Patent application number | Description | Published |
20080243746 | Compact Decision Diagrams - In one embodiment, a method includes determining an initial projected size of a BDD representing data for storage. The projected size corresponds to an initial projected number of decision nodes composing the BDD. The method includes determining an initial node structure for the decision nodes of the BDD according to the initial projected size of the BDD. The initial node structure includes for each decision node a variable identifier (ID), a 1-edge pointer, and a 0-edge pointer each represented by a minimum number of bits accommodating the initial projected number of decision nodes composing the BDD. | 10-02-2008 |
20080243907 | Efficient Indexing Using Compact Decision Diagrams - In one embodiment, a method includes accessing an inverted index of a searchable set of objects including key words. The inverted index includes multiple lists each corresponding to a particular key word and identifying a particular subset of the objects including the particular key word. The method includes generating a binary decision diagram (BDD) for each of one or more of the lists. The BDD corresponds to the particular key word of the list, and each decision node of the BDD represents an object in the searchable set of objects including the particular key word of the list. The method includes storing each of one or more of the lists as its BDD. Storage of the BDD facilitates more efficient storage of the inverted index. | 10-02-2008 |
20090094020 | Recommending Terms To Specify Ontology Space - In one embodiment, a set of target search terms for a search is received. Candidate terms are selected, where a candidate term is selected to reduce an ontology space of the search. The candidate terms are to a computer to recommend the candidate terms as search terms. In another embodiment, a document stored in one or more tangible media is accessed. A set of target tags for the document is received. Terms are selected, where a term is selected to reduce an ontology space of the document. The terms are sent to a computer to recommend the terms as tags. | 04-09-2009 |
20090094021 | Determining A Document Specificity - In one embodiment, determining a document specificity includes accessing a record that records the clusters of documents. The number of themes of a document is determined from the number of clusters of the document. The specificity of the document is determined from the number of themes. | 04-09-2009 |
20090094207 | Identifying Clusters Of Words According To Word Affinities - In one embodiment, identifying clusters of words includes accessing a record that records affinities. An affinity between a first and second word describes a quantitative relationship between the first and second word. Clusters of words are identified according to the affinities. A cluster comprises words that are sufficiently affine with each other. A first word is sufficiently affine with a second word if the affinity between the first and second word satisfies one or more affinity criteria. A clustering analysis is performed using the clusters. | 04-09-2009 |
20090094208 | Automatically Generating A Hierarchy Of Terms - In certain embodiments, generating a hierarchy of terms includes accessing a corpus comprising terms. The following is performed for one or more terms to yield parent-child relationships: one or more parent terms of a term are identified according to directional affinity; and one or more parent-child relationships are established from the parent terms and each term. A hierarchical graph is automatically generated from the parent-child relationships. | 04-09-2009 |
20090094209 | Determining The Depths Of Words And Documents - In one embodiment, determining a document depth includes accessing a record that describes documents. The record records affinities associated with the documents. A document depth for a document is determined from the affinities. A document depth analysis may be performed using the document depth. In one embodiment, determining a word depth includes accessing a record that describes the affinities of words. A word depth is determined for a word from the affinities. | 04-09-2009 |
20090094231 | Selecting Tags For A Document By Analyzing Paragraphs Of The Document - In one embodiment, assigning tags to a document includes accessing the document, where the document comprises text units that include words. The following is performed for each text unit: a subset of words of a text unit is selected as candidate tags, relatedness is established among the candidate tags, and certain candidate tags are selected according to the established relatedness to yield a candidate tag set for the text unit. Relatedness between the candidate tags of each candidate tag set and the candidate tags of other candidate tag sets is determined. At least one candidate tag is assigned to the document according to the determined relatedness. | 04-09-2009 |
20090094232 | Refining A Search Space In Response To User Input - In one embodiment, a search space of a corpus is searched to yield results. The corpus comprises documents associated with keywords, where each document is associated with at least one keyword indicating at least one theme of the document. One or more keywords are determined to be irrelevant keywords. The search space is refined according to the irrelevant keywords. | 04-09-2009 |
20090094233 | Modeling Topics Using Statistical Distributions - In one embodiment, modeling topics includes accessing a corpus comprising documents that include words. Words of a document are selected as keywords of the document. The documents are clustered according to the keywords to yield clusters, where each cluster corresponds to a topic. A statistical distribution is generated for a cluster from words of the documents of the cluster. A topic is modeled using the statistical distribution generated for the cluster corresponding to the topic. | 04-09-2009 |
20090094262 | Automatic Generation Of Ontologies Using Word Affinities - In one embodiment, generating an ontology includes accessing an inverted index that comprises inverted index lists for words of a language. An inverted index list corresponding to a word indicates pages that include the word. A word pair comprises a first word and a second word. A first inverted index list and a second inverted index list are searched, where the first inverted index list corresponds to the first word and the second inverted index list corresponds to the second word. An affinity between the first word and the second word is calculated according to the first inverted index list and the second inverted index list. The affinity describes a quantitative relationship between the first word and the second word. The affinity is recorded in an affinity matrix, and the affinity matrix is reported. | 04-09-2009 |
20090171928 | Ranking Nodes for Session-Based Queries - In one embodiment, a method includes accessing a model of a set of nodes including a session node and multiple linked nodes linked to the session node. The linked nodes include parent nodes and child nodes. A parent node links one or more child nodes to the session node, and a child node has one or more parent nodes linking the child node to the session node. The method includes generating a probability distribution for the set of nodes that distributes probabilities to all linked nodes within a predetermined number of links from the session node. Each child node receives from each of its parent nodes a predetermined fraction of a probability distributed to the parent node, and the parent node uniformly distributes to each of its child nodes the predetermined fraction of the probability distributed to the parent node. | 07-02-2009 |
20090204609 | Determining Words Related To A Given Set Of Words - In one embodiment, display of a user entry window of a graphical user interface is initiated. Search terms entered into the user entry window to initiate a first search are received. One or more first search results from a corpus of documents are determined according to the search terms. Display of the search terms at a current search terms window of the graphical user interface is initiated. Display of the first search results at a search results window of the graphical user interface is initiated. Display of the first search suggestions at a search suggestion window of the graphical user interface is initiated. | 08-13-2009 |
20090259636 | Facilitating Display Of An Interactive And Dynamic Cloud Of Terms Related To One Or More Input Terms - According to certain embodiments, facilitating display of terms includes facilitating display of a graphical user interface. One or more first input terms entered into a user entry window of the graphical user interface are received. One or more first output terms related to the first input terms are determined. Display of a first graphical cloud comprising the first output terms is facilitated. The first input terms are modified to yield one or more second input terms. One or more second output terms related to the second input terms are determined. Display of a second graphical cloud comprising the second output terms is facilitated. | 10-15-2009 |
20100036835 | Caching Query Results with Binary Decision Diagrams (BDDs) - Construct a plurality of first binary decision diagrams (BDDs), each representing a different one of a plurality of words. Construct a plurality of second BDDs, each representing a different one of a plurality of search queries, each of the search queries comprising one or more of the words. Construct a plurality of third BDDs, each representing a different one of a plurality of web pages. Construct a plurality of fourth BDDs, each representing a different one of a plurality of search results, each search result comprising one or more web pages. Construct a plurality of fifth BDDs each representing a different one of a plurality of search tuples, each of the search tuples comprising a different one of the search queries and a different one of the search results. Construct a sixth BDD representing the search queries and the search results. | 02-11-2010 |
20100125809 | Facilitating Display Of An Interactive And Dynamic Cloud With Advertising And Domain Features - According to certain embodiments, display of a graphical cloud of a graphical user interface is facilitated. The graphical cloud comprises a user entry field and a domain interface. A set of input terms entered into the user entry field are received. A selected domain entered into the domain interface is received. One or more output terms related to the input terms and specific to the selected domain are determined. Display of the graphical cloud comprising the output terms is facilitated. | 05-20-2010 |
20100211534 | Efficient computation of ontology affinity matrices - In one embodiment, generating an ontology includes accessing an inverted index comprising a plurality of inverted index lists. An inverted index list may correspond to a term of a language. Each inverted index list may comprise a term identifier of the term and one or more document identifiers indicating one or more documents of a document set in which the term appears. The embodiment also includes generating a term identifier index according to the inverted index. The term identifier index comprises a plurality of sections and each section corresponds to a document. Each section may comprise one or more term identifiers of one or more terms that appear in the document. | 08-19-2010 |
20100217742 | Generating A Domain Corpus And A Dictionary For An Automated Ontology - According to one embodiment, generating a domain corpus includes accessing a knowledge base. The knowledge base comprises a set of articles. Each article corresponds to a particular topic and comprises one or more terms that link to other articles corresponding to other topics. A first set of first articles is selected from the knowledge base for a domain corpus. A second set of second articles related to the first set of first articles is identified. The second set of second articles is selected from the knowledge base for the domain corpus. The domain corpus is made available to access. | 08-26-2010 |
20100217764 | Generating A Dictionary And Determining A Co-Occurrence Context For An Automated Ontology - According to one embodiment, generating a dictionary and determining a co-occurrence context includes accessing a domain corpus comprising articles. Each article corresponds to a particular topic and comprises one or more terms that link to one or more other articles corresponding to one or more other topics. Each topic is designated as a term to yield a dictionary. A co-occurrence context is defined for the domain corpus. At least two terms appearing in the co-occurrence context are considered co-occurring. Co-occurrences among the terms are calculated according to the co-occurrence context. | 08-26-2010 |