US20080306918A1 - System and method for wikifying content for knowledge navigation and discovery - Google Patents

System and method for wikifying content for knowledge navigation and discovery Download PDF

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US20080306918A1
US20080306918A1 US12/078,474 US7847408A US2008306918A1 US 20080306918 A1 US20080306918 A1 US 20080306918A1 US 7847408 A US7847408 A US 7847408A US 2008306918 A1 US2008306918 A1 US 2008306918A1
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computer
concepts
causing
concept
code means
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Albert Mons
Nickolas Barris
Christine Chichester
Barend Mons
Erik Van Mulligen
Marc Weeber
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Knewco Inc
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Knewco Inc
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Assigned to KNEWCO, INC. reassignment KNEWCO, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: MONS, ALBERT, BARRIS, NICKOLAS, MONS, BAREND, MULLIGEN, ERIK VAN, WEEBER, MARC, CHICHESTER, CHRISTINE
Publication of US20080306918A1 publication Critical patent/US20080306918A1/en
Priority to PCT/US2009/038378 priority patent/WO2010011372A1/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00—Data processing: database and file management or data structures
    • Y10S707/99931—Database or file accessing
    • Y10S707/99933—Query processing, i.e. searching
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00—Data processing: database and file management or data structures
    • Y10S707/99941—Database schema or data structure
    • Y10S707/99943—Generating database or data structure, e.g. via user interface

Definitions

  • the present invention generally relates to systems and methods for intellectual networking, and more particularly to systems and methods for navigating among the concepts found in the large amounts of data produced by intellectuals in order to facilitate the knowledge discovery process.
  • PubMed which uses a Boolean model.
  • the query above would be transformed to something like “lung cancer AND treatment.”
  • PubMed offers much refinement using keyword searching, it is still vulnerable to the typical disadvantages of Boolean searching: highly specific queries such as “papers AND discuss AND new treatments AND lung cancer” will typically yield results ranging from few to none.
  • the results adhere to the word based and Boolean queries, and rank ordering the results based on relevance is typically not possible.
  • both the documents in a collection and the queries are represented by a vector of the most important words (i.e., keywords) in the text.
  • the vector ⁇ papers, discuss, new treatments, lung cancer ⁇ represents the query above.
  • Numeric values representing importance are assigned.
  • angles between query and document vectors are typically computed. The smaller the angle between two vectors, the more similar these vectors are, or, in other words, the more similar or associated a document is to the query.
  • the result of a vector space query is a list of documents that are similar in vector space.
  • the first major improvement over Boolean systems is that the results can be rank-ordered. Thus, the first result is typically more relevant to the query than the last.
  • the second major improvement is that even if not all words from the query are in any one document, in most cases the system will still return relevant results. Generally, the more refined and extensive a query is, the more refined the results are.
  • a biochemical reaction involves not only different reactants, but often also a mediator molecule (i.e., a catalyst). Further, such reactions are often localized to specific cells, and even to specific parts of a cell. Extraction algorithms would first search for the part in the text that mentions one or more of the reactants then attempt to fill in the template by, for example, interpreting the name of a cell type as the location of the reaction. In many cases, advanced Natural Language Processing (NLP) techniques are needed as it is important not to interchange the subject and the object. Also, semantic analysis to extract the actual meaning is needed.
  • NLP Natural Language Processing
  • Swanson's first discovery for example, was that Eskimos have a fish-rich diet, and the intake of fatty acids in fish oils (A) is known to lower blood platelet aggregation and blood viscosity (B). Eskimos have therefore a lower incidence of different heart-related diseases.
  • A fish oils
  • B blood viscosity
  • C blood viscosity
  • Swanson D. R. “Fish Oil, Raynaud's Syndrome, and Undiscovered Public Knowledge,” Perspectives in Biology and Medicine, 1986; 30:7-18, the entirety of which is incorporated by reference herein.
  • Another approach to hypothesizing novel relationships from existing data is to employ standard IR tools.
  • the key issue here is that a transformation is needed from a document world to an “object” world.
  • An object can be anything that represents a concept or real-world entity.
  • documents describing a certain disease may be combined or clustered into a format that is typical for that disease.
  • the vector space model for example, can easily accommodate this transformation.
  • the vectors of the documents describing the disease can be combined into one vector representing the disease.
  • collections of documents may be transformed into collections of diseases, drug, genes, proteins, etc.
  • discovery comprises finding objects associated with the query object in the vector space.
  • the rank-ordered result of the query will contain not only drugs that have been mentioned together with lung cancer, but also drugs that have never been studied in this disease's context, which may be hypothetical new treatments for lung cancer.
  • a query using a vector representing Raynaud's disease in an object database storing chemicals and drugs will result in both existing treatments and potentially new treatments (such as fish oil).
  • An important aspect of this “object” approach is that a search with any kind of object may be conducted, and any other kind of object may be requested.
  • the life cycle of a scientific project starts with the birth of an idea, which may be a well-defined hypothesis or just a hunch, by one or more scientists.
  • the idea often follows from previous experimental outcomes that are combined with reported knowledge and novel hypotheses.
  • the challenge of today's data and knowledge deluge is to optimally combine the widely varying sources of information and knowledge to select only the most promising hypotheses.
  • Such data structures, systems, methods and computer program products should allow vast data stores to be semantically searched, navigated, compressed and stored in order to facilitate relational, associative and/or other types of knowledge discovery.
  • aspects of the present invention meet the above-identified needs by providing enhanced systems, methods and computer program products for knowledge navigation and discovery, particularly within the context of intellectual networking sites.
  • the data structures, systems, methods and computer program products for facilitating knowledge navigation and discovery are independent of choice of language and other concept representations.
  • every concept in a thesaurus or ontology, or a collection thereof is assigned a unique identifier.
  • Two basic types of concepts are defined: (a) a source concept, corresponding to a query; and (b) a target concept, corresponding to a concept having some relationship with the source concept.
  • Each concept, identified by its unique identifier is assigned minimally three attributes: (1) factual; (2) co-occurrence; and (3) associative values.
  • the source concept with all its associated (target) concepts that relate to the source concept with one or more of the attributes is stored in a novel data structure referred to as a “KnowletTM”.
  • a data structure is a way of storing data in a computer so that it can be used efficiently. Often a carefully chosen data structure will allow the most efficient algorithm to be used.
  • a well-designed data structure allows a variety of critical operations to be performed, using as few resources, both in terms of execution time and memory space, as possible. Data structures are implemented using data types, references and operations on them provided by a programming language.)
  • the factual attribute, F is an indication of whether the concept has been mentioned in authoritative databases (i.e., databases or other repositories of data that have been deemed authoritative by the scientific community in a given area of science and/or other area of human endeavor).
  • the factual attribute is not, in and of itself, an indication of the veracity or falsehood of the source and target concepts relationship.
  • the co-occurrence attribute, C is an indication of whether the source concept has been mentioned together with the target concept in a unit of text (e.g., in the same sentence, in the same paragraph, in the same abstract, etc.) within a database or other data store or repository that have not been deemed authoritative. Again, the co-occurrence attribute is not, in and of itself, an indication of the veracity or falsehood of the concepts relationship.
  • the associative attribute, A is an indication of conceptual overlap between the two concepts.
  • the Knowlet with its three F, C, and A attributes represents a “concept cloud.” When an interrelation is created among the concept clouds of all identified concepts, a “concept space” is created. It should be noted that the Knowlets and their respective F, C, and A attributes are periodically updated (and may be changed), as databases and other repositories of data are populated with new information. The collection of Knowlets and their respective F, C, and A attributes are then stored in a knowledge database.
  • the data structure, system, method and computer program product for knowledge navigation and discovery utilize an indexer to index a given source (e.g., textual) of knowledge using a thesaurus (also referred to as “highlighting on the fly”).
  • a matching engine is then used to create the F, C, and A attributes for each Knowlet.
  • a database stores the Knowlet space.
  • the semantic associations between every pair of Knowlets/concepts are calculated based on the F, C, and A attributes for a given concept space.
  • the Knowlet matrix and the semantic distances may be used for meta analysis of entire fields of knowledge, by showing possible associations between concepts that were previously unexplored.
  • An advantage of aspects of the present invention is that it can be provided as a research tool in the form of a Web-based or proprietary search engine, Internet browser plug-in, Wiki, or proxy server.
  • Another advantage of aspects of the present invention is that it allows users not only to make new (relational and associative) discoveries using concepts, but also allows such users to find experts related to a concept using authorship information located in the data store.
  • Another advantage of aspects of the present invention is that it uses a novel data structure called a “Knowlet” which allows scientists to make new (relational and associative) discoveries using concepts (and their automatically included synonyms) from a data store and a relevant (e.g., biomedical) ontology or thesaurus.
  • a relevant e.g., biomedical
  • Knowlets enable precise information retrieval and extraction as well as relational and associative discovery and can be applied to any collection of content in any discipline at any level of scientific detail and explanation.
  • Yet another advantage of aspects of the present invention is that redundancy from the World Wide Web, or any other data store, may be removed without losing unique information bits, thereby resulting in a compressed or “zipped” version of the Web that may be more easily stored, searched and shared.
  • Yet another advantage of aspects of the present invention is that it allows more complex (and thorough) Internet search queries to be automatically built during concept browsing than can ever be crafted by humans.
  • Yet another advantage of aspects of the present invention is that it allows public data stores and authoritative ontologies or thesauri, to be augmented by private data stores and ontologies or thesauri thereby allowing for a more complete concept space and thus more knowledge navigation and discovery capabilities.
  • Yet another advantage of aspects of the present invention is that it allows users to more easily identify experts related to particular concepts for collaborative research purposes.
  • FIG. 1 is a system diagram of an exemplary environment, in which the present invention, in one aspect, may be implemented.
  • FIG. 2 is a block diagram of an exemplary computer system useful for implementing the present invention.
  • FIG. 3 is a flowchart depicting an exemplary Knowlet space creation and navigation process according to an aspect of the present invention.
  • FIG. 4 is a block diagram depicting an exemplary composition of a Knowlet data structure according to an aspect of the present invention.
  • FIGS. 5A & 5B are flowcharts depicting an exemplary login process according to an aspect of the present invention.
  • FIG. 6 is a flowchart depicting an exemplary Wikifier functionality according to an aspect of the present invention.
  • FIG. 7 is a flowchart depicting an exemplary click and link functionality according to an aspect of the present invention.
  • FIGS. 8A & 8B are flowcharts depicting an exemplary Wikifier functionality according to an aspect of the present invention.
  • FIGS. 9-28 are exemplary windows or Graphic User Interface (GUI) screens generated by aspects of the graphical user interface of the present invention.
  • GUI Graphic User Interface
  • aspects of the present invention are directed to systems, methods and computer program products for knowledge navigation and discovery within the context of intellectual networking sites.
  • an automated tool is provided to users, such as biomedical research scientists, to allow them to navigate, search and perform knowledge discovery within a vast data store, such as PubMed—one of the most-widely used biomedical bibliographic databases which is maintained and provided by the U.S. National Library of Medicine. PubMed includes over 17 million abstracts and citations of biomedical articles dating back to the 1950's. In such an aspect, the present invention does more than simply allow biomedical researchers to perform Boolean searches using keywords to find relevant articles.
  • one aspect of the present invention allows scientists to make new relational, associative and/or other discoveries using concepts or units of thought (which would automatically include all synonyms of a concept expressed in a given language) from a data store and a relevant (e.g., biomedical) ontology or thesaurus, such as the United States National Library of Medicine's Unified Medical Language System® (UMLS) databases that contain information about biomedical and health related concepts.
  • a relevant e.g., biomedical
  • UMLS Unified Medical Language System®
  • FIG. 1 presents an exemplary system diagram 100 of various hardware components and other features in accordance with an aspect of the present invention.
  • data and other information and services for use in the system is, for example, input by a user 101 via a terminal 102 , such as a personal computer (PC), minicomputer, laptop, palmtop, mainframe computer, microcomputer, telephone device, mobile device, personal digital assistant (PDA), or other device having a processor and input and display capability.
  • a terminal 102 such as a personal computer (PC), minicomputer, laptop, palmtop, mainframe computer, microcomputer, telephone device, mobile device, personal digital assistant (PDA), or other device having a processor and input and display capability.
  • PC personal computer
  • minicomputer laptop, palmtop, mainframe computer
  • microcomputer telephone device
  • mobile device mobile device
  • PDA personal digital assistant
  • the terminal 102 is coupled to a server 106 , such as a PC, minicomputer, mainframe computer, microcomputer, or other device having a processor and a repository for data or connection to a repository for maintaining data, via a network 104 , such as the Internet, via communication couplings 103 and 105 .
  • a server 106 such as a PC, minicomputer, mainframe computer, microcomputer, or other device having a processor and a repository for data or connection to a repository for maintaining data, via a network 104 , such as the Internet, via communication couplings 103 and 105 .
  • a service provider may allow access, on a free registration, paid subscriber and/or pay-per-use basis, to the knowledge navigation and discovery tool via a World-Wide Web (WWW) site on the Internet 104 .
  • WWW World-Wide Web
  • system 100 is scaleable such that multiple users, entities or organizations may subscribe and utilize it to allow their users 101 (i.e., their scientists, researchers, authors and/or the public at large who wish to perform research) to search, submit queries, review results, and generally manipulate the databases and tools associated with system 100 .
  • alternate aspects of the present invention may include providing the tool for knowledge navigation and discovery as a stand-alone system (e.g., installed on one PC) or as an enterprise system wherein all the components of system 100 are connected and communicate via a secure, inter-corporate, wide area network (WAN) or local area network (LAN), rather than as a Web service as shown in FIG. 1 .
  • WAN wide area network
  • LAN local area network
  • GUI screens may be generated by server 106 in response to input from user 101 over the Internet 104 .
  • server 106 is a typical Web server running a server application at a Web site which sends out Web pages in response to Hypertext Transfer Protocol (HTTP) or Hypertext Transfer Protocol Secured (HTTPS) requests from remote browsers being used by users 101 .
  • HTTP Hypertext Transfer Protocol
  • HTTPS Hypertext Transfer Protocol Secured
  • server 106 (while performing any of the steps of process 300 described below) is able to provide a GUI to users 101 of system 100 in the form of Web pages.
  • These Web pages sent to the user's PC, laptop, mobile device, PDA or the like device 102 and would result in GUI screens (e.g., screens in FIGS. 9-28 ) being displayed.
  • Knowlet a novel data element or structure called a “Knowlet” is employed to enable lightweight storage, precise information retrieval and extraction as well as relational, associative and/or other discovery. That is, each concept in a relevant ontology or thesaurus (in any discipline at any level of scientific detail) may be represented by a Knowlet such that it is a semantic representation of the concept, resulting from a combination of factual information extraction, co-occurrence based connections and associations (e.g., vector-based) in a concept space.
  • the factual (F), the textual co-occurrence (C), as well as the associative (A) attributes or values between the concept in question and all other concepts in the relevant ontology or thesaurus, and with respect to one or more relevant data stores, are stored in the Knowlet for each individual concept.
  • the Knowlet can take the form of a Zope (an open-source, object-oriented web application server written in the Python programming language distributed under the terms of the Zope Public License by the Zope Corp. of Fredericksburg, Va.) data element that stores all forms of relationships between a source concept and all its target concepts, including the values of the semantic associations to such target concepts).
  • Zope an open-source, object-oriented web application server written in the Python programming language distributed under the terms of the Zope Public License by the Zope Corp. of Fredericksburg, Va.
  • semantic distance is the distance or proximity between two concepts in a defined concept space, which can differ based on which data store or repository of data (i.e., collection of documents) used to create the concept space, but also based on the matching control logic used to define the matching between the two concepts, and the relative weight given to factual (F), co-occurrence (C) and associative (A) attributes.
  • F factual
  • C co-occurrence
  • A associative
  • aspects of the present invention seek to apply this power of vast and diverse elements of human thought to data stores or repositories of data. Given the above, aspects of the present invention are able to “overlay” concepts within a given text with factual, co-occurrence and associative attributes, for example. It will be recognized by those of ordinary skill in the art, however, that any number of attributes may be used, as long as these attribute(s) represent a relationship that may link a given concept with another concept.
  • Computer program listing Appendix 1 presents an XML representation of an exemplary Knowlet according to an aspect of the present invention.
  • Knowlets can be exported into standard ontology and Web languages such as the Resource Description Framework (RDF) and the Web Ontology Language (OWL). Therefore, any application using such languages may be enabled to use the Knowlet output of the present invention for reasoning and querying with programs such as the SPARQL Protocol and RDF Query Language.
  • RDF Resource Description Framework
  • OWL Web Ontology Language
  • a search tool is provided to user 101 for knowledge navigation and discovery.
  • an automated tool is provided to users, such as biomedical research scientists, to allow them to navigate, search and perform knowledge discovery within a vast data store, such as PubMed.
  • Process 300 begins at step 302 with control passing immediately to step 304 .
  • step 304 connects system 100 to one or more data stores (e.g., PubMed) containing the knowledge base in which the user seeks to navigate, search and discover.
  • PubMed data stores
  • step 306 connects the system to one or more ontologies or thesauri relevant to the data store(s).
  • the ontology may be one or more of the following ontologies, among others: the UMLS (as of 2006, the UMLS contained well over 1,300,000 concepts); the UniProtKB/Swiss-Prot Protein Knowledgebase, an annotated protein sequence database established in 1986; the IntAct, a freely available, open source database system for protein interaction data derived from literature curation or direct user submissions; the Gene Ontology (GO) Database, an ontology of gene products described in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner; and the like.
  • the UMLS as of 2006, the UMLS contained well over 1,300,000 concepts
  • the UniProtKB/Swiss-Prot Protein Knowledgebase an annotated protein sequence database established in 1986
  • the IntAct a freely available, open source database system for protein interaction data derived from literature curation or direct user submissions
  • aspects of the present invention are language-independent, and each concept may be given a unique numerical identifier and synonyms (whether in the same natural language, jargon or in different languages) of that concept would be given the same numerical identifier. This helps the user navigate, search and perform discovery activities in a non-language specific (or dependent) manner.
  • step 308 goes through each record of the data store (e.g., go through each abstract of the PubMed database), tags the concepts from the ontology (e.g., ULMS) that appear in each record, and builds an index recording the locations where each concept is found in each record (e.g., each abstract in PubMed).
  • the index built in step 308 is accomplished by utilizing an indexer (sometimes referred to as a “tagger”) which are known in the relevant art(s).
  • the indexer is a named entity recognition (NER) indexer (which utilizes the one or more ontologies or thesauri relevant to the data store(s) loaded in step 306 ) such as the Peregrine indexer developed by the Biosemantics Group, Medical Informatics Department, Erasmus University Medical Center, Rotterdam, The Netherlands; and described in Schuemie M., Jelier R., Kors J., “Peregrine: Lightweight Gene Name Normalization by Dictionary Lookup” Proceedings of Biocreative 2 , which is hereby incorporated by reference in its entirety.
  • NER named entity recognition
  • NER indexers examples include: the ClearForest Tagging Engine available from Rueters/ClearForest of Waltham, Mass.; the GENIA Tagger available from the Department of Information Science, Faculty of Science, University of Tokyo; the iHOP service available from http://www.ihop-net.org; IPA available from Ingenutity Systems of Redwood City, Calif.; Insight DiscovererTM Extractor available from Temis S.A. of Paris, France; and the like.
  • step 310 creates a Knowlet for each concept in the ontology which “records” the relationship between that concept and all other concepts (as well as semantic distances/associations) within the concept space.
  • a search engine such as the Lucene Search Engine, may be used to search the data store(s) for the occurrences of the concepts loaded into the system in step 306 and to determine the relationships between the concepts using the index created in step 308 .
  • the Lucene Search Engine used in this example, is available under the Apache Software Foundation License and is a high-performance, full-featured text search engine library written in Java suitable for nearly any application that requires full-text (especially cross-platform) search.
  • step 312 creates and stores within the system (e.g., storing within a data store associated with server 106 ) a “Knowlet space” (or concept space), which is a collection of all the Knowlets created in step 310 , thus forming a larger, dynamic ontology.
  • the Knowlet space may be (at most) a [N] ⁇ [N ⁇ 1] ⁇ [3] matrix detailing how each of N concepts relates to all other N ⁇ 1 concepts in a Factual (F), Co-occurrence and (C) Associative (A) manner.
  • step 312 includes the steps of calculating the F, C and A attributes (or values) for each concept pair.
  • the Knowlet space is a virtual concept space based on all Knowlets, where each concept is the source concept for its own Knowlet and a target concept for all other Knowlets. (When the F, C or A values are non-zero within a Knowlet for a particular source/target concept combination, this is denoted herein as being in a F+, C+ or A+state, respectively. And, when the values are less than or equal to zero, they are denoted as F ⁇ , C ⁇ or A ⁇ , respectively.)
  • N may be well over 1,000,000 in magnitude.
  • the Knowlet space may be represented as an [N] ⁇ [N ⁇ 1] ⁇ [Z] matrix detailing how each of N concepts relates to all other N ⁇ 1 concepts with respect to each of Z attributes.
  • step 312 would include the steps of calculating Z number of attributes (or values) for each concept pair.
  • the Knowlet space may be made smaller (and thus optimized for computer memory storage and processing) than a [N] ⁇ [N ⁇ 1] ⁇ [Z] matrix by reducing the [N ⁇ 1] portion of the Knowlet.
  • This is accomplished by a scheme where each concept is the source concept for its own Knowlet, and only those subset of N ⁇ 1 target concepts where any of the Z attribute values (e.g., the F, C and A values) are positive are included as target concepts in the source concept's Knowlet.
  • the F value may be determined, for example, by factual relationships between two concepts as determined by analyzing the data store.
  • ⁇ noun> ⁇ verb> ⁇ noun> (or ⁇ concept> ⁇ relation> ⁇ concept>) triplets are examined to deduce factual relationships (e.g., “malaria”, “transmitted” and “mosquitoes”).
  • factual relationships e.g., “malaria”, “transmitted” and “mosquitoes”.
  • the F value may be, for example, either zero (no factual relationship) or one (there is a factual relationship), depending on the search of the one or more data stores loaded in step 304 .
  • the factual F value is zero or one, in one aspect of the present invention, it will be recognized by those of ordinary skill in the art that the factual attribute F may be influenced by taking into account one or more weighting factors, such as the semantic type(s) of the concepts, for example, as defined in the thesaurus. For example, a more meaningful relationship is presented by ⁇ gene> and ⁇ disease>, than by ⁇ gene> and ⁇ pencil>, which may in turn influence the F value.
  • the F value is determined by the existence (or nonexistence) of factual relationships in authoritative data sources accepted by the scientific community in a given area, such as PubMed.
  • the F value is not an indication of the veracity or authenticity of the concept or relationship, and that it may be determined based on other factors.
  • repetition of facts is of great value for the readability of individual text (e.g., articles) in the data store, but the fact itself is a single unit of information, and needs no repetition within the Knowlet space.
  • the C value is determined by the co-occurrence relationship between two concepts, determined by whether they appear within the same textual grouping (e.g., per sentence, per paragraph, or per x number of words).
  • the C value may range from zero to 0.5 based on the number of times a co-concurrence of the two concepts is found within the data store(s).
  • a co-occurrence may be determined by taking into account one or more weighting factors, such as the semantic type(s) of the concepts in the data store.
  • the C value may therefore be influenced by, for example, one or more weights.
  • the A value is determined by the associative relationship between two concepts.
  • the A value may range from zero to 0.4 depending on the outcome of a multidimensional scaling process in a cluster of concepts (i.e., n-dimensional space), which explores similarities or dissimilarities in the data store between the two concepts.
  • the A value is an indication of conceptual overlap between two concepts. In one example, the closer the two concepts are in the multidimensional cluster of concepts, the higher the associative value A between them will be. If there is little or no conceptual overlap, the associative value A will be closer to zero.
  • a concept profile is constructed as follows: For each concept found in the data store(s) loaded into system 100 , a number of records are retrieved in which that specific concept has a significant incidence. In certain aspects, high precision may be favored at the expense of (IR) recall.
  • a list is thus constructed such that concepts from minimally one, but up to a pre-defined threshold (e.g., 250), selected records within the data store (e.g., abstracts in PubMed) that are “about” that source concept.
  • a ranked concept lists is then constructed by terminology-based, concept-indexing of the entire returned record (e.g., a PubMed abstract), followed by weighted aggregation into one list of concepts.
  • the concepts in this list exhibit a high association with the source concept.
  • These lists can now be expressed as vectors in multidimensional space and the associative score (A), for each of the vector pairs, is calculated. This associative score is recorded as a value between 0 and 1 in the A category of the Knowlet.
  • Thresholds can be calculated by comparing the distribution concept profile matches of non-related concepts of certain semantic types with those that are known to interact (e.g., all proteins that are not known to interact with those that are known to interact in Swiss-Prot and IntAct).
  • the A parameter represents the most interesting aspect of the Knowlet (e.g., while using system 100 in a “discovery” mode as detailed below). As facts are moved from a C+ and F ⁇ state to an F+ state, the data store(s) loaded into system 100 become more factually solidified.
  • steps 304 - 312 may be periodically repeated so as to capture updates to the data store(s) (e.g., new abstracts in PubMed) and/or ontology(ies) (i.e., new concepts).
  • data store(s) e.g., new abstracts in PubMed
  • ontology(ies) i.e., new concepts
  • step 314 receives a search query from a user consisting of one or more source concepts (i.e., a selected concept taken as the starting point for knowledge navigation and discovery within the concept space).
  • source concepts i.e., a selected concept taken as the starting point for knowledge navigation and discovery within the concept space.
  • step 316 performs a lookup in the Knowlet space and calculates a semantic distance (SD) for all N ⁇ 1 potential target concepts relative to the source concept, and produces a set of target concepts (i.e., concepts in the concept space that have a relation to the source concept).
  • SD semantic distance
  • the system would return a set of target concepts corresponding to the 50 highest SD values calculated within the Knowlet space.
  • the semantic distance may be calculated:
  • w 1 , w 2 and w 3 are weights assigned to the F, C and A values, respectively.
  • users may be able to query the system in different modes which would then automatically adjust the w 1 , w 2 and w 3 values.
  • w 1 , w 2 and w 3 may be set to 1.0, 0.0 and 0.0, respectively.
  • w 1 , w 2 and w 3 may be set to 1.0, 0.5 and 2.0, respectively.
  • the F, C and A values may be weighted by different factors or characteristics (e.g., by semantic type) in different modes.
  • the SD or semantic association is the computed semantic relationship between a source concept and a target concept based on weighted factual, co-occurrence and associative information.
  • step 318 presents the target concepts to the user via GUI such that the user may view the source concept, the set of target concepts (color coded according to F, C, A and/or SD values) and the list of records within the data store(s) (i.e., the PubMed abstracts) which form the basis of the relationships for the SD calculations.
  • Process 300 then terminates as indicated by step 320 .
  • FIG. 4 a block diagram depicting an exemplary composition of a Knowlet data structure 400 , as produced by process 300 , according to an aspect of the present invention is shown.
  • any concept in the biomedical literature for instance a protein or a disease
  • a source concept can be treated as a source concept (depicted as a blue ball in FIG. 4 ).
  • authoritative databases such as UMLS or UniProtKB/Swiss-Prot concerning the concept and its factual relationships with other concepts. This information is captured and all concepts that have a “factual” relationship with the source concept in any of the participating databases are thus included in the Knowlet of that concept.
  • These “factually associated concepts” are depicted in the Knowlet visualization as solid green balls in FIG. 4 .
  • the source concept may be mentioned with other concepts in one and the same sentence in the literature.
  • the two concepts co-occur, there is a high chance for a meaningful, or even causal, relationship between the two concepts.
  • Most concepts that have a factual relationship are likely to be mentioned in one or more sentences in the literature at large, but as process 300 may have only mined one data store (e.g., PubMed), there might be many factual associations that are not easy to recover from such data store alone. For instance, many protein-protein interactions described in UniProtKB/Swiss-Prot cannot be found as co-occurrences in PubMed.
  • Target concepts which co-occur minimally once in the same sentence as the source concept are depicted as green rings in the visualization of the Knowlet in FIG. 4 .
  • the last category of concepts is formed by those that have no co-occurrence per unit of text (e.g., a sentence) in the indexed records of the data store, but have sufficient concepts in common with the source concepts in their own Knowlet to be of potential interest. These concepts are depicted as yellow rings in FIG. 4 and could represent implicit associations. Each source concept has a relationship of varying strength with other (target) concepts and each of these distances has been assigned with a value for Factual (F), Co-occurrence (C) and Associative (A) factors. The semantic association (or SD value) between each concept pair is computed based on these values.
  • F F
  • C Co-occurrence
  • A Associative
  • the user may enter two or more source concepts.
  • the system produces a set of target concepts which relate to all of the source concepts entered.
  • source concepts A and B may have no factual (F) or co-occurrence (C) relationships in the one or more data store(s) loaded into the system in step 304 .
  • a traditional search engine may yield no results while performing a traditional Boolean/keyword search.
  • the present invention is able to produce target concepts which associatively (A) link the source concepts A and B.
  • steps 308 and 310 described above can be augmented by also indexing the authors of the records in the data store (i.e., the authors of the publications whose abstracts appear in PubMed).
  • the authors of the records in the data store i.e., the authors of the publications whose abstracts appear in PubMed.
  • the universe of M authors are uniquely mapped to the N concepts such that the Knowlet space is now a [N+M] ⁇ [N+M ⁇ 1] ⁇ 3 matrix (i.e., a concept space where each concept has a Knowlet and each author has a Knowlet).
  • a concept space where each concept has a Knowlet and each author has a Knowlet.
  • the universe of M authors are uniquely mapped to the N concepts such that the Knowlet space is a [N+M] ⁇ [N+M ⁇ 1] ⁇ 3 matrix (provided the number of Z attributes is three)
  • many useful tools can be presented to users of system 100 .
  • various contribution factors may be calculated for each of the M authors who appear in the data store(s) loaded into the system in step 304 . The contribution factors would distinguish between those authors who were simply prolific (i.e., had a large number of publications) and those who were “innovative” (i.e., those authors whose works were responsible for two concepts co-occurring for the first time within the Knowlet space).
  • contribution factors may be calculated in a number of ways given the Knowlet space and the F, C and A parameters stored therein (e.g., the contribution factor may be based upon a per sentence, per article, or other basis). Contribution factors may also be calculated based on a sentence, sentences, an abstract or document, or a publication in general.
  • any images found within the data store(s) loaded into the system in step 304 may be associated with any of the N concepts during step 308 . These images would then be indexed and referenced within the Knowlet space and utilized as another data point (or field) upon which the tool to navigate, search and perform discovery activities described herein may operate.
  • two separate Knowlet (or concept) spaces resulting from parallel set of steps 304 - 312 described above may be compared and searched to aid in the knowledge navigation and discovery process. That is, a Knowlet space created using a database and ontology from a first field of study may be compared to a second Knowlet space created using a database and ontology from a second (e.g., related) field of study.
  • the present invention may provide an indication, based on the Knowlet space, that one or more relevant results may be found in the Knowlet space derived from another ontology or thesaurus.
  • the tool to navigate, search and perform discovery activities may be provided in an enterprise fashion for use by an authorized set of users (e.g., research scientists within the R&D department of a for-profit entity, research scientists within a university, and the like).
  • the one or more (public) data stores loaded into the system can be augmented by one or more proprietary data stores (e.g., internal, unpublished R&D) and/or the one or more (public) ontologies or thesauri loaded into the system can be augmented by one or more proprietary ontologies or thesauri.
  • the combination of public and private data allows for a more complete (and, if desired, proprietary) concept space and thus more knowledge navigation and discovery capabilities.
  • the one or more private data stores loaded into the system may be unpublished articles by authors within the enterprise. This would allow users within the enterprise, for example, to capture and recognize, for example, new co-occurrences within the Knowlet space before the publication goes to print.
  • the tool to navigate, search and perform discovery activities may offer users one or more security options.
  • a Knowlet space created through the use of one or more proprietary data stores e.g., internal, unpublished R&D
  • one or more proprietary ontologies or thesauri may be stored within system 100 in an encrypted manner during step 312 .
  • an encryption process may be applied to the Knowlet space such that only those with a decoding key (i.e., authorized users) may decrypt the Knowlet space.
  • the tool for navigating, searching and performing knowledge discoveries may be used to select and/or categorize the output of Internet search engines “on the fly.”
  • the output of the search engine may be sorted and categorized, by URL, into folders in a data repository, for example, within the plug-in itself.
  • the present invention in one aspect, may create a user's interest profile.
  • step 318 presents the target concepts to the user via a GUI such that the user may view the source concept, a wiki containing the definition of the source concept, and the set of target concepts.
  • the user may edit the definition of the source concept in one or more of the displayed wikis (based on their observations of the target concepts and the list of records within the data store(s) which form the basis of the relationships for the SD calculations).
  • a button on a tool bar or pull-down menu may be provided to serve as a “newness indicator.” That is, as a user browses the Internet and comes across a Web page of interest, the user may click a “newness” button on a tool bar or pull-down menu provided by the present invention which would then parse through the HTML code of the active Web page “on the fly” and grey-out (e.g., show in grey) all the concepts found in the user's personal Knowlet space.
  • the user's attention would be directed to the text on the Web page which actually represents “new” knowledge with respect to the user (i.e., knowledge gained from documents already read by the user would appear in grey or any other desired color, which would be in contrast to the remaining text, the color or other attributes of which would not be modified).
  • the tool to navigate, search and perform discovery activities may be provided via a proxy server such that a user's “favorite” or “bookmarked” Web sites are pre-parsed.
  • the user's browser would highlight (e.g., show in yellow) all the concepts found in the one or more ontologies or thesauri loaded in step 306 above without any manual intervention (i.e., without having to activate a “wikifier” button or menu option).
  • the tool to navigate, search and perform knowledge discovery may be provided as a word processing/text editing plug-in or add-on. That is, as a user edits a wiki displayed along with the target concepts (as described above) or authors a new paper, the one or more ontologies or thesauri relevant the Knowlet space loaded into the system in step 306 above may be periodically consulted. Such a plug-in or add-on would recognize any of the N concepts as they are being typed by the user, and then make “on the fly” suggestions as to as synonyms, homonyms, translations and/or connected concepts thus functioning as a “Do you mean [list of n suggested concepts]?” tool.
  • the plug-in or add-on may allow displaying and/or changing the status of a concept in real time. For example, an indication may be provided regarding, among other factors, whether a concept of interest is appropriately defined and whether it is translated in one or more languages, thus providing an on-line “on the fly” concept status report.
  • Web 1.0 refers to the state of the World Wide Web between approximately 1994 and 2004. Such state was a “read-only” state where most sites were one-way, published media (i.e., text and pictures).
  • Web 2.0 was coined circa 2004 (and which has very loosely defined boundaries) to refer to the evolution of the Web to a “read-and-write” state. That is, Web 2.0 reflects the Web-based communities and hosted services such as social-networking sites, wikis, blogs, and folksonomies, which aim to facilitate creativity, collaboration and sharing among users.
  • aspects of the present invention facilitate a “semantic Web” (i.e., a Web 3.0 state) where a dynamic, interactive Web of concepts (or “Concept Web”) and their relationships derived from the World Wide Web and off-line resources, where both redundancy and ambiguity have been removed.
  • a “semantic Web” i.e., a Web 3.0 state
  • Concept Web a dynamic, interactive Web of concepts
  • the first premise for the Concept Web is that a user/researcher performing an Internet search is not interested in data and information per se, but in a synthesis of these “building blocks” into executable knowledge upon which they can act.
  • This premise holds, for example, when the user is looking for the “best hotel in Amsterdam,” all the way through to a highly complicated biological pathway. Such user is not interested in all information about all hotels in Amsterdam, nor can they read all 5000 scientific papers referring to all 50 genes in a hypothetical pathway. Instead, the user is really interested in making a decision where to stay in Amsterdam or which gene to postulate as causing a given disorder.
  • the Concept Web according to aspects of the present invention, enables just that desired outcome while reducing the interim need for reading and analyzing to a bare minimum, and without losing crucial information and trust.
  • the “ambiguity problem” with respect to pages of text on the Internet refers to the property of words, terms, notations, signs, symbols and concepts within a particular context as being undefined, indefinable, multi-defined or without an obvious definition, and thus having a misleading, or unclear, meaning.
  • the “size problem” with respect to pages of text on the Internet refers to the fact that most recent (2007) estimates of Web pages on the Internet are at 500 billion Web pages, spread over more than 100 million Web sites.
  • aspects of the present invention may further include emerging disambiguation techniques to optimally reduce ambiguity.
  • the attributes and values of the relationships change based on multiple instances of factual statements, increasing co-occurrence or associations. This approach results in a minimal growth of the Concept Web space as compared to the text space.
  • a “zipping of the Web” i.e., a compression
  • two separate Knowlet (or concept) spaces resulting from parallel sets of steps 304 - 312 described above may be compared and searched to aid in the knowledge navigation and discovery process. That is, a Knowlet space created using a database and ontology from a first field of study may be compared to a second Knowlet space created using a database and ontology from a second field of study.
  • aspects of the present invention described above which result in a “zipping of the Web” may be utilized to compare two or more zipped datasets at the concept level.
  • an intellectual networking site with additional functionality is provided to further assist in the knowledge navigation and discovery processes.
  • Process 500 begins at step 502 with control passing immediately to step 504 .
  • each person within a field of interest e.g., each of the M authors within the one or more data stores, for example, PubMed, as loaded into system 100 in step 304
  • a WikiID a static, unique identifier
  • a personal Web page or “homepage” is then created in step 506 within an intellectual networking Web site community.
  • the homepage contains the author's (or expert's) name, including alternate spellings or common misspellings of their name, and curriculum vitae-related information (e.g., contact information, personal information, employment history, education, publications, professional qualifications, awards, professional memberships, conferences attended, interests, active projects, patents, and the like) and be accessible in an edit mode only to the expert or his/her designee (e.g., a personal assistant) via a login/password scheme as determined in step 508 . Further, the expert, in step 510 would then be able to select which portion or portions of their homepage they want to “publish” (i.e., make available for browsing) to other experts on the intellectual networking Web site.
  • curriculum vitae-related information e.g., contact information, personal information, employment history, education, publications, professional qualifications, awards, professional memberships, conferences attended, interests, active projects, patents, and the like
  • the expert in step 510 would then be able to select which portion or portions of their homepage they want to “publish”
  • the WikiID (and its link to each user's homepage) may be used for administrative purposes within the relevant intellectual networking community (e.g., registering for conferences, submitting papers, grant proposals and reports, etc.) obviating the need to manually fill out forms as is currently done for such activities.
  • a button is provided as an Internet browser plug-in or add-on such that the user can click the button to link (and post) in step 514 the URL of any page currently being browsed by them to their homepage on the intellectual networking Web site.
  • the Internet browser plug-in or add-on button may be labeled a “Clink!” button (i.e., a combination of clicking and linking).
  • the clink button would function not only to save (static) URLs of interests for the user related to concepts they are researching. Rather, clinking a URL also tags the concepts of interest to the user that appear on the page designated by the URL, thereby expanding the user's personal Knowlet space (i.e., expanding the knowledge base upon which the F, C and A attribute values can be calculated, besides the one or more data stores loaded into system 100 in step 304 of the above-described methodology).
  • the concepts appearing on the pages designated by the clinked URLs can then be manipulated in step 516 for knowledge discovery (e.g., background mode searching, discovery mode searching, etc.) as described above with concepts appearing in the documents within the one or more data store(s) loaded into system 100 (e.g., PubMed) in step 304 of process 300 .
  • knowledge discovery e.g., background mode searching, discovery mode searching, etc.
  • users in step 520 may organize their “clinked” URLs on their homepage into folders or any other groupings, name each clinked URL and the like.
  • a user in step 522 can view their own homepage, highlight concepts (e.g., from their own curriculum vitae) they are interested in at the moment, and then have the clinked URLs related to the selected concept(s) appear, be highlighted or otherwise be distinguished from those URLs not related to the selected concept(s).
  • Process 500 then terminates as indicated by step 526 .
  • the intellectual networking Web site may take the form of a wiki site and thus allow collaborative efforts and other user/community features typically associated with wiki sites.
  • WikiPeople intellectual networking site to facilitate knowledge navigation and discovery activities.
  • benefits of a WikiPeople site include: automatic alerts for literature based knowledge discovery; using the WikiID for funding, publishing and conferences; matching across all major languages on a user's curriculum vitae; and possibilities for job offerings, etc.
  • Process 600 begins at step 302 with control passing immediately to step 604 .
  • step 604 As a user browses the Internet in step 604 and comes across a Web page of interest in step 606 , the user may click a “wikifier” button in step 608 on a tool bar or pull-down menu provided by the present invention which would then parse through the HTML code of the active Web page “on the fly” in step 610 and highlight (e.g., show in color) in step 612 all the concepts found in the one or more ontologies or thesauri previously loaded in step 306 above into the system. This would allow the user to highlight one or more concepts of interests to perform a search in step 614 within the system of the present invention, using an Internet search engine such as Yahoo!, Google and the like, or even to perform a search within a specified wiki.
  • an Internet search engine such as Yahoo!, Google and the like
  • An advantage of such an aspect of the present invention is that it builds more complex (and thorough) Internet search queries (i.e., Boolean “And” queries) than can ever be crafted by humans. This is due to the loaded ontologies or thesauri with its unique numerical identifier and synonyms (whether in the same language or in different languages).
  • the “wikifier” button or menu option may be used on a Web page that itself represents the results (or output) of an Internet search engine, thus in step 616 highlighting “on the fly” all the concepts found in the one or more ontologies or thesauri previously loaded in step 306 into the system as described above.
  • An entry regarding the highlighted concept may be made in the wiki.
  • This entry may be edited later by the same or other users of the system.
  • the selected and edited wiki entry in step 618 may be the user's local copy or an enterprise's (i.e., community's) global copy.
  • an on-the-fly “edit” button may be provided as part of the Internet browser plug-in or add-on such that it instantly in step 620 makes selected parts of the HTML output of a Web page “copyable” to a wiki page of a given concept, thus avoiding the need for massive importing of data from one Web site to another Web site.
  • the result of this aspect of the present invention is to “federate” distributed sites (which may be in different natural languages) at the concept level and present them in a common GUI.
  • federating refers to transforming a query and broadcasting it to a group of disparate databases, merging the results and presenting them in a succinct and unified format and allowing the results to be sorted.
  • the user is then presented in decision step 622 with the option of browsing further (in which case process 600 returns to step 604 ) or ending the session (as indicated by step 624 ).
  • Process 700 begins at step 702 with control passing immediately to step 704 .
  • a feature of the “Clink!” button is that a user may first go to any page in the “wikifier” environment while browsing, as in step 704 , and click two or more concepts in step 706 that are factually related in their opinion.
  • the wikifier will then, in step 708 , display in a pop up whether the concepts are already factually associated in the Concept Space or allowing the results to be sorted.)
  • the user is then presented in decision step 622 with the option of browsing further (in which case process 600 returns to step 604 ) or ending the session (as indicated by step 624 ).
  • Process 700 begins at step 702 with control passing immediately to step 704 .
  • a feature of the “Clink!” button is that a user may first go to any page in the “wikifier” environment while browsing, as in step 704 , and click two or more concepts in step 706 that are factually related in their opinion. The wikifier will then, in step 708 , display in a pop up whether the concepts are already factually associated in the Concept Space or not.
  • the user can just select the concepts in the text and press the “Clinck!” button. This action will result in the insertion of a “Clincked!” button in step 712 in each of the individual Wiki pages of the selected concepts.
  • Process 700 then terminates as indicated by step 720 .
  • modes for the Wikifier may include: and Exploration Mode: (current pop ups); a Tagging Mode: allows user to select tags, view selected tags, and store in an “Expert Profile,” “Interest Profile” or “Activity Profile”; a Translation Mode: (source language/target language) shows definitions in one or more languages available from a (drop-down); Clincking Mode: Prompts user to accept concepts in clincked pages displaying them as a ranked list (connected to Tagging mode); an Expert Location Mode: shows intellectual matches (can be used to find peers, reviewers, experts, etc.; and a Thesaurus Enrichment Mode: shows “others” by default and shows potential concepts in pages (simple NLP and bi ⁇ trigrams etc.).
  • funders and publishers within the community may keep internal databases with more detailed information on users as reviewers, grantees, etc., which will be linked to each user's public WikiPeople homepage via their WikiID.
  • the tool to navigate, search and perform discovery activities may be provided to users to perform and provide a tool which allows a user to create, “on the fly,” a Web page connected to an editable environment, such as the Wiki.
  • Process 800 begins at step 802 with control passing immediately to step 804 .
  • a user logs on to the system or enters the concept web portal in step 804 and the GUI screen shown in FIG. 9 is displayed.
  • the GUI screen of FIG. 9 will the user to enter a concept as shown in step 806 .
  • the user is also able to select the functionality (i.e., either Wikifier or the Concept Web Navigator) in step 808 .
  • server 106 launches the selected functionality in step 810 and the user is prompted to select a data source in step 812 .
  • the data source selection may be presented as a drop-down screen as shown in FIG. 10 .
  • Exemplary data sources shown include PubMed, BioMedCentral, Google, Google Scholar and Pub Repository.
  • the system accesses and passes the selected data source in step 814 through the Wiki proxy server and then shows highlighted concepts on the data source web site in step 816 . Exemplary displays are shown in FIGS. 15-22 for different data sources.
  • the user may make use of different Wikifier search functionalities and capabilities in step 818 , such as obtaining a definition of the concept, linking the concept to the concept web, obtaining methods for searching other websites with the concept, etc. as shown in FIG. 23 .
  • the user is further exposed to highlighting concept categories in step 820 and as displayed in FIG. 24 where the highlighted concepts will depend on the categories the user selects from the toolbar at the top of the browser as shown.
  • the Wikifier search functionality when prompted in step 822 lists the query concepts and offers a list of sites available for searching as shown in FIG. 25 .
  • FIG. 26 shows an exemplary GUI screen displayed when Google is selected to be searched in step 822 .
  • the query expansion may be used to refine the user's search
  • decision step 824 determines of the user encounters an unrecognized concept. If not, process 800 proceeds to step 830 . If the user does encounter an unrecognized concept in step 824 (as shown in FIG. 28 ), the user is presented, in decision step 826 , with the option of creating a new wiki page or just entering another concept. If the user chooses to enter another concept, process 800 returns to step 806 . If the user decides to create a new wiki page, one is created in step 828 after which the user is presented with the option of entering another concept (step 830 ) or ending process 800 (as indicated by step 832 ).
  • aspects of the present invention may be implemented using hardware, software or a combination thereof and may be implemented in one or more computer systems or other processing systems.
  • the manipulations performed by the present invention were often referred to in terms, such as adding or comparing, which are commonly associated with mental operations performed by a human operator. No such capability of a human operator is necessary, or desirable in most cases, in any of the operations described herein which form part of the present invention. Rather, the operations are machine operations.
  • Useful machines for performing the operation of the present invention include general purpose digital computers or similar devices.
  • the invention is directed toward one or more computer systems capable of carrying out the functionality described herein.
  • An example of a computer system 200 is shown in FIG. 2 .
  • the computer system 200 includes one or more processors, such as processor 204 .
  • the processor 204 is connected to a communication infrastructure 206 (e.g., a communications bus, cross-over bar, or network).
  • a communication infrastructure 206 e.g., a communications bus, cross-over bar, or network.
  • Computer system 200 can include a display interface 202 that forwards graphics, text, and other data from the communication infrastructure 206 (or from a frame buffer not shown) for display on the display unit 230 .
  • Computer system 200 also includes a main memory 208 , preferably random access memory (RAM), and may also include a secondary memory 210 .
  • the secondary memory 210 may include, for example, a hard disk drive 212 and/or a removable storage drive 214 , representing a floppy disk drive, a magnetic tape drive, an optical disk drive, etc.
  • the removable storage drive 214 reads from and/or writes to a removable storage unit 218 in a well known manner.
  • Removable storage unit 218 represents a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 214 .
  • the removable storage unit 218 includes a computer usable storage medium having stored therein computer software and/or data.
  • secondary memory 210 may include other similar devices for allowing computer programs or other instructions to be loaded into computer system 200 .
  • Such devices may include, for example, a removable storage unit 222 and an interface 220 .
  • Examples of such may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an erasable programmable read only memory (EPROM), or programmable read only memory (PROM)) and associated socket, and other removable storage units 222 and interfaces 220 , which allow software and data to be transferred from the removable storage unit 222 to computer system 200 .
  • EPROM erasable programmable read only memory
  • PROM programmable read only memory
  • Computer system 200 may also include a communications interface 224 .
  • Communications interface 224 allows software and data to be transferred between computer system 200 and external devices. Examples of communications interface 224 may include a modem, a network interface (such as an Ethernet card), a communications port, a Personal Computer Memory Card International Association (PCMCIA) slot and card, etc.
  • Software and data transferred via communications interface 224 are in the form of signals 228 which may be electronic, electromagnetic, optical or other signals capable of being received by communications interface 224 . These signals 228 are provided to communications interface 224 via a communications path (e.g., channel) 226 . This channel 226 carries signals 228 and may be implemented using wire or cable, fiber optics, a telephone line, a cellular link, an radio frequency (RF) link and other communications channels.
  • RF radio frequency
  • computer program medium and “computer usable medium” are used to generally refer to media such as removable storage drive 214 , a hard disk installed in hard disk drive 212 , and signals 228 .
  • These computer program products provide software to computer system 200 .
  • the invention is directed to such computer program products.
  • Computer programs are stored in main memory 208 and/or secondary memory 210 . Computer programs may also be received via communications interface 224 . Such computer programs, when executed, enable the computer system 200 to perform the features of the present invention, as discussed herein. In particular, the computer programs, when executed, enable the processor 204 to perform the features of the present invention. Accordingly, such computer programs represent controllers of the computer system 200 .
  • the software may be stored in a computer program product and loaded into computer system 200 using removable storage drive 214 , hard drive 212 or communications interface 224 .
  • the control logic when executed by the processor 204 , causes the processor 204 to perform the functions of the invention as described herein.
  • the invention is implemented primarily in hardware using, for example, hardware components such as application specific integrated circuits (ASICs).
  • ASICs application specific integrated circuits
  • the invention is implemented using a combination of both hardware and software.

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