TL;DR: In this paper, the authors present an apparatus and method for dynamically constructing electronic and printable documents and forms, where an entity reference is read from a document instance and compared to entity identifiers provided in a catalog containing a plurality of entity identifiers.
Abstract: An apparatus and method for dynamically constructing electronic and printable documents and forms. An entity reference is read from a document instance and compared to entity identifiers provided in a catalog containing a plurality of entity identifiers. Each of the entity identifiers in the catalog is associated with an entity resolution process. An inference engine or other entity resolving processor is invoked to effectuate the resolution process associated with a matching entity identifier. The inference engine or entity resolving processor resolves the entity reference to a resolved entity, such as a component of text or graphics to be included in a document. Linking between the document, entity reference, and resolved entity provides for detailed auditing of the entity resolution process. A resolved entity may contain one or more embedded entity references which are similarly resolved. The dynamic document construction methodology may be implemented using a distributed networking approach, or on a stand-alone computer system. A significant advantage of the present invention concerns the re-usability of textual, graphical, and other components, thereby providing for the construction of any arbitrary document type having any arbitrary number of presentation formats. In one embodiment, the inference engine used to resolve entity references is converted to an executable form to enhance portability. A document or form constructed in accordance with the present invention may be published in printed or electronic form, such as in the form of a World Wide Web (Web) page.
TL;DR: In this paper, a method for identifying an entity from a plurality of entity references, each entity reference being linked with a separate ghost entity, is provided, which comprises the steps of comparing an entity reference of a first ghost entity with an entity relation of a second ghost entity to determine a match probability between the entity reference between the first ghost entities and the entity references of the second ghost entities.
Abstract: Disclosed herein are various exemplary systems and methods for linking entity references to entities and identifying associations between entities. In particular, a method for identifying an entity from a plurality of entity references, each entity reference being linked with a separate ghost entity, is provided. The method comprises the steps of comparing an entity reference of a first ghost entity with an entity reference of a second ghost entity to determine a match probability between the entity reference of the first ghost entity and the entity reference of the second ghost entity, linking the entity reference of the first ghost entity additionally with the second ghost entity and the entity reference of the second ghost entity additionally with the first ghost entity when the match probability is greater than or equal to a match threshold and repeating the steps of comparing and linking for one or more ghost entity pairings possible from the ghost entities. The method further comprises determining, for one or more entity references linked to a ghost entity, a score for the entity reference based at least in part on a match probability between the entity reference and a value representing the one or more entity references linked to the ghost entity and identifying the ghost entity as an actual entity based at least in part on one or more scores for the one or more entity references linked to the ghost entity.
TL;DR: In this paper, the content of Web sites or other information within a markup format is automatically translated using an appropriate script written in the conversion language to “blindly” process a large number of web sites, which may employ an ECMAScript interpreter, a tier architecture, an SGML parser and dynamic tree-to-tree transformations.
Abstract: The content of Web sites or other information within a markup format is automatically translated using an appropriate script written in the conversion language to “blindly” process a large number of Web sites. These implementations may employ an ECMAScript interpreter, a tier architecture, an SGML parser and dynamic tree-to-tree transformations. The tier architecture is used to control multiple target requests, grouping and organizing responses into markup documents. The SGML parser can provide fault-tolerant analysis of markup documents to make them conform to XML standards. The SGML parser can generate the tree of the resulting document as a dynamic mode representing the content of the original data. Dynamic tree-to-tree transformation is provided in general via a “template/match/select” script, and may also use such tools as an ECMAScript interpreter, a regular expression search, direct access to nodes by DOM navigation, and a transformation and service environment.
TL;DR: This paper presents a meta-analysed version of the SGML Declaration designed to facilitate rapid and efficient development of SGML-based document structures for posterity.
Abstract: Table of Contents * Background to SGML * SGML Documents * The Reference Concrete Syntax * Entity Declaration and Use * Declaring and Using SGML Elements * Attributes * Minimization * Other SGML Declarations * Multiple Document Structures * Altering the Concrete Syntax * The SGML Declaration * Document Parsing * Appendixes
TL;DR: In this paper, a method for stabilizing a knowledge graph is proposed, in which same entities in a semantic relation list between entities provided as an input are represented as a single node based on names and types of the entities.
Abstract: A method for stabilizing a knowledge graph includes: generating a knowledge graph in which same entities in a semantic relation list between entities provided as an input are represented as a single node based on names and types of the entities; computing, on the knowledge graph, semantic similarities between all potential entity pairs of same entity types by comparing, for each potential entity pair, a type of relation associated with an entity in the entity pair and an opponent entity to the entity; and selecting, based on the semantic similarities, a representative entity from each of semantically similar entity pairs on the knowledge graph and integrating an opponent entity to the representative entity into the representative entity. The method further includes computing relation weighted values between the entities by using a graph analysis and statistic information, and adding the weighted values to the knowledge graph.