WO1999012108A1 - Methods and/or systems for selecting data sets - Google Patents
Methods and/or systems for selecting data sets Download PDFInfo
- Publication number
- WO1999012108A1 WO1999012108A1 PCT/GB1998/002611 GB9802611W WO9912108A1 WO 1999012108 A1 WO1999012108 A1 WO 1999012108A1 GB 9802611 W GB9802611 W GB 9802611W WO 9912108 A1 WO9912108 A1 WO 9912108A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- data
- keywords
- information
- key words
- noun
- Prior art date
Links
Classifications
-
- 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
- G06F16/31—Indexing; Data structures therefor; Storage structures
- G06F16/313—Selection or weighting of terms for indexing
-
- 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
- Y10S707/912—Applications of a database
- Y10S707/918—Location
-
- 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
- Y10S707/99936—Pattern matching access
Definitions
- the present invention relates to methods and/or systems for selecting data sets, which finds particular application in selecting documents for instance from an information base such as that accessible using the Internet.
- the Internet world-wide Web is a known communications system based on a plurality of separate communications networks connected together. It provides a rich source of information from many different providers but this very richness creates a problem in accessing specific information as there is no central monitoring and control.
- the present invention is not concerned with providing another tool for searching systems such as the World Wide Web (W3): there are already many of these. They are being added to frequently with ever increasing coverage of the Web and sophistication of search engines.
- WW3 World Wide Web
- embodiments of the present invention relate to the following problem: having found useful information on W3, how can it be stored for easy retrieval and how can other users likely to be interested in the information be identified and informed? More specifically, the applicant's co-pending application PCT/GB96/00132 provides an information retrieval agent, known as a JASPER agent, that is used for identifying and retrieving information from distributed information systems such as the W3.
- a JASPER agent an information retrieval agent
- a first aspect of the present invention there is provided apparatus for determining a measure of similarity between at least a first and a second data set, said apparatus comprising:
- input means for receiving at least said first and second data sets; ii) processing means for identifying a set of keywords in at least the first of the data sets, the processing means having access to at least one rule set and identifying the set of keywords by use of said at least one rule set, the processing means further determining said measure of similarity; and iii) output means to output said measure of similarity
- said rule set includes a rule concerning relative location of data items in a respective data set
- said processing means determines the measure of similarity by comparing at least one set of key words, identified by said processing means in the first data set, with a set of keywords comprising or derived from said second data set.
- Embodiments of the present invention enable two or more keywords within a data set to be associated with each other, for example keywords that form a phrase, with the result that the accuracy in comparison of similarity of data sets may be improved.
- the apparatus further comprises information retrieval means and a data store, said first data set comprising data retrieved from an information base by said information retrieval means and said second data set comprising a set of key words stored in said data store.
- the set of keywords may have been provided by a user, or stored in a user profile.
- the rule set may provide means to identify adjacent items in the data set which can be treated together, as a single keyword. This entails not only location information but also, for instance, a grammatical test on adjacent items such as one or more of the following:
- identifying tags to selected data items in at least the first of the data sets, in accordance with at least a first rule; ii) identifying a set of potential key words by reference to either the presence or the absence of said identifying tags; iii) selecting sets of two or more potential keywords which are adjacent by applying at least a second rule; iv) classifying each selected set of potential keywords as a single keyword; v) generating a set of keywords which comprises each classified set of potential keywords as a single keyword, together with the remaining keywords from the identified set of potential keywords; and vi) comparing the generated set of keywords with a set of keywords either comprising or derived from the second data set.
- said first rule may advantageously relate at least in part to the grammatical category of the data items.
- Said at least a second rule may comprise one or more rules from the following set:
- Figure 1 shows an information access system incorporating a Jasper agent system
- Figure 2 shows in schematic format a storage process offered by the access system
- Figure 3 shows the structure of an intelligent page store for use in the storage process of Figure 1 ;
- Figure 4 shows in schematic format retrieval processes offered by the access system
- Figure 5 shows a flow diagram for the storage process of Figure 2
- Figures 6, 7 and 8 show flow diagrams for three information retrieval processes using a Jasper access system
- Figure 9 shows a keyword network generated using a clustering technique, for use in extending and/or applying user profiles in a Jasper system.
- Figure 10 shows a part of the Jasper agent of Figure 1 that is used to identify associated key words.
- Embodiments of the present invention provide improvements to information access and information retrieval systems, such as the JASPER agent described below.
- a description of the embodiments of the present invention is provided subsequent to the description of this JASPER agent.
- the present invention however, is not limited to JASPER agents. It has further application in other areas, such as information systems that employ user profiling techniques and information systems employing key word retrieval and key word searching techniques.
- Each agent generally comprises functionality to perform a task or tasks on behalf of an entity (human or machine- based) in an autonomous manner, together with local data, or means to access data, to support the task or tasks.
- agents for use in storing or retrieving information in embodiments of the present invention are referred to for simplicity as “Jasper agents", this stemming from the acronym “Joint Access to Stored Pages with Easy Retrieval”.
- Jasper agents Given the vast amount of information available on W3, it is preferable to avoid the copying of information from its original location to a local server. Indeed, it could be argued that such an approach is contrary to the whole ethos of the Web.
- Jasper agents store only relevant "meta-information".
- this meta-information can be thought of as being at a level above information itself, being about it rather than being actual information. It can include for instance keywords, a summary, document title, universal resource locator (URL) and date and time of access.
- This meta-information is then used to provide a pointer to, or to "index on", the actual information when a retrieval request is made.
- Most known W3 clients MosaicTM and NetscapeTM for example) provide some means of storing information about pages of interest to the user. Typically, this is done by allowing the user to create a (possibly hierarchical) menu of names associated with particular URLs.
- KBS knowledge- based systems
- an information access system may be built into a known form of information retrieval architecture, such as a client-server type architecture connected to the Internet.
- a customer such as an international company, may have multiple users equipped with personal computers or workstations 405. These may be connected via a World Wide Web (WWW) viewer 400 in the customer's client context to the customer's WWW file server 410.
- WWW World Wide Web
- the Jasper agent 105 effectively an extension of the viewer 400, may be actually resident on the WWW file server 410.
- the customer's WWW file server 410 is connected to the Internet in known manner, for instance via the customer's own network 41 5 and a router 420. Service providers' file servers 425 can then be accessed via the Internet, again via routers.
- a text summarising tool 1 20 and two data stores one holding user profiles (the profile store 430) and the other (the intelligent page store 100) holding principally meta-information for a document collection.
- the agent 105 itself can be built as an extension of a known viewer such as Netscape.
- the agent 105 is effectively integrated with the viewer 400, which might be provided by Netscape or by Mosaic etc, and can extract W3 pages from the viewer 400.
- a Jasper agent being a software agent, can generally be described as a software entity, incorporating functionality for performing a task or tasks on behalf of a user, together with local data, or access to local data, to support that task or tasks.
- the tasks relevant in a Jasper system one or more of which may be carried out by a Jasper agent, are described below.
- the local data will usually include data from the intelligent page store 100 and the profile store 430, and the functionality to be provided by a Jasper agent will generally include means to apply a text summarising tool and store the results, access or read, and update, at least one user profile, means to compare keyword sets with other keyword sets, or meta-information, and means to trigger alert messages to users.
- a Jasper agent will also be provided with means to monitor user inputs for the purpose of selecting a keyword set to be compared.
- a Jasper agent is provided with means to apply an algorithm in relation to first and second keyword sets to generate a measure of similarity therebetween. According to the measure of similarity, either the first or second keyword sets may then be proactively updated by the Jasper agent, or the result of comparing the first or second keyword sets with a third keyword set, or with meta-information, may be modified.
- Embodiments of the present invention might be built according to different software systems. It might be convenient for instance that object-oriented techniques are applied. However, in embodiments as described below, the server will be Unix based and able to run ConTextTM, a known natural language processing system offered by Oracle Corporation, and a W3 viewer. The system might generally be implemented in "C" although the client might potentially be any machine which can support a W3 viewer.
- FIGs 2 and 5 show the actions taken when a Jasper agent 105 stores information in an intelligent page store (IPS) 100.
- the user 1 10 first finds a W3 page of sufficient interest to be stored by the Jasper system in an IPS 100 associated with that user (STEP 501 ).
- the user 1 10 then transmits a 'store' request to the Jasper agent 105, resident on the customer's WWW file server 410, via a menu option on the user's selected W3 client 1 1 5 (Mosaic and Netscape versions are currently available on all platforms) (STEP 502).
- the Jasper agent 105 then invites the user 1 10 to supply an associated annotation, also to be stored (STEP 503).
- this might be the reason the user is interested in the page and can be very useful for other users in deciding which pages retrieved from the IPS 100 to visit. (Information sharing is further discussed below.)
- the Jasper agent 105 next extracts the source text from the page in question, again via the W3 client 1 1 5 on W3 (STEP 504).
- Source text is provided in a "HyperText” format and the Jasper agent 105 first strips out HyperText
- HTML Markup Language
- ConText 1 20 first parses a document to determine the syntactic structure of each sentence (STEP 507).
- the ConText parser is robust and able to deal with a wide range of the syntactic phenomena occurring in English sentences.
- ConText 1 20 enters its 'concept processing' phase (STEP 508).
- facilities offered are:
- ConText can extract all the parts of a document which are particularly relevant to a certain concept.
- ConText 1 20 is used by the Jasper agent 105 in a client-server architecture: after parsing the documents, the server generates application- independent marked-up versions (STEP 509). Calls from the Jasper agent 105 using an Applications Programming Interface (API) can then interpret the mark-ups. Using these API calls, meta-information is obtained from the source text (STEP 510). The Jasper agent 105 first extracts a summary of the text of the page. The size of the summary can be controlled by the parameters passed to ConText 1 20 and the Jasper agent 105 ensures that a summary of 100-1 50 words is obtained.
- API Application Programming Interface
- the Jasper agent 105 uses a further call to ConText 1 20, the Jasper agent 105 then derives a set of keywords from the source text. Following this, the user may optionally be presented with the opportunity to add further keywords via an HTML form 1 25 (STEP 51 1 ). In this way, keywords of particular relevance to the user can be provided, while the Jasper agent 105 supplies a set of keywords which may be of greater relevance to a wider community of users.
- the Jasper agent 105 has generated the following meta-information about the W3 page of interest: the ConText-supplied general keywords; user-specific keywords; the user's annotations; a summary of the page's content; the document title; universal resource location (URL) and date and time of storage.
- the Jasper agent 105 then adds this meta-information for the page to files 1 30 of the IPS 100 (STEP 51 2).
- the keywords (of both types) are then used to index on files containing meta- information for other pages.
- a Jasper agent 105 When a Jasper agent 105 is installed on a user's machine, the user provides a personal profile: a set of keywords which describe information the user is interested in obtaining via W3. This profile is held, or at least maintained, by the agent 105 in order to determine which pages are potentially of interest to a user.
- the user supplies a set of keywords to the Jasper agent 105 via an HTML form 300 provided by the Jasper agent 105 (STEP 601 ).
- the Jasper agent 105 then retrieves the ten most closely matching pages held in IPS 100 (STEP 602), using a simple keyword matching and scoring algorithm. Keywords supplied by the user when the page was stored (as opposed to those extracted automatically by ConText) can be given extra weight in the matching process.
- the user can specify in advance a retrieval threshold below which pages will not be displayed.
- the agent 105 then dynamically constructs an HTML form 305 with a ranked list of links to the pages retrieved and their summaries (STEP 603). Any annotation made by the original user is also shown, along with the scores of each retrieved page. This page is then presented to the user on their W3 client (STEP 604).
- Any user can ask a Jasper agent "What's new?" (STEP 701 ).
- the agent 105 then interrogates the IPS 100 and retrieves the most recently stored pages (STEP 702). It then determines which of these pages best match the user's profile, again based on a simple keyword matching and scoring algorithm (STEP 703).
- An HTML page is then presented to the user showing a ranked list of links to the recently stored pages which best match the user's profile, and also to other pages most recently stored in IPS (STEP 704) , with annotations where provided.
- the user is provided with a view both of the pages recently stored and likely to be of most interest to the user, and a more general selection of recently stored pages (STEP 705).
- a user can update the profile which his Jasper agent 105 holds at any time via an HTML form which allows him to add and/or delete keywords from the profile. In this way, the user can effectively select different "contexts" in which to work.
- a context is defined by a set of keywords (those making up the profile, or those specified in a retrieval query) and can be thought of as those types of information which a user is interested in at a given time.
- the agent 105 checks the profiles of other agents' users in its 'local community' (STEP 802). This local community could be any predetermined community. If the page matches a user's profile with a score above a certain threshold (STEP 803), a message, for instance an "email" message, can be automatically generated by the agent 105 and sent to the user concerned (STEP 804), informing him of the discovery of the page.
- This local community could be any predetermined community.
- the email header might be for instance in the format:
- a list of keywords is provided and the user can assess the relative importance of the information to which the message refers.
- the keywords in the message header vary from user to user depending on the keywords from the page which match the keywords in their user profile, thus personalising the message to each user's interests.
- the message body itself can give further information such as the page title and URL, who stored the page and any annotation on the page which the storer provided.
- the Jasper agent 105 and system described above provide the basis for an extremely useful way of accessing relevant information in a distributed arrangement such as W3. Variations and extensions may be made in a system without departing from the scope of the present invention. For instance, at a relatively simple level, improved retrieval techniques might be employed. As examples, vector space or probabilistic models might be used, as described by G Salton in "Automatic Text Processing", published in 1 989 by Addison-Wesley in Reading, Massachusetts, USA. Alternatively, indexing might be made more versatile by providing indexing on meta-information other than keywords.
- extra meta-information might be the date of storage of a page and the originating site of the page (which Jasper can extract from the URL.)
- These extra indices allow users (via an HTML form) to frame commands of the type: Show me all pages I stored in 1994 from Cambridge University about artificial intelligence and information retrieval.
- a thesaurus might be used by Jasper agents
- Adaptive Agents The use of user profiles by Jasper agents 105 to determine information relevant to their users, though powerful can be improved.
- the user profile must be re-specified by adding and/or deleting keywords.
- a better approach is for the agent to change the user's profile as the interests of the user change over time.
- This change of context can occur in two ways: there can be a short-term switch of context from, for example, work to leisure. The agent can identify this from a list of current contexts it holds for a user and change into the new context. This change could be triggered, for example, when a new page of different information type is visited by the user.
- Jasper system Another possible variation of a Jasper system would be to integrate the user's own computer filing system with the IPS 100, so that information found on
- the Jasper IPS 100 and the related documents can essentially be called a collection; it is a set of documents indexed by keywords. It differs from a 'traditional' collection in that the documents are typically located remotely from the index; the index (the IPS 100) actually points to a URL which specifies the location of the document on the Internet. Furthermore, various additional pieces of meta-information are attached to documents in a Jasper system, such as the user who stored the page, when it was stored, any annotation the user may have provided and so forth.
- Jasper IPS 100 One important area where a Jasper system differs from most document collections is that each document has been entered in the IPS 100 by a user who made a conscious decision to mark it as a piece of information which he and his peers would be likely to find useful in the future. This, along with the meta- information held, makes a Jasper IPS 100 a very rich source of information.
- Jasper's term-document matrix can be used to calculate a similarity matrix for the documents identified in the Jasper IPS 100.
- the similarity matrix gives a measure of the similarity of documents identified in the store. For each pair of documents the Dice coefficient is calculated. For two documents Di and Dj.
- [X] is the number of terms in X and XnY is the number of terms co- occurring in X and Y. This coefficient yields a number between 0 and 1 .
- a coefficient of zero implies two documents have no terms in common, while a coefficient of 1 implies that the sets of terms occurring in each document are identical.
- the similarity matrix, Sim say, represents the similarity of each pair of documents in the store, so that for each pair of documents / and /.
- This matrix can be used to create clusters of related documents automatically, using the hierarchical agglomerative clustering process described in "Hierarchic Agglomerative Clustering Methods for Automatic Document Classification” by Griffiths A et al in the Journal of Documentation, 40:3, September 1 984, pp 1 75-205.
- each document is initially placed in a cluster by itself and the two most similar such clusters are then combined into a larger cluster, for which similarities with each of the other clusters must then be computed. This combination process is continued until only a single cluster of documents remains at the highest level.
- the way in which similarity between clusters (as opposed to individual documents) is calculated can be varied. For a Jasper store, "complete-link clustering" can be employed.
- the similarity between the least similar pair of documents from the two clusters is used as the cluster similarity.
- the resulting cluster structures of the Jasper store can then be used to create a three-dimensional (3D) front end onto the Jasper system using the VRML (Virtual Reality Modelling Language).
- VRML Virtual Reality Modelling Language
- VRML is a known language for 3D graphical spaces or virtual worlds networked via the global Internet and hyperlinked within the World Wide Web).
- Keywords (terms) occurring in relation to a particular Jasper document collection can also be clustered in a way which mirrors exactly the document cluster technique described above: a similarity matrix for the keywords in the Jasper store can be constructed which gives a measure of the 'similarity' of keywords in the store. For each pair of documents, the Dice coefficient is calculated. For two keywords Ki and Kj, the Dice coefficient is given by:
- the first way is profile enhancement.
- the user profile can be enhanced by using those keywords most similar to the keywords in the user's profile.
- an enhanced profile might add VRML to the original profile (assuming VRML is clustered close to virtual, reality and Internet). In this way, documents containing VRML but not virtual, reality and Internet may be retrieved whereas they would not have been with the unenhanced profile.
- Figure 9 shows an example network of keywords 900 which has been built from the keyword similarity matrix extracted from a current Jasper store.
- the algorithm is straightforward: given an initial starting keyword, find the four words most similar to it from the similarity matrix. Link these four to the original word and repeat the process for each of the four new words. This can be repeated a number of times (in Figure 9, three times). Double lines 901 between two words indicate that both words occur in the other's four most similar keywords.
- the second way is proactive searching.
- the keywords comprising a user's profile can be used to search for new WWW pages relevant to their interest proactively by Jasper, which can then present a list of new pages which the user may be interested in without the user having to carry out a search explicitly.
- These proactive searches can be carried out by a Jasper system at some given interval, such as weekly.
- Clustering is useful here because a profile may reflect more than one interest. Consider, for example, the following user profile: Internet, WWW, html, football, Manchester, united, linguistics, parsing, pragmatics. Clearly, three separate interests are represented in the above profile and searching on each separately is likely to yield far superior results than merely entering the whole profile as a query for the given user.
- Clustering keywords from the document collection can automate the process of query generation for proactive searching by a user's Jasper agent.
- search results When the search results are obtained by Jasper, they can be summarised and matched against the user's profile in the usual way to give a prioritised list of new URLs along with locally held summaries.
- Embodiments of the present invention provide improvements to the JASPER system above. These embodiments will now be described with reference to Figure 10, which identifies elements within the Jasper agent that are used to identify associated key words within a document that may improve the performance of the Jasper system above.
- the above clustering techniques can be enhanced by identifying two or more keywords that are associated with each other, for example keywords that form a phrase. These associated keywords are then entered into the document- term matrix as single terms.
- each paragraph has the same keywords, namely "people", “company”, “latest”, “information”, “technology”, “copy”, “transfer”, “file”, “local”, “area” and “network”. If the keywords "information” and “technology” and “local”, “area” and “network”.
- Paragraph 1 Paragraph 2 people 1 company 1 latest 1 information 1 technology 1 copy 1 transfer 1 file 1 local 1 area 1 network 1
- the matrix shows there are 1 1 terms common to both paragraphs and that each paragraph contains 1 1 terms.
- a Dice co-efficient of 0.6 may be considered a more accurate reflection of the similarities and differences between the subject matter of the two paragraphs.
- phrase structures and grammatical structures have a high probability of identifying sets of key words that are associated in such a way that their inclusion as a single entry in a similarity matrix is likely to enhance its result.
- Adjacent key words consisting of two nouns, or a noun followed by a verb are common examples of the type of grammatical structures which would occur in a short phrase and are therefore likely to improve the quality of a similarity matrix.
- a verb followed by an adjective is a combination unlikely to occur in a short phrase and are therefore considered unlikely to enhance the quality of a similarity matrix.
- Embodiments of the present invention will include a list of such phrase structures and grammatical structures. The text of a document being analysed will be examined for the presence of sets of key words that form such structures. This is in addition to the initial process of identifying these key words.
- embodiments of the present invention need to find a compromise between identifying only those grammatical structures that have a high probability of enhancing a similarity matrix and identifying too many grammatical structures that have a lower probability of enhancing a similarity matrix.
- Figure 10 is a representation of the elements within the Jasper agent 105 that are used to identify associated keywords within a document.
- Input text 1000 is downloaded from the W3 client 1 1 5 into a Jasper agent
- Parser 1 1005 analyses the input text 1000 for abbreviations and acronyms.
- the input text 1000 is then parsed again by Parser 1 1005 so as to divide it up into word groups 1010, such as sentences, paragraphs, headers (such as HTML headers) or items isolated by blank lines.
- Word groups 1010 such as sentences, paragraphs, headers (such as HTML headers) or items isolated by blank lines.
- Tags identifying abbreviations and acronyms allow the second parsing process of Parser 1 1005 to distinguish between full stops occurring at the end of an abbreviation or acronym and full stops at the end of a sentence. This helps to prevent spurious splitting of word groups 1010 mid sentence that may be caused by the presence of a full stop at the end of an abbreviation or acronym.
- the word groups 1010 are input to a second parser 1020, "Parser 2". Parser 2 1020 performs four operations on each word group 1010.
- Parser 2 analyses the word groups 1010 for words with unusual capitalisation. Such words are often used as the name of an entity, such as a corporate communications network or computer system. For example, imagine that a corporation has chosen to call one of its computer systems "Over”. It may appear in the middle of a sentence as "Over” in which case it will be tagged as a word with unusual capitalisation. Other variations of this type that may be expected include OvEr, OveR. Words which have been identified as having unusual capitalisation are marked as "stop list" override.
- a stoplist contains a list of words that typically do not reflect the information content of a document. For example, words such as “as”, “is”, “are”, “the”, “they”, “where", “by”, “my” etc.
- a stoplist may also contain a list of prefixes and suffixes. The stoplist operates in this instance to reduce a word, with a prefix or suffix, or both, to a basic form without the prefix or suffix. This is known as stemming and examples are “manufacturing” which is reduced to “manufacture”, “predetermination” to “determine”, and “preselect” to “select”. Secondly, the word groups 1010 are compared against a "stoplist” database 1025.
- words not in the stoplist and words marked as stoplist override are tagged as being relevant to the information content of the document.
- each adjacent pair of words that have been tagged as being relevant to the information content of the document are further tagged as being a set of key words that may enhance the result of a similarity matrix.
- each pair of words that are tagged as being relevant to the information content of a document and that are separated by words on the stop list are not considered to form associated key words.
- these sets of key words are identified according to their grammatical structures. These structures are defined by the combination of word types in the keyword set, e.g. a first structure may be a noun followed by a verb and an alternate structure may be an adjective followed by a noun. Sets of key words falling within a preferred list of grammatical structures are then tagged for inclusion in a similarity matrix as a single entry rather than as individual entries.
- JASPER agent Alternatively, "?” may also represent an acronym or a word appearing in the document with unusual capitalisation. Examples of such words include IT, LAN, WAN, xDSL and OveR.
- IT typically is used to mean Information Technology
- LAN Local Area Network
- WAN Wide Area Network
- xDSL refers generically to a class of technology known as Digital Subscriber Line technology
- OveR may be a name of a corporate facility such as a communications networks.
- each of these associated key words will be entered into the Jasper key word store, as a single complex key word, they may also be used in the key word clustering technique, detailed above, that is used to enhance user profiles.. This may improve the quality of pro-active searching performed by the JASPER agent 1 1 5. It may also be used by a search engine, or similar device, to identify documents containing associated key words that have been used to define the target information of the search.
- the process is not limited to English language documents. Similar techniques may be used for other languages.
- ProSum is a summarising tool made available by British
- Telecommunications pic on the Internet at the BT Labs shop located at http://www.labs.bt.com.
- embodiments of the present invention might be found useful for locating information on other systems, such as documents on a user's internal systems which are in HyperText.
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
Description
Claims
Priority Applications (7)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US09/155,172 US6353827B1 (en) | 1997-09-04 | 1998-08-28 | Methods and/or systems for selecting data sets |
NZ503279A NZ503279A (en) | 1997-09-04 | 1998-08-28 | Selecting data sets using keywords suitable for searching |
CA002302264A CA2302264C (en) | 1997-09-04 | 1998-08-28 | Methods and/or systems for selecting data sets |
EP98940436A EP1010105B1 (en) | 1997-09-04 | 1998-08-28 | Methods and/or systems for selecting data sets |
JP2000509044A JP4274689B2 (en) | 1997-09-04 | 1998-08-28 | Method and system for selecting data sets |
DE69809263T DE69809263T2 (en) | 1997-09-04 | 1998-08-28 | METHODS AND SYSTEM FOR SELECTING DATA SETS |
AU88762/98A AU742831B2 (en) | 1997-09-04 | 1998-08-28 | Methods and/or systems for selecting data sets |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP97306878.6 | 1997-09-04 | ||
EP97306878 | 1997-09-04 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO1999012108A1 true WO1999012108A1 (en) | 1999-03-11 |
Family
ID=8229494
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/GB1998/002611 WO1999012108A1 (en) | 1997-09-04 | 1998-08-28 | Methods and/or systems for selecting data sets |
Country Status (9)
Country | Link |
---|---|
US (1) | US6353827B1 (en) |
EP (1) | EP1010105B1 (en) |
JP (1) | JP4274689B2 (en) |
CN (1) | CN1269897A (en) |
AU (1) | AU742831B2 (en) |
CA (1) | CA2302264C (en) |
DE (1) | DE69809263T2 (en) |
NZ (1) | NZ503279A (en) |
WO (1) | WO1999012108A1 (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001039038A1 (en) * | 1999-11-25 | 2001-05-31 | Datastat | Method and device for retrieving information |
WO2001093071A2 (en) * | 2000-05-29 | 2001-12-06 | Saora Kabushiki Kaisha | System and method for saving browsed data |
WO2002037326A1 (en) * | 2000-11-03 | 2002-05-10 | Envisional Technology Limited | System for monitoring publication of content on the internet |
US7120641B2 (en) | 2002-04-05 | 2006-10-10 | Saora Kabushiki Kaisha | Apparatus and method for extracting data |
Families Citing this family (53)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6115709A (en) * | 1998-09-18 | 2000-09-05 | Tacit Knowledge Systems, Inc. | Method and system for constructing a knowledge profile of a user having unrestricted and restricted access portions according to respective levels of confidence of content of the portions |
US6549897B1 (en) * | 1998-10-09 | 2003-04-15 | Microsoft Corporation | Method and system for calculating phrase-document importance |
JP3685938B2 (en) * | 1998-12-18 | 2005-08-24 | 富士通株式会社 | Communication support method and communication support system |
EP1124189A4 (en) * | 1999-06-04 | 2004-07-21 | Seiko Epson Corp | Document sorting method, document sorter, and recorded medium on which document sorting program is recorded |
US7213198B1 (en) * | 1999-08-12 | 2007-05-01 | Google Inc. | Link based clustering of hyperlinked documents |
US20020059223A1 (en) * | 1999-11-30 | 2002-05-16 | Nash Paul R. | Locator based assisted information browsing |
US8478732B1 (en) * | 2000-05-02 | 2013-07-02 | International Business Machines Corporation | Database aliasing in information access system |
US6704728B1 (en) | 2000-05-02 | 2004-03-09 | Iphase.Com, Inc. | Accessing information from a collection of data |
US6711561B1 (en) | 2000-05-02 | 2004-03-23 | Iphrase.Com, Inc. | Prose feedback in information access system |
US7383299B1 (en) * | 2000-05-05 | 2008-06-03 | International Business Machines Corporation | System and method for providing service for searching web site addresses |
US8290768B1 (en) | 2000-06-21 | 2012-10-16 | International Business Machines Corporation | System and method for determining a set of attributes based on content of communications |
US9699129B1 (en) | 2000-06-21 | 2017-07-04 | International Business Machines Corporation | System and method for increasing email productivity |
US6408277B1 (en) | 2000-06-21 | 2002-06-18 | Banter Limited | System and method for automatic task prioritization |
JP2002140339A (en) * | 2000-10-31 | 2002-05-17 | Tonfuu:Kk | System, device and program for retrieving law and the like |
US6978419B1 (en) * | 2000-11-15 | 2005-12-20 | Justsystem Corporation | Method and apparatus for efficient identification of duplicate and near-duplicate documents and text spans using high-discriminability text fragments |
US7644057B2 (en) | 2001-01-03 | 2010-01-05 | International Business Machines Corporation | System and method for electronic communication management |
US20040111386A1 (en) * | 2001-01-08 | 2004-06-10 | Goldberg Jonathan M. | Knowledge neighborhoods |
US7136846B2 (en) | 2001-04-06 | 2006-11-14 | 2005 Keel Company, Inc. | Wireless information retrieval |
WO2002091154A2 (en) * | 2001-05-10 | 2002-11-14 | Changingworlds Limited | Intelligent internet website with hierarchical menu |
US20040205454A1 (en) * | 2001-08-28 | 2004-10-14 | Simon Gansky | System, method and computer program product for creating a description for a document of a remote network data source for later identification of the document and identifying the document utilizing a description |
US8078545B1 (en) | 2001-09-24 | 2011-12-13 | Aloft Media, Llc | System, method and computer program product for collecting strategic patent data associated with an identifier |
US20030074409A1 (en) * | 2001-10-16 | 2003-04-17 | Xerox Corporation | Method and apparatus for generating a user interest profile |
US7343372B2 (en) * | 2002-02-22 | 2008-03-11 | International Business Machines Corporation | Direct navigation for information retrieval |
US9805373B1 (en) | 2002-11-19 | 2017-10-31 | Oracle International Corporation | Expertise services platform |
JP4024137B2 (en) * | 2002-11-28 | 2007-12-19 | 沖電気工業株式会社 | Quantity expression search device |
US20050187913A1 (en) * | 2003-05-06 | 2005-08-25 | Yoram Nelken | Web-based customer service interface |
US8495002B2 (en) * | 2003-05-06 | 2013-07-23 | International Business Machines Corporation | Software tool for training and testing a knowledge base |
US7752200B2 (en) | 2004-08-09 | 2010-07-06 | Amazon Technologies, Inc. | Method and system for identifying keywords for use in placing keyword-targeted advertisements |
US20070061158A1 (en) * | 2005-09-09 | 2007-03-15 | Qwest Communications International Inc. | Compliance management using complexity factors |
US20070061157A1 (en) * | 2005-09-09 | 2007-03-15 | Qwest Communications International Inc. | Obligation assignment systems and methods |
US8290962B1 (en) * | 2005-09-28 | 2012-10-16 | Google Inc. | Determining the relationship between source code bases |
US8799512B2 (en) * | 2005-10-19 | 2014-08-05 | Qwest Communications International Inc. | Cross-platform support for a variety of media types |
US8170189B2 (en) | 2005-11-02 | 2012-05-01 | Qwest Communications International Inc. | Cross-platform message notification |
US20070143355A1 (en) * | 2005-12-13 | 2007-06-21 | Qwest Communications International Inc. | Regulatory compliance advisory request system |
EP1798678A1 (en) * | 2005-12-15 | 2007-06-20 | Sap Ag | Method and system for automatically controlling forum posting |
US8122049B2 (en) * | 2006-03-20 | 2012-02-21 | Microsoft Corporation | Advertising service based on content and user log mining |
US9323821B2 (en) * | 2006-04-05 | 2016-04-26 | Qwest Communications International Inc. | Network repository auto sync wireless handset |
US20070239832A1 (en) * | 2006-04-05 | 2007-10-11 | Qwest Communications International Inc. | Communication presentation in a calendar perspective |
US20070239895A1 (en) * | 2006-04-05 | 2007-10-11 | Qwest Communications International Inc. | Cross-platform push of various media types |
US8320535B2 (en) * | 2006-04-06 | 2012-11-27 | Qwest Communications International Inc. | Selectable greeting messages |
US7603351B2 (en) * | 2006-04-19 | 2009-10-13 | Apple Inc. | Semantic reconstruction |
US7890521B1 (en) * | 2007-02-07 | 2011-02-15 | Google Inc. | Document-based synonym generation |
US20080208852A1 (en) * | 2007-02-26 | 2008-08-28 | Yahoo! Inc. | Editable user interests profile |
US8780130B2 (en) | 2010-11-30 | 2014-07-15 | Sitting Man, Llc | Methods, systems, and computer program products for binding attributes between visual components |
US9715332B1 (en) | 2010-08-26 | 2017-07-25 | Cypress Lake Software, Inc. | Methods, systems, and computer program products for navigating between visual components |
US8661361B2 (en) | 2010-08-26 | 2014-02-25 | Sitting Man, Llc | Methods, systems, and computer program products for navigating between visual components |
US10397639B1 (en) | 2010-01-29 | 2019-08-27 | Sitting Man, Llc | Hot key systems and methods |
US9760634B1 (en) * | 2010-03-23 | 2017-09-12 | Firstrain, Inc. | Models for classifying documents |
US9727619B1 (en) * | 2013-05-02 | 2017-08-08 | Intelligent Language, LLC | Automated search |
US9892723B2 (en) * | 2013-11-25 | 2018-02-13 | Rovi Guides, Inc. | Systems and methods for presenting social network communications in audible form based on user engagement with a user device |
CN106796587B (en) * | 2014-04-30 | 2020-11-13 | 皮沃塔尔软件公司 | Method and system for verifying analysis results |
WO2016043609A1 (en) * | 2014-09-18 | 2016-03-24 | Empire Technology Development Llc | Three-dimensional latent semantic analysis |
CN108205553B (en) * | 2016-12-19 | 2021-12-28 | 深圳联友科技有限公司 | Interface processing system and method based on text file |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO1988004454A2 (en) * | 1986-12-04 | 1988-06-16 | Tnet, Inc. | Information retrieval system and method |
US5210868A (en) * | 1989-12-20 | 1993-05-11 | Hitachi Ltd. | Database system and matching method between databases |
US5297039A (en) * | 1991-01-30 | 1994-03-22 | Mitsubishi Denki Kabushiki Kaisha | Text search system for locating on the basis of keyword matching and keyword relationship matching |
WO1995029452A1 (en) * | 1994-04-25 | 1995-11-02 | Apple Computer, Inc. | System for directing relevance-ranked data objects to computer users |
WO1996023265A1 (en) * | 1995-01-23 | 1996-08-01 | British Telecommunications Public Limited Company | Methods and/or systems for accessing information |
Family Cites Families (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH04127370A (en) * | 1990-09-19 | 1992-04-28 | Toshiba Corp | Information collecting system |
US5265065A (en) * | 1991-10-08 | 1993-11-23 | West Publishing Company | Method and apparatus for information retrieval from a database by replacing domain specific stemmed phases in a natural language to create a search query |
GB9220404D0 (en) * | 1992-08-20 | 1992-11-11 | Nat Security Agency | Method of identifying,retrieving and sorting documents |
US5598557A (en) * | 1992-09-22 | 1997-01-28 | Caere Corporation | Apparatus and method for retrieving and grouping images representing text files based on the relevance of key words extracted from a selected file to the text files |
US5758257A (en) * | 1994-11-29 | 1998-05-26 | Herz; Frederick | System and method for scheduling broadcast of and access to video programs and other data using customer profiles |
US5819260A (en) * | 1996-01-22 | 1998-10-06 | Lexis-Nexis | Phrase recognition method and apparatus |
US5721897A (en) * | 1996-04-09 | 1998-02-24 | Rubinstein; Seymour I. | Browse by prompted keyword phrases with an improved user interface |
US5794233A (en) * | 1996-04-09 | 1998-08-11 | Rubinstein; Seymour I. | Browse by prompted keyword phrases |
US5857184A (en) * | 1996-05-03 | 1999-01-05 | Walden Media, Inc. | Language and method for creating, organizing, and retrieving data from a database |
US5956711A (en) * | 1997-01-16 | 1999-09-21 | Walter J. Sullivan, III | Database system with restricted keyword list and bi-directional keyword translation |
US5933822A (en) * | 1997-07-22 | 1999-08-03 | Microsoft Corporation | Apparatus and methods for an information retrieval system that employs natural language processing of search results to improve overall precision |
US6055528A (en) * | 1997-07-25 | 2000-04-25 | Claritech Corporation | Method for cross-linguistic document retrieval |
-
1998
- 1998-08-28 EP EP98940436A patent/EP1010105B1/en not_active Expired - Lifetime
- 1998-08-28 US US09/155,172 patent/US6353827B1/en not_active Expired - Lifetime
- 1998-08-28 CN CN98808771A patent/CN1269897A/en active Pending
- 1998-08-28 CA CA002302264A patent/CA2302264C/en not_active Expired - Lifetime
- 1998-08-28 WO PCT/GB1998/002611 patent/WO1999012108A1/en active IP Right Grant
- 1998-08-28 AU AU88762/98A patent/AU742831B2/en not_active Expired
- 1998-08-28 NZ NZ503279A patent/NZ503279A/en unknown
- 1998-08-28 DE DE69809263T patent/DE69809263T2/en not_active Expired - Lifetime
- 1998-08-28 JP JP2000509044A patent/JP4274689B2/en not_active Expired - Lifetime
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO1988004454A2 (en) * | 1986-12-04 | 1988-06-16 | Tnet, Inc. | Information retrieval system and method |
US5210868A (en) * | 1989-12-20 | 1993-05-11 | Hitachi Ltd. | Database system and matching method between databases |
US5297039A (en) * | 1991-01-30 | 1994-03-22 | Mitsubishi Denki Kabushiki Kaisha | Text search system for locating on the basis of keyword matching and keyword relationship matching |
WO1995029452A1 (en) * | 1994-04-25 | 1995-11-02 | Apple Computer, Inc. | System for directing relevance-ranked data objects to computer users |
WO1996023265A1 (en) * | 1995-01-23 | 1996-08-01 | British Telecommunications Public Limited Company | Methods and/or systems for accessing information |
Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001039038A1 (en) * | 1999-11-25 | 2001-05-31 | Datastat | Method and device for retrieving information |
BE1013153A3 (en) * | 1999-11-25 | 2001-10-02 | Datastat S A | Method and system for information collection. |
WO2001093071A2 (en) * | 2000-05-29 | 2001-12-06 | Saora Kabushiki Kaisha | System and method for saving browsed data |
WO2001093071A3 (en) * | 2000-05-29 | 2003-12-31 | Saora Kabushiki Kaisha | System and method for saving browsed data |
AU2001258688B2 (en) * | 2000-05-29 | 2008-04-17 | Saora Kabushiki Kaisha | System and method for saving browsed data |
US7822735B2 (en) | 2000-05-29 | 2010-10-26 | Saora Kabushiki Kaisha | System and method for saving browsed data |
WO2002037326A1 (en) * | 2000-11-03 | 2002-05-10 | Envisional Technology Limited | System for monitoring publication of content on the internet |
GB2384598A (en) * | 2000-11-03 | 2003-07-30 | Envisional Technology Ltd | System for monitoring publication of content on the internet |
GB2384598B (en) * | 2000-11-03 | 2005-06-29 | Envisional Technology Ltd | System for monitoring publication of content on the internet |
US7120641B2 (en) | 2002-04-05 | 2006-10-10 | Saora Kabushiki Kaisha | Apparatus and method for extracting data |
Also Published As
Publication number | Publication date |
---|---|
CA2302264C (en) | 2009-09-15 |
CN1269897A (en) | 2000-10-11 |
JP2001515245A (en) | 2001-09-18 |
AU8876298A (en) | 1999-03-22 |
NZ503279A (en) | 2001-07-27 |
US6353827B1 (en) | 2002-03-05 |
CA2302264A1 (en) | 1999-03-11 |
DE69809263T2 (en) | 2003-07-10 |
EP1010105B1 (en) | 2002-11-06 |
AU742831B2 (en) | 2002-01-10 |
DE69809263D1 (en) | 2002-12-12 |
EP1010105A1 (en) | 2000-06-21 |
JP4274689B2 (en) | 2009-06-10 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CA2302264C (en) | Methods and/or systems for selecting data sets | |
EP0807291B1 (en) | Methods and/or systems for accessing information | |
US5931907A (en) | Software agent for comparing locally accessible keywords with meta-information and having pointers associated with distributed information | |
CA2281645C (en) | System and method for semiotically processing text | |
US6519586B2 (en) | Method and apparatus for automatic construction of faceted terminological feedback for document retrieval | |
US7076484B2 (en) | Automated research engine | |
US20050149494A1 (en) | Information data retrieval, where the data is organized in terms, documents and document corpora | |
US7099870B2 (en) | Personalized web page | |
Attardi et al. | Categorisation by Context. | |
Lam et al. | Using contextual analysis for news event detection | |
US5978798A (en) | Apparatus for and method of accessing a database | |
JP4428850B2 (en) | Information search apparatus and information search method | |
US7483877B2 (en) | Dynamic comparison of search systems in a controlled environment | |
Croft et al. | TREC-2 routing and ad-hoc retrieval evaluation using the INQUERY system | |
O’Riordan et al. | Information filtering and retrieval: An overview | |
Davies et al. | Networked information management | |
Nogueras-Iso et al. | Exploiting disambiguated thesauri for information retrieval in metadata catalogs | |
Petrakis et al. | Similarity searching in the CORDIS text database | |
Sharma et al. | Improved stemming approach used for text processing in information retrieval system | |
Davare et al. | Text Mining Scientific Data to Extract Relevant Documents and Auto-Summarization | |
MXPA97005582A (en) | Methods and / or systems to access information | |
Watcholder et al. | Automatic identification of index terms for interactive browsing | |
Wondergem | INdex navigator for searching and exploring the WWW | |
Eskicioğlu | A Search Engine for Turkish with Stemming |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
WWE | Wipo information: entry into national phase |
Ref document number: 98808771.5 Country of ref document: CN |
|
WWE | Wipo information: entry into national phase |
Ref document number: 09155172 Country of ref document: US |
|
AK | Designated states |
Kind code of ref document: A1 Designated state(s): AL AM AT AU AZ BA BB BG BR BY CA CH CN CU CZ DE DK EE ES FI GB GE GH GM HR HU ID IL IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MD MG MK MN MW MX NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT UA UG US UZ VN YU ZW |
|
AL | Designated countries for regional patents |
Kind code of ref document: A1 Designated state(s): GH GM KE LS MW SD SZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE BF BJ CF CG CI CM GA GN GW ML MR NE SN TD TG |
|
DFPE | Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101) | ||
121 | Ep: the epo has been informed by wipo that ep was designated in this application | ||
WWE | Wipo information: entry into national phase |
Ref document number: 88762/98 Country of ref document: AU |
|
WWE | Wipo information: entry into national phase |
Ref document number: 1998940436 Country of ref document: EP |
|
ENP | Entry into the national phase |
Ref document number: 2302264 Country of ref document: CA Ref document number: 2302264 Country of ref document: CA Kind code of ref document: A |
|
NENP | Non-entry into the national phase |
Ref country code: KR |
|
WWE | Wipo information: entry into national phase |
Ref document number: 503279 Country of ref document: NZ |
|
WWP | Wipo information: published in national office |
Ref document number: 1998940436 Country of ref document: EP |
|
REG | Reference to national code |
Ref country code: DE Ref legal event code: 8642 |
|
NENP | Non-entry into the national phase |
Ref country code: CA |
|
WWG | Wipo information: grant in national office |
Ref document number: 88762/98 Country of ref document: AU |
|
WWG | Wipo information: grant in national office |
Ref document number: 1998940436 Country of ref document: EP |