that does not mean that the structure can be easily used. Linguistic structure in its broader context should be concerned with making real world references to convey, process, and assign meaning, as well as to manage and resolve ambiguity.
An example of an approach to produce the linguistic structures is disclosed by US Patent No. 7,464,026 B2 which relates to a system and method for performing semantic analysis that interprets a linguistic structure output by a natural language linguistic analysis system. The semantic analysis system converts the linguistic output by the natural language linguistic analysis system into a data structure model referred to as a semantic discourse representation structure (SemDRS). However, such approach does not use schema extracted from standard vocabularies and linguistic ontologies. Instead, it uses application schema which is dependent on each application. Furthermore, the transformation rules are manually built for each specific application schema. Therefore, there is a need to provide a system and method for linguistic processing that is able to transform linguistic representation to abstract representation and vice versa. SUMMARY OF INVENTION
In a first aspect of the present invention, a system (100) for transforming an abstract representation to a linguistic representation and/or vice versa comprises a representation processor (110), a stored mappings database (140), a standard vocabularies database (150), and a linguistic ontologies database (160). The representation processor (110) includes an abstractor component (120), wherein the abstractor component (120) is used to transform a linguistic representation into an abstract representation by using abstraction rules; and a specializer component (130), wherein the specializer component (120) is used to transform an abstract representation into a linguistic representation by using specialization rules.
Preferably, the abstractor component (120) comprises a concepts and properties extractor component (121), wherein the concepts and properties extractor component (121) is used to extract all the concepts, properties and linguistic tags based on the input of a linguistic representation; a verb determinator component (122), wherein the verb determinator component (122) is used to determine whether a concept is a verb using the linguistic ontologies database (160); a schemas extractor component (123), wherein the schemas extractor component (123) is used to extract a set of schemas from the standard vocabularies (150) and the linguistic ontologies (160) databases; a schemas mapper component (124), wherein the schemas mapper component (124) is used to map a concept to the extracted schemas from the schemas extractor component (123); and a concepts and properties matcher component (125), wherein the concepts and properties matcher component (125) is used to process all the unprocessed concepts and properties based on the abstraction rules.
Preferably, the specializer component (130) comprises a triple extractor component (131), wherein the triple extractor component (131) is used to extract all the triples from an abstract representation; a property matcher component (132), wherein the property matcher component (132) is used to match a property of a triple to a schema in the stored mappings database (140) based on the specialization rules; a verb determinator component (133), wherein the verb determinator component (133) is used to determine whether a property is a verb by using the linguistic ontologies database (160); a verb mapper component (134), wherein the verb mapper component (134) is used to map a property of a triple to a schema in the linguistic ontologies database (160); a semantic roles mapper component (135), wherein the semantic roles mapper component (135) is used to convert a triple or a triple with a schema to a set of triples of a linguistic representation by using the linguistic ontologies database (160); and a triple assembler component (136), wherein the triples assembler component (136) is used to perform maximal join of all possible linguistic representation triples.
In a second aspect of the present invention, a method for transforming a linguistic representation to an abstract representation is provided. The method is characterised by the steps of receiving a linguistic representation as an input; extracting all the concepts, properties, linguistic tags of the linguistic representation by a concepts and properties extractor component (121); determining whether the extracted concepts in the set are empty; sending the extracted concepts to a verb determinator component (122) if the extracted concepts in the set are not empty; determining whether the concept is a verb by the verb determinator component (122); sending the concept to a schemas mapper component (124); mapping the concept to a schema provided by a schemas extractor component (123); storing the mapped schemas in a stored mappings database (140); sending a set of triples with unprocessed concept and property from the schema mapper component (124) to a concepts and properties matcher component (125); and processing the set of triples based on an abstraction rules by the concepts and properties matcher component (125).
Preferably, the step of determining whether the concept is a verb by the verb determinator component (122) includes the steps of determining whether the concept is a verb by using the linguistic ontologies database (160); and checking whether the concept with its linked properties maps to one of the available schemas for this verb by using the linguistic ontologies database (160). In a third aspect of the present invention, a method for transforming an abstract representation to a linguistic representation is provided. The method is characterised by the steps of receiving an abstract representation as an input; extracting all the triples from the abstract representation by a triple extractor component (131) to produce a set of triples; determining whether the triples in the set are empty by the triple extractor component (131); sending the triples to a property matcher component (132) if the triples in the set are not empty; matching the property of the triples with a schema in a stored mappings database (140) and specialization rules by the property matcher component (132); sending the schema and the triples to a semantic roles mapper component (135) and proceeding to step (m) if there is a match between the property of the triples with a schema in the stored mappings database (140) and the specialization rules; sending the triples to a verb determinator component (133) if there is no match between the property of the triples with a schema in the stored mappings database (140) and the specialization rules; determining whether the property of the triples is a verb by the verb determinator component (133); sending the triples to the semantic roles mapper component (135) and proceeding to step (m) if the property of the triples is a verb; sending the triples to a verb mapper component (134) if the property of the triples is not a verb; mapping the property of the triple to a schema in a linguistic ontologies database (160) by the verb mapper component (134); sending the triples and the schema to the semantic roles mapper component (135); and converting the triples and/or the schema to a set of triples of a linguistic representation by the semantic roles mapper component (135).
Preferably, the method includes the step of sending the triples to a triples assembler component (136) and performing maximal join of all possible linguistic representation triples by the triples assembler component (136) if the triples in the set are empty.
Preferably, the step of converting the triples and/or the schema to a set of triples of a linguistic representation includes finding a suitable concept type hierarchy and/or schema in the linguistic ontologies database (160) and mapping the concepts of the triples with the suitable concept type hierarchy and/or schema.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention. FIGS. 1(a-c) show block diagrams of a system (100) to transform an abstract representation to a linguistic representation and vice versa according to an embodiment of the present invention. FIG. 2 shows a flowchart of a method for transforming linguistic representation to abstract representation according to an embodiment of the present invention.
FIG. 3 shows a flowchart of a method for transforming abstract representation to linguistic representation according to an embodiment of the present invention.
DESCRIPTION OF THE PREFFERED EMBODIMENT
A preferred embodiment of the present invention will be described herein below with reference to the accompanying drawings. In the following description, well known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.
Referring to FIG. 1a, there is shown a block diagram of a system (100) to transform an abstract representation to a linguistic representation and vice versa according to an embodiment of the present invention. The system (100) is able to transform a linguistic representation into an abstract representation. Moreover, the system (100) is able to transform an abstract representation into a linguistic representation. The linguistic representation refers to a structure representing meaning of a natural language text in terms of an entity-and-relation model of a non- linguistic domain. As an example, a natural language text "John writes a book with a quilt' is provided as a linguistic representation below:
[Person:John]<-(agnt)<-[write]->(thme)->[Book]
->(inst)->[Quill]
On the other hand, the abstract representation is a more high-level structure compared to the linguistic representation, containing standardized terms, easier to read by a human but still processable by a computer. An example of an abstract representation of the above linguistic representation is provided below:
[Person: John] ->(dc:author)->[Book]
->(use)->[Quill] The system (100) comprises of a representation processor (110), a stored mappings database (140), a standard vocabularies database (150) and a linguistic ontologies database (160). The representation processor (110) includes an abstractor component (120) and a specializer component (130). The representation processor (110) is used to receive a linguistic representation and an abstract representation and transform it into an abstract representation and a linguistic representation respectively by using predefined abstraction rules and specialization rules.
The stored mappings database (140) is used to store mapped schemas. As an example of a stored mapping is provided below:
[Author]<-(agnt)<-[write]->(thme)->[Text] maps to [Author]->(dc:author)->[Text]. The standard vocabularies database (150) is used to describe relationships and concepts of a term. Such standard vocabularies database includes but not limited to Friend of a Friend (FOAF) knowledge base.
The linguistic ontologies database (160) used preferably include VerbNet and FrameNet, wherein VerbNet is lexical database of verb and FrameNet is a lexical database.
FIG. 1b shows a block diagram of the abstractor component (120) of the representation processor (110). The abstractor component (120) is used to transform a linguistic representation into an abstract representation by using the predefined abstraction rules. The abstractor component (120) comprises of a concepts and properties extractor component (121), a verb determinator component (122), a schemas extractor component (123), a schemas mapper component (124), and a concepts and properties matcher component (125).
The concepts and properties extractor component (121) is used to extract all the concepts and properties based on the input of a linguistic representation. Moreover, the concepts and properties extractor component (121) is also used to identify linguistic tags for the concepts identified. Thus, the concepts and properties extractor component (121) produces a set of concepts, properties and linguistic tags based on the linguistic representation provided.
The verb determinator component (122) is used to determine whether a concept is a verb or not by using the linguistic ontologies database (160). In particular, the verb determinator component (122) uses VerbNet of the linguistic ontologies database (160) to determine whether the concept is a verb and thereon, the verb determinator component (122) uses FrameNet to check whether the concept with its linked properties maps to one of the available schemas for this verb.
The schemas extractor component (123) is used to extract a set of schemas from the standard vocabularies (150) and the linguistic ontologies (160) databases. In particular, the schemas extractor component (123) extracts the associated schemas for a verb from FrameNet. Moreover, the schemas extractor component (123) keeps track of relationships between schemas from FrameNet and schemas from the standard vocabularies database (150).
The schemas mapper component (124) is used to map a concept to the extracted schemas from the schemas extractor component (123) to produce a set of triples with unprocessed concept and property. As an example, where the concept is "write" from the linguistic representation of "[Person:John]<-(agnt)<-[write]->(thme)- >[BookJ' and the extracted schema from FrameNet is provided as u[Author]<-(agnt)<- [write]->(thme)->[TextJ' while the extracted schema from the standard vocabularies database is provided as "[Author]->(dc:author)->[TextJK, the schemas mapper component (124) maps "write" from the linguistic representation to "write" from the extracted schema from FrameNet by determining whether "Person: John" can be mapped to "Author", "Bool? can be mapped to "Text, "agnf can be mapped to "agnf and "thme" can be mapped to "thme" by using the concept hierarchy of the linguistic ontologies database (160). Thereon, the schemas mapper component (124) determines whether there is a relationship between the extracted schema from FrameNet and the extracted schema from the standard vocabularies database (150), wherein the concept "write" of the linguistic representation is mapped to the extracted schema from the standard vocabularies database (150) if there is a relationship between the two extracted schemas. Thus, the schemas mapper component (124) produces the triple as "Person: John dc:author Soo/ ". The concepts and properties matcher component (125) is used to process all the unprocessed concepts and properties based on predefined abstraction rules. For example, the unprocessed concept and property of the linguistic representation of "[Person:John]<-(agnt)<-[write]->(thme)->[Book] ->(inst)->[QuillJ' are provided as "insf and "Quilt' as "Quiir is not a verb and the schemas of "write" do not have the property of "insf to be matched to. Thus, the concepts and properties matcher component (125) processes "insf and "Quilf by using the abstraction rule of "if C1 is a verb and there exists C2 such that [C2] (agnt) [C1] AND [C1] (inst) [C3] exists, THEN C2 use C3."
FIG. 1c shows a block diagram of the specializer component (130) of the representation processor (110). The specializer component (120) is used to transform an abstract representation into a linguistic representation by using the predefined specialization rules. The specializer component (130) includes a triple extractor component (131), a property matcher component (132), a verb determinator component (133), a verb mapper component (134), a semantic roles mapper component (135) and a triple assembler component (136). The triple extractor component (131) is used to extract all the triples from an abstract representation provided as an input.
The property matcher component (132) is used to match a property of a triple to a schema in the stored mappings database (140) and predefined specialization rules. As an example, where a property for a triple "Person ohn dc:author Book' is provided as "dciauthof, a schema in the stored mappings database (140) is mapped to the property, wherein "[Author]<-(agnt)<-[write]->(thme)->[Textr is mapped to u[Author]->(dc:author)->rTextJ'. Thereon, the triple is matched to a hypothesis of the predefined specialization rules.
The verb determinator component (133) is used to determine whether a property is a verb or not by using the linguistic ontologies database (160). In particular, the verb determinator component (133) uses VerbNet of the linguistic ontologies database (160) to determine whether the concept is a verb and thereon, the verb determinator component (133) uses FrameNet to determine whether the concept with its linked properties maps to one of the available schemas for this verb. As an example for the linguistic representation of "[Person:John]<-(agnt)<-[write]- >(thme)->[BookJ', the verb determinator component (133) determines whether "write" is a verb and since it is a verb, the verb determinator component (133) determines the available schemas for "write" that resulted to a schema of "[Author]<-(agnt)<- [write]->(thme)->[T extj'.
The verb mapper component (134) is used to map a property of a triple to a schema in the linguistic ontologies database (160). For example, if the property of a tripe is provided as "dc:author", the verb mapper component determine whether the label which is "author" is a verb or not, wherein the property is mapped to a schema if the label is a verb or transforming the label to the closest similar verb if the label is not a verb. The semantic roles mapper component (135) is used to convert a triple or a triple with a schema to a set of triples of a linguistic representation. The conversion is done by using the linguistic ontologies database (160) to find a suitable concept type hierarchy and/or schema to map with the concepts of the triples. For instance, where a triple is provided as "Person ohn write Boo ', the semantic roles mapper component (135) extracts semantic roles for the verb "write" from FrameNet which results to the semantic roles of "Animate" and "Resource". Thereon, the semantic roles mapper component (135) maps "Animate" to "Person: John" and "Resource" to "Book'. As a result, the semantic roles mapper component (135) outputs "Person ohn agnt Write" and "Write thme Book'.
The triples assembler component (136) is used to perform maximal join of all possible linguistic representation triples. As an example, the triples assembler component (136) performs a maximal join on an input of " [Person: JohnJ-(agnt)- [WriteJ' and "fWrite]-(thme)-[Book]' to result in the output of " [Person: John]-(agnt)- [Write]-(thme)-[BookJ
Referring to FIG. 2, there is shown a flowchart of a method for transforming a linguistic representation to an abstract representation by using the system (100) of FIG. 1. Initially, the concepts and properties extractor component (121) receives a 000252
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linguistic representation as an input, wherein the linguistic representation is a conceptual graph comprising of concepts and properties.
The concepts and properties extractor component (121) extracts all the concepts and properties of the linguistic representation as in step 201. For a linguistic representation provided as "[Person:John]<-(agnt)<-[write]->(thme)->[Book] ->(inst)- >[Quilir, the extracted concepts include "Person: John", "write", "Book' and "Quiir, the extracted properties include "agnf, "thme" and "insf. In addition to that, the concepts and properties extractor component (121) also identifies linguistic tags for the extracted concepts based on the extracted properties.
Thereon, the concepts and properties extractor component (121) determines whether the extracted concepts in the set are empty as in decision 202. If the extracted concepts are empty, the method ends.
Otherwise, the extracted concepts are sent to the verb determinator component (122) to determine whether the concept is a verb as in step 203. The verb determinator component (122) determines whether the concept is a verb by using the linguistic ontologies database (160). In particular, the verb determinator component (122) uses VerbNet of the linguistic ontologies database (160) to determine whether the concept is a verb and thereon, the verb determinator component (122) uses FrameNet to check whether the concept with its linked properties maps to one of the available schemas for this verb. As an example for the linguistic representation of " [Person: John]<-(agnt)<-[write]->(thme)->[BookJ', the verb determinator component (122) determines whether "write" is a verb and since it is a verb, the verb determinator component (122) determines the available schemas for "write" that resulted to a schema of "[Author]<-(agnt)<-[write]->(thme)-> TextT.
In decision 204, if the concept is not a verb, the method returns to decision 202 wherein the concepts and properties extractor component (121) determines whether the extracted concepts in the set are empty. However, if the concept is a verb as in decision 204, the concept is sent to the schemas mapper component The schemas mapper component (124) maps the received concept to a schema provided by the schemas extractor component (123) as in step 205. Thereon, the schema mapper component (124) stores all schemas that have been mapped with the concept received from the verb determinator component (122). The mapped schemas are stored in the stored mappings database (140). Thus, the schema mapper component (124) produces a set of triples with unprocessed concept and property which is sent to the concepts and properties matcher component (125). As an example, where the concept is "write" from the linguistic representation of "[Person:John]<-(agnt)<-[write]->(thme)->[BookJ' and the schemas extractor component (123) provides a schema extracted from FrameNet as "[Author]<-(agnt)<- [write]->(thme)->[Textr while a schema extracted from the standard vocabularies database as "[Author]->(dc:author)->[TextJ' the schemas mapper component (124) maps "write" from the linguistic representation to "write" from the extracted schema from FrameNet by determining whether "Person: John" can be mapped to "Author", "Book" can be mapped to "Text, "agnf can be mapped to "agnf and "thme" can be mapped to "thme" by using the concept hierarchy of the linguistic ontologies database (160). Thereon, the schemas mapper component (124) determines whether there is a relationship between the extracted schema from FrameNet and the extracted schema from the standard vocabularies database (150), wherein the concept "write" of the linguistic representation is mapped to the extracted schema from the standard vocabularies database (150) if there is a relationship between the two extracted schemas. The schemas mapper component (124) produces the triple as "Person: John dc: author Book'. In step 206, the concepts and properties matcher component (125) processes the set of triples from the schema mapper component (124) by using the predefined abstraction rules. As a result, the concepts and properties matcher component (125) produces an abstract representation of the linguistic representation provided as the input. As an example, concepts and properties matcher component (125) processes the triple of "Person: John dc.author Book" with unprocessed concept and property of of "insf and "Quilt' by using the abstraction rule below:
if C1 is a verb and there exists C2 such that [C2] (agnt) [C1] AND [C1] (inst) [C3] exists, THEN C2 use C3.
As a result, the concepts and properties matcher component (125) produces "John dc:author Book' and "John use Quilf as the abstract representations. 000252
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FIG. 3 shows a flowchart of a method for transforming an abstract representation to a linguistic representation by using the system (100) of FIG. 1. Initially, the triple extractor component (131) receives an abstract representation such as a Resource Description Framework (RDF) document. Thereon, the triple extractor component (131) extracts all the triples from the input as in step 301. Thus, the triple extractor component (131) produces a set of triples based on the abstract representation provided as the input. In decision 302, the triple extractor component (131) determines whether the triples in the set are empty. If the triples are empty, the triples are sent to the triples assembler component (136). Thereon, the triples assembler component (136) performs maximal join of all possible linguistic representation triples as in step 303. As an example, the triples assembler component (136) performs a maximal join on a set of triples of ' [Person: John]-(agnt)-[Write ' and "[Write]-(thme)-[BookJ' that resulted to an output of " '[Person: John]-(agnt)-[Write]-(thme)-[BookJ'.
However, if the triples in the set are not empty, the triples are sent to the property matcher component (132). The property matcher component (132) matches the property of the triples with a schema in the stored mappings database (140) and predefined specialization rules as in step 304. As an example, if the triple is provided as "Person ohn dcauthor Book", the property matcher component (132) matches "dc:author" to a schema in the stored mappings database (140), wherein u[Author]<- (agnt)<-[write]->(thme)->[TextJ' is mapped to "[Author]->(dc:author)->[TextJ'. Thereon, the triple is matched to a hypothesis of the predefined specialization rules.
In decision 305, if there is a match between the property of the triples with a schema in the stored mappings database and predefined specialization rules, the schema and the triples are sent to the semantic roles mapper component (135). The semantic roles mapper component (135) converts the triples and the schema to a set of triples of a linguistic representation as in step 306. The conversion is done by using the linguistic ontologies database (160) to find a suitable concept type hierarchy to map with the concepts of the triples. As an example, the semantic roles mapper component (135) converts a triple provided as "Person: John write Book" to a set of triples of "Person: John agnt Write" and "Write thme Book" by extracting semantic roles for the verb "write" from FrameNet that resulted to the semantic roles of "Animate" and "Resource", and mapping "Animate" to "Person ohn" and "Resource" to "BoolC. Once the semantic roles mapper component (135) has converted the triples and schema to a set of triples of linguistic representation, the method returns to decision 302.
If there is no match between the property of the triples and a schema in the stored mappings database (140) in decision 305, the triples is sent to the verb determinator component (133). The verb determinator component (133) determines whether the property of the triples is a verb by using the linguistic ontologies database (160) as in step 307.
If the property of the triples is a verb as in decision 308, the verb determinator sends the triples to the semantic roles mapper component (135). Thereon, the semantic roles mapper component (135) converts the triples to a set of triples of a linguistic representation as in step 306. The conversion is done by using the linguistic ontologies database (160) to find a suitable concept type hierarchy and schema to map with the concepts of the triples. Thereon, the method returns to decision 302. If the property of the triples is not a verb as in decision 308, the verb determinator component (133) sends the triples to the verb mapper component (134). Thereon, the verb mapper component (134) maps the property of the triple to a schema in the linguistic ontologies database (160) as in step 309. The triples and the schema are then sent to semantic roles mapper component (135).
The semantic roles mapper component (135) converts the triples and schema to a set of triples of a linguistic representation as in step 306. The conversion is done by using the linguistic ontologies database (160) to find a suitable concept type hierarchy to map with the concepts of the triples. As a result, the semantic roles mapper component (135) produces a set of triples based on the abstract representation provided as the input.
While embodiments of the invention have been illustrated and described, it is not intended that these embodiments illustrate and describe all possible forms of the invention. Rather, the words used in the specifications are words of description rather than limitation and various changes may be made without departing from the scope of the invention.

Claims

1. A system (100) for transforming an abstract representation to a linguistic representation and/or vice versa comprising:
a) a representation processor (110),
b) a stored mappings database (140),
c) a standard vocabularies database (150), and
d) a linguistic ontologies database (160);
wherein said system (100) is characterised in that said representation processor (110) includes:
e) an abstractor component (120), wherein said abstractor component (120) is used to transform a linguistic representation into an abstract representation by using abstraction rules; and
f) a specializer component (130), wherein said specializer component (120) is used to transform an abstract representation into a linguistic representation by using specialization rules.
2. The system (100) as claimed in claim 1 , wherein said abstractor component (120) comprising:
a) a concepts and properties extractor component (121), wherein said concepts and properties extractor component (121) is used to extract all the concepts, properties and linguistic tags based on the input of a linguistic representation;
b) a verb determinator component (122), wherein said verb determinator component (122) is used to determine whether a concept is a verb using said linguistic ontologies database (160);
c) a schemas extractor component (123), wherein said schemas extractor component (123) is used to extract a set of schemas from said standard vocabularies (150) and said linguistic ontologies (160) databases;
d) a schemas mapper component (124), wherein said schemas mapper component (124) is used to map a concept to the extracted schemas from said schemas extractor component (123); and
e) a concepts and properties matcher component (125), wherein said concepts and properties matcher component (125) is used to process all the unprocessed concepts and properties based on the abstraction rules.
The system (100) as claimed in claim 1 , wherein said specializer component (130) comprising:
a) a triple extractor component (131), wherein said triple extractor component (131) is used to extract all the triples from an abstract representation;
b) a property matcher component (132), wherein said property matcher component (132) is used to match a property of a triple to a schema in said stored mappings database (140) based on the specialization rules;
c) a verb determinator component (133), wherein said verb determinator component (133) is used to determine whether a property is a verb by using said linguistic ontologies database (160);
d) a verb mapper component (134), wherein said verb mapper component (134) is used to map a property of a triple to a schema in said linguistic ontologies database (160);
e) a semantic roles mapper component (135), wherein said semantic roles mapper component (135) is used to convert a triple or a triple with a schema to a set of triples of a linguistic representation by using said linguistic ontologies database (160); and
f) a triple assembler component (136), wherein said triples assembler component (136) is used to perform maximal join of all possible linguistic representation triples.
A method for transforming a linguistic representation to an abstract representation by using the system (100) as claimed in claim 2, is characterised by the steps of:
a) receiving a linguistic representation as an input;
b) extracting all the concepts, properties, linguistic tags of the linguistic representation by a concepts and properties extractor component (121);
c) determining whether the extracted concepts in the set are empty; d) sending the extracted concepts to a verb determinator component (122) if the extracted concepts in the set are not empty;
e) determining whether the concept is a verb by the verb determinator component (122);
f) sending the concept to a schemas mapper component (124);
g) mapping the concept to a schema provided by a schemas extractor component (123);
h) storing the mapped schemas in a stored mappings database (140); i) sending a set of triples with unprocessed concept and property from the schema mapper component (124) to a concepts and properties matcher component (125); and
j) processing the set of triples based on an abstraction rules by the concepts and properties matcher component (125).
The method as claimed in claim 4, wherein the step of determining whether the concept is a verb by the verb determinator component (122) includes the steps of:
a) determining whether the concept is a verb by using the linguistic ontologies database (160); and
b) checking whether the concept with its linked properties maps to one of the available schemas for this verb by using the linguistic ontologies database (160).
A method for transforming an abstract representation to a linguistic representation by using the system (100) as claimed in claim 3, is characterised by the steps of:
a) receiving an abstract representation as an input;
b) extracting all the triples from the abstract representation by a triple extractor component (131) to produce a set of triples;
c) determining whether the triples in the set are empty by the triple extractor component (131);
d) sending the triples to a property matcher component (132) if the triples in the set are not empty; e) matching the property of the triples with a schema in a stored mappings database (140) and specialization rules by the property matcher component (132);
f) sending the schema and the triples to a semantic roles mapper component (135) and proceeding to step (m) if there is a match between the property of the triples with a schema in the stored mappings database (140) and the specialization rules;
g) sending the triples to a verb determinator component (133) if there is no match between the property of the triples with a schema in the stored mappings database (140) and the specialization rules;
h) determining whether the property of the triples is a verb by the verb determinator component (133);
i) sending the triples to the semantic roles mapper component (135) and proceeding to step (m) if the property of the triples is a verb;
j) sending the triples to a verb mapper component (134) if the property of the triples is not a verb;
k) mapping the property of the triple to a schema in a linguistic ontologies database (160) by the verb mapper component (134); I) sending the triples and the schema to the semantic roles mapper component (135); and
m) converting the triples and/or the schema to a set of triples of a linguistic representation by the semantic roles mapper component
(135).
The method as claimed in claim 6, wherein the method includes the step of sending the triples to a triples assembler component (136) and performing maximal join of all possible linguistic representation triples by the triples assembler component (136) if the triples in the set are empty.
The method as claimed in claim 6, wherein the step of converting the triples and/or the schema to a set of triples of a linguistic representation includes finding a suitable concept type hierarchy and/or schema in the linguistic ontologies database (160) and mapping the concepts of the triples with the suitable concept type hierarchy and/or schema.
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