Patent Application
20180129739
The present invention is in the field of information systems. More particularly, but not exclusively, the present invention relates to user interfaces for graph-based information architectures.
User interfaces for capturing information from users are a fundamental part of computer science.
The current ontology for capturing information from users involves the provision of a visual display in combination with an input device(s) such as a touch-screen, mouse, and/or keyboard. The visual display guides the user in the nature of the information that can be captured by displaying various user interface (UI) elements, such as text boxes, slider bars, radio buttons, etc., and guiding the user through a user experience (UX).
Specific types of information capture benefit from the design, development, and deployment of specific user interfaces (UIs) incorporating a specific user experience (UX).
In the past ten years, information recordal and representation has evolved from simple text documents to hyper-linked documents and websites to graph-based technologies such as Facebook.
There is a desire for user interfaces to similarly evolve to provide contextually assisted capture of information for graph-based technologies. An improved user interface for graph-based architectures may be particularly useful for online social systems because these systems tend to be broadly targeted and may be used by users who are not particularly computer-savvy. Existing prior art in online social systems, which range from dating sites to Linked-In to Facebook, base their user input around a digital representation of the fixed form. An online social network profile is a predefined form and, therefore, the scope and number of its permissible attributes (fields) are controlled by the system's developers and not its contributors. As such the terms of engagement for interactions on prior online social systems are entirely defined by the system's developers yet the full scope of user interaction cannot be foreseen a priori.
Many such systems that depend on the collection and interaction of user-provided attributes and intentions employ alternative methods of user information collection; they massively supplement their inherent fixed-form limitations with user-attribute inferences calculated from the programmatic analysis of extended user-generated blocks of text in such forms as comments, emails and notes.
A significant disadvantage of such systems is the error-prone inference about user intention; it does not enable the user concerned to explicitly declare their intention, or not. Furthermore, the development of the various instances of this intentional inference system are often motivated by monetising user information to the advertising market and not by any desire to improve the connectivity or networking potential from the user's perspective. As these online social systems are form-based, this limits the system's developers to controlling all data entry parameters and thereby restricting the system's informational scope and its ability to signal intent.
It is an object of the present invention to provide a method and system for defining information within a graph-based architecture which overcomes the disadvantages of the prior art, or at least provides a useful alternative.
According to a first aspect of the invention there is provided a computer-implemented method of defining an entity for a graph-based information system, including:
i) receiving user input at one or more processors to create or select one of a plurality of options for a node in a graph;
ii) receiving user input at one or more processors to create or select one of a plurality of options for one or more child nodes in the graph;
iii) defining a value for the created/selected option for one of the child nodes at one or more processors in response to user input; and
iv) storing the value in at least one memory;
wherein the node and one or more child nodes are displayed within a user interface at a user device.
According to a further aspect of the invention there is provided a system for defining an entity for a graph-based information system, including:
A user device comprising an input and a display, the user device configured for providing a user interface;
One or more processors configured for receiving user input to create or select one of a plurality of options for a node, receiving user input to create or select one of a plurality of options for one or more child nodes; defining a value for the created/selected option for the one or more the child nodes in response to user input and storing the value in at least one memory;
One or more memory configured for storing the node, the one or more child nodes, and the value for the one or more child nodes.
wherein the node and one or more child nodes are displayed within the user interface at the user device.
Other aspects of the invention are described within the claims.
Embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings in which:
The present invention provides a method and system for defining information within a graph-based architecture.
The inventors have discovered that visually representing the graph structure to a user and providing the ability for the user to create or select input options for a node provides a richer informational structure which remains easy to conceptualise and use.
In
The system 100 may comprise one or more user devices 102.
Each user device 102 includes an input 103 and a display 104. The input 103 may be one or a combination of mouse, touchpad, keyboard, or other means for input (e.g. gyroscopes, audio, etc.). The display 104 may be an LCD or OLED screen. The display 104 and input 103 may be formed of a touch-screen display.
Each user device 102 is configured to provide a user interface to display information on the display 104 and to receive input 103 from the user via the input.
Each user device 102 may include other conventional hardware such as a processor, a memory and a communications module for communicating with other apparatus.
A server 105 is also shown.
The server 105 may include one or more processors 106.
The server 105 may include other conventional hardware such as a communication module for communicating with other apparatus.
The one or more processors 106 may be configured to receive input from the user via the user device 102 to create or select options for a node and for child nodes. The one or more processors 106 may also be configured to receive input from the user via the user device 102 to define a value for one or more of the child nodes. The one or more processors 106 may be configured to store the value, the node, and/or the child nodes in one or more memory 107.
It will be appreciated that in different embodiments each of the one or more processors 106 may be configured to perform separate functionality, or all of the functionality described.
A communications system 108 is also shown. The communications system 108 may be comprised of one or more communications networks, such as cellular networks, wireless networks (such as wifi) or wired networks (such as Ethernet LANs or WANs). The networks may be interconnected.
The user devices 102 and the server 105 may be configured to communicate via the communication system 108.
It will be appreciated that in alternative embodiments, the user device 102 and server 105 may be a part of the same apparatus and be co-located. Alternatively, the one or more processors 106 and one or more memory 107 may be arranged in a distributed hardware architecture and linked via a communications infrastructure.
With reference to
In step 201, input is received (e.g. at processor 106) from a user (e.g. via user device 102) to create an option or select an option for a node in a graph. A list of least some of plurality of possible options may be displayed (e.g. at the display 104 of the user device 102) to the user to select from. The possible options may be generated (e.g. at processor 106) based upon previous options for corresponding nodes created or selected by other users in other graphs. Corresponding nodes may be determined based upon a correspondence in relationships between the nodes in the other graphs and relationships between nodes in the user's graph. The list of possible options displayed may be generated based upon frequency of use by other users.
The list of possible options may be displayed as visual elements radially surrounding a parent node in the graph. The user may select the possible options by actuating (e.g. at input 103 at the user device 102) its associated visual element. One visual element radially surrounding the parent node may represent a “new” option. The user may select this new option (e.g. at input 103 at user device 102) and enter information to create the option for the node. During provision of the information, one or more possible options which at least partially match the information may be displayed to enable the user to quick select an option for the node.
In step 202, input is received (e.g. at processor 106) from the user (e.g. via user device 102) to create an option or select an option for one or more child nodes in the graph. A list of least some of plurality of possible options may be displayed (e.g. at display 104 at user device 102) to the user to select from. The possible options may be generated (e.g. at processor 105) based upon previous options for corresponding nodes created or selected by other users in other graphs. Corresponding nodes may be determined based upon a correspondence in relationships between the nodes in the other graphs and relationships between nodes in the user's graph. The list of possible options displayed may be generated based upon frequency of use by other users.
The list of possible options may be displayed as visual elements radially surrounding a parent node in the graph. The user may select the possible options by actuating (e.g. at input 103) its associated visual element. One visual element radially surrounding the parent node may represent a “new” option. The user may select this new option (e.g. at input 103) and enter information to create the option for the child node. During provision of the information, one or more possible options which at least partially match the information may be displayed to enable the user to quick select an option for the child node.
A link may also be specified between the node and the child nodes. In one embodiment, this link may be specified by the system as “related to”—that is, the child nodes are related to their parent node. In another embodiment, the user may modify or specify the link between a parent node and their child nodes (e.g. at user device 102). This link can therefore define the nature of the relationship between the parent node and the child node.
In step 203, input is received (e.g. at processor 106) from the user (e.g. via input 103 at user device 102) to define a value for one or more of the child nodes. The type of input may be defined by the child node. For example, if the child node relates to time (e.g. “at”) then the input type is defined as time and input must be a time value (e.g. 13:00 or 1 pm).
An input form may be displayed to the user in a user interface (e.g. at user device 102). The input form may assist the user in providing a value of a type defined by the child node. For example, if the child node relates to date (e.g. “on”) then the input type is defined as date and the input form may be calendar to enable the user to select a date.
In step 204, the value is stored in a memory (e.g. by server 105 in memory 107). The created/selected options for the node and one or more child nodes may also be stored (e.g. in memory 107).
In one embodiment, the value may be a link to an external data source(s) and dynamically retrieved.
With reference to
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With reference to
The graph-based architecture includes a set of graphs of nodes. The set of graphs of nodes may be stored in one or more memory (e.g. 107). Each graph may have been defined by the method 200 described in relation to
Each graph is associated with an entity. In one embodiment, the entities represent users. In an alternative embodiment, the entities represent a type of information (e.g. a document). It will be appreciated that the graph-based architecture may be adapted to work with any type of entity which is associated with structured information.
In step 301, input is received (e.g. at processor 106) from a user (e.g. via user device 102) to define a plurality of related nodes. The input may be received via a user interface provided on a user device (e.g. 102). The nodes may be related such that the nodes form a graph. In one embodiment, to define the related nodes, the user may create a connected graph of nodes. In another embodiment, to define the related nodes, the user may select (e.g. at a user device 102) a plurality of nodes from a larger predefined graph of nodes. Some or all of these selected nodes may also be connected to one another within this larger graph. The larger predefined graph of nodes may represent an entity. The entity may be related to the user. For example, the entity may comprise information about the user such as name, date of birth, job, interests, etc. The larger predefined graph of nodes may have also been created in accordance with the method 200 described in relation to
In step 302, the defined nodes are searched within the set of graphs for matches (e.g. by a processor 106). One or more matching engines may be used to search within the set of graphs. The user may define which matching engines to use and/or parameters for the matching engines to calibrate the closeness of the match (i.e. thresholds). The matching engines may include the following engines:
a) An exact match engine—this engine finds matches by finding those graphs which have connected nodes in same arrangement as the defined nodes;
b) An exact skip match engine—this engine finds matches by finding those graphs which have connected nodes separated by no more than a predefined number of nodes as the defined nodes. The user may be able to predefine the number of nodes before the searching begins;
c) A thesaurus match engine—this engine operates the same as (b) but includes nodes that match synonyms of the defined nodes;
d) A data match engine—this engine operates the same as (b) but matches nodes when their associated values are within a specified range. The user may be able to specify the range;
e) A translation engine—this engine operates the same as (b) but matches nodes that match translations of the defined nodes. The user may be able to define which languages that can be used; and
f) A complementary engine—this engine operates the same as (b) but matches nodes that are complementary to the defined nodes (for example, a node that specifies that something has been “Lost” might be matched to a node that specifies that something has been “Found”).
In one embodiment, the matching engines match the nodes within the graphs via inferences. For example, if a defined node specifies a birth-date, then an age may also be dynamically generated to match to nodes specifying ages. In one embodiment, inferences for a node are not generated dynamically but generated periodically and stored associated with the respective node.
In one embodiment, the nodes within the graphs and/or defined nodes are connected via links (or edges), and a link represents the relationship between a parent node and child node. The matching engines may also utilise the nature of the links between the nodes to locate matches.
In step 303, entities with graphs that match the defined nodes are retrieved (e.g. from a memory 107 by a processor 106) when the level of match exceeds a threshold or thresholds. The threshold(s) may be those defined for the matching engine used. The matching engines may use predefined thresholds. One or more of the thresholds may be provided or modified by the user.
The retrieved entities may be displayed to the user (e.g. at display 104 on user device 102) in real-time as they are located. In one embodiment, only the matching nodes of the retrieved entities are displayed.
Embodiments of the invention will now be described with reference to
In these embodiments, the graph architecture is used for providing an information matching network and includes a plurality of graphs. These graphs will be termed Ontys.
There may be two types of Ontys: a User Onty and a Master Onty.
The User Onty represents an entity and contains nodes which relate to that specific entity. For example, the entities may represent human beings and the User Onty may represent a social profile. In this way, the graph architecture may be used to provide a social network.
In this system there will be many User Ontys.
The Master Onty is constructed from nodes in the User Ontys and represents all possible options for a new node from a specific parent node.
The system of these embodiments differs from existing graph-based social networks because each users profile is convoluted with the graph. Contrary to existing social networks, the graph is user defined, flexible and versatile. The system imposes no prior expectations on the user's intentions as regards how the user may wish to describe whatever they wish, or why.
During creation of a User Onty by a user, the possible options for adding nodes to the User Onty are suggested by the system using the anonymous contributions of others to the Master Onty. In this way, an interesting collective, anonymous, self-organising information representation system may be created as an emergent property of the information architecture. Every subsequent user to the previous can benefit from prior input; at any point in their graph they can ‘inherit’ a suggestion or, if so required, contribute something new and so offer a mutation. This may create an interesting combination of collective feedback through anonymous cooperation.
Due to the flexibility provided by the system, a user's profile is richer than typical social networks and can be considered more as a “narrative”.
Users build digital narratives by creating a graph of nodes linked by edges within a user interface provided by the system. The graph is displayed as a radial network comprising the centre node with connected child nodes of a number up to a maximum defined by visual space economy of the display device in question. The number of displayed levels (generations) of the graph is also dictated by display size however this will typically be two, the centre focus node and the next level down.
The graph view includes two types of node, both colour- and size-coded. One type is hints, which are suggestions provided by the system from a frequency selection of the aggregate input of all other users at this point in the graph (see later for a more comprehensive explanation). These hints can be added to the current user's graph and at such point then they become the second type: a user element in that user's individual graph. When the desired term does not feature in the hints, the user may enter a new term. At such point the new term becomes an element of their graph and furthermore a hint in all other users graphs. If the system recognises the new term being entered as a non-displayed hint (by limit of the frequency of use selection) then this is automatically suggested as the hint in question.
The links connecting nodes default to ‘related to’ or associate numerical data to a node (metrics, dates, times and location coordinates). Users may however narrow the semantic content of any link type and define such to be, for instance, ‘want’, ‘seek’, ‘offer’.
When new terms are entered, the user may require to restrict this term from featuring amongst the anonymous hints for others, for example in the case of their name. In such cases the user can set a user element to be hidden. Hidden nodes will not then be suggested for addition to all other users at that point in the graph.
Users may navigate through their ONTY by clicking on a child to drill down in that direction. In such case the selected child becomes the central node with focus and the next generation of connected nodes are displayed. To return back they click the centre node. Alternatively users may use a map view to display their entire graph and navigate to any point within.
Users can delete individual leaf nodes or can agree (when challenged) to delete the current node and all its descendants (if selecting to delete a node with children).
For any individual node or collection of nodes in a tree, if the current delete action results in the removal of the final use of that node/node collection across the user population, then those nodes drop off the master hint nodes graph.
Nodes or collections of nodes can be moved from one parent to another by dragging the lowest level member of that tree from one location to another on the graph, by dragging the graph subtree on to its intended new parent.
Users can search for elements within their graph, including synonyms.
As well as lexical elements, users can add elements to their graph denoting numerate measurements, dates, times or the coordinates of physical locations. All such numerate elements do not contribute to the master graph or then become hints for other users. They are labelled DataOntyElements.
When a user adds elements to their graph that require accompanying data, they can select to link a data element to that term. In such case the system offers the choice of 4 data capture dialogues: metric/date/time/location. In many instances, typing the associated concept, e.g. weight, will trigger the option to provide the measurement in question using the appropriate input dialogue.
Once a data entry dialogue type is selected (or suggested), the user can enter the relevant data and select the unit system they require. The system stores the user-defined measurement-unit pair and also the SI equivalent if this is differs from that given.
In the case of a metric data element, for example, the user may wish to select/add a node called ‘length’ and then use the metric data type dialogue to associate ‘50’ & ‘metres’ with that node.
Alternatively, a user can add a date value or a date range to an element of their graph by selecting such data from a calendar dialogue. Dates are simple integer dates with no implied time zone reference.
If so desired, users enter the time required from a clock dialogue. Users can elect to store a matchable time of day as invariant across time zones, for when the local time of day is what matters, irrespective of the user's time zone. Alternatively, users should assign a corresponding time zone to any given time of day. This is necessary when matching an instance of time (e.g. a deadline) is what counts across all time zones concerned. When time zones are given, time of day will be stored in GMT.
Users may associate a location data element to an element of their graph by selecting a point on the map on the (searchable) cartographic dialogue. This results in the system storing the geographic coordinates of that location (and not any associated place name). When users add nodes to their ONTY containing place names such as ‘London’ or ‘New York’ these have no associated coordinates and assert only their semantic value. Such use of place names is included in the Master Onty hint system.
The system reserves another type of node—dynamic—for cases when the user wishes to incorporate an external, linked, and therefore variable, element into their graph. This data source is therefore elsewhere on the internet and must be linkable and, for instance, interpretable either from its position on the source page or via identification as an xml element. Like other DataOntyElements, dynamic nodes do not contribute to the Master Onty.
In one embodiment, a user can use their individual graph—their user Onty or uOnty—to perform a search for matches across all other users' uOntys. Hence a user can look for matches with other users based on the contents of their own uOnty. The searching user selects any subset of their uOnty to construct a search Onty (sOnty). The system then analyses the entire database for matching components of other users' uOntys to this sOnty.
To search for matches, the system employs an extensible number of matching modules, or MatchBots. Each MatchBot specialises in seeking matches in a different manner, e.g. word semantics or event coincidence. Different types of matching components may include, but are not limited to:
Different MatchBots can be combined into a MatchBox to perform an overall match based on different subcomponents. The system presents a default MatchBox configuration to perform searches. This is a reasonable combination of the set of MatchBots available at that time. As the total number of MatchBots are expected to increase with time, constant tuning of the default MatchBox matching engine is also expected. In addition to the default MatchBox, search architecture customisation is available for those users who wish to experiment with their own matching procedures.
When a user effects a search, the MatchBox proposes connections between the searching user and one or more possible matches. Both the searcher and searched can then inspect the proposed match by viewing the intersection—but only the intersection—between the searcher's sOnty and the target's uOnty. The pair may now communicate using integrated instant messaging. If three messages are exchanged then a bind is created between those two users based on that sOnty.
As such, the sOnty and its match(es) form the ‘switchboard’ that enables connections between any pair or collection of user pairs. Proposed connections may be rejected or the corresponding user blocked at any stage.
Users can define and vary the precision of the MatchBots in order to tweak the filter of results. Any numerical or range based element in the sOnty can be assigned a tolerance or margin of error in various matching subroutines.
Semantic tolerance and narrative flexibility can be adjusted by altering the maximum number of element to element hops between any matching term in one user's graph compared to that of any other.
In some cases, to produce useful matches, supplementary information may have to be inferred from that given by the users in question. In such cases there may be an information gap between the actual declared data in a user's Onty and the internalised, logical or deducible information that user may assume is understood, either by the system, or by any prospective matching user. For example, a user may provide a DOB and assume the system will be ‘aware’ of their age. The system in this and similar cases employs an Additive Inferer to add the missing information to the uOnty in question.
Other logical inferences that the system may have to make in order to augment the given uOnty with all necessary information could be for example to infer the date range period a certain user may intend when declaring a particular season, given their location hemisphere.
Moreover, binds between graph-object owners may depend on complementarity as well as similarity. Complementary matching is driven by inferring potential relationships between two user graphs that go beyond that explicitly declared, just as one can infer a positive and negative electrical charge attract. For example, in a dating context, a hetero female user would seek the complementary male users.
When any pair of users create a bind by accepting a connection proposal, or reject/block that proposal, this represents a piece of information. The matchbots can store this information: the components of the sOnty, matched uOnty and the users concerned, plus the acceptance/rejection outcome. Over time the matchbots can use this information to improve on the match proposal behaviour.
As searching or matched users accept valid binds, converse and do not block each other, the matchbots can again store this information and use it to accrue a reliability rating for each user on the system. This in turn they will use to weight the higher reliability rating users above others in match proposals. Once a conversation is in progress, any individual message may then be flagged as being of potentially nefarious and requiring inspection. Both these mechanisms contribute to the bottom-up self-policing nature of the system.
Directed searches can be one shot or the searcher can direct them to persist and in such case they may trigger new match proposals when relevant novel user data is provided by a potential match partner. Hence the searching user can select a frequency at which they wish the sOnty search in question to run, from daily upwards. Hence if new matching uOnty data comes on to the system at a future date then this will get picked up by a persisting scheduled search and trigger notification to the users concerned.
System Structures
Exemplary structures for the system above will now be described.
Ontys
Onty is the collective noun for the Onty Elements.
Master Onty (mOnty)
The mOnty is the collection of Master Onty Elements (MOEs) in the system. There should be no replication in the mOnty, each element is unique. Measurements, Dates, Times, Latitude/Longitude pairings (DOE values) should not be stored in the mOnty. All elements in the mOnty are lowercase, and this is set regardless of the case of the uOnty.
User Onty (uOnty)
The uOnty is the collection of User Onty Elements (UOEs) for a given user.
System Elements
System elements are elements used throughout the system itself, they're generally augmented when passed out to an API call, and simplified for creating. This section details the internal elements. The section on API Elements below provides information on the elements passed in and out of the API.
There are 3 types of Onty Element, the User Onty Element (UOE), the Master Onty Element (MOE) and the Data Onty Element (DOE).
Master Onty Element
The MOE represents an element that has either been seeded or generated when a user creates their UOE. Collectively they are the Master Onty (mOnty).
Properties
User Onty Element
UOEs represent user entered data. Together they form a User's Onty.
Properties
Data Onty Elements (DOE)
Data elements are elements that store information other than the value of a UOE, for example, a user might put the value of ‘Brighton’ into their UOE, and then use the DOE to specify the actual location using Latitude/Longitude.
Nine types of data elements in the system will now be described. It will be appreciated that the system may include one or more of these data element types:
1. Date Data Onty Element (DDOE)
The date element is just a date, with no time information stored.
Properties
Example JSON
2. Time Data Onty Element (TDOE)
The time element is just a time, with no date information stored.
Properties
Example JSON
3. Measurement Data Onty Element (MDOE)
The measurement elements are for storing any SI (International System) units. The units are defined by the user (for example Centimetres, Centigrade, Stones) and stored in the base SI unit, so if someone stored −273.15° C. the underlying structure would store the actual value entered by the user and 0° K as well.
Should the user want to store some other form of measurement, they can simply add their own user defined types and they will be stored with the base unit also being the user defined type (no conversion to a base SI unit as none would be known).
Properties
Example JSON
4. Location 2D Data Onty Element (L2DDOE)
The location element is an element with Latitude and Longitude properties.
Properties
Example JSON
5. Location 3D Data Onty Element (L3DDOE)
The location 3D data onty element represents a point in 3-dimensional space.
Properties
6. Vector Data Onty Element (VDOE)
A VDOE can be used to represent a vector in terms of magnitude and direction, usually these will be part of a Journey or similar.
Properties
7. Time Zone Data Onty Element (TZDOE)
The TZDOE represents a specific Time Zone for an element. A TZDOE is a special class of DOE that is generally attached to another DOE, (in this case a Time Data Onty Element, see Onty Elements document), though there is no restriction on what it can be attached to.
User A is looking for someone to talk to about a cloud service that had a failure at 4:45 GMT. User A knows when it happened for them, and so creates their Onty as shown in
User B is also looking for someone to talk to about it, they're based in the US (East Coast), but haven't added any location data, so they add to their Onty as in
Now, when either of them searches for the nodes shown in
They will match, as the system will be able to tell that User A's nodes in their Onty as shown in
Is a match for User B's nodes in their Onty as shown in
Properties
The TimeZone property can be either a valid Time Zone (GMT+7 for example) or None, Invariant, or Locked. Locked would mean any searches would need to be exact matches, Invariant and None are similar in effect, and mean that the Time Zone can be ignored.
8. Dynamic Data Onty Element (DyDOE)
DyDOE's are special versions of the other Data Onty Elements, where the value is dynamically updated, an example would be something like an ‘Age’ node which has an MDOE of {“Unit”:“Years”, “Value”:“25”} which is valid this year, but would be ‘26’ next year.
Properties
A DyDOE has the same properties as a particular version of the DOE it's representing, but also has extra properties:
9. Linked Data Onty Element (LnkDOE)
An LnkDOE provides a way to link to external sources, (such as the UK Met Office or Magic Seaweed) to be able to pull data without having to manually type it. As an example, a user might have forgotten the surf conditions when they were away on a particular day, and they can import the data from Magic Seaweed and save it to their own Onty.
Properties
An LnkDOE has the same properties as a basic DOE, but also has these extra properties:
The source could be a website, or it could be another element in the users Onty itself.
API Elements
These are the elements crafted by a client to POST or PUT or DELETE from the API, and the shape of the responses in GET, POST, PUT and DELETE cases.
Unlike the System Elements there are (broadly) only two types of element, the Master Onty Elements are not returned to the User.
Creating a New UOE
When adding a UOE to a user's onty, the element details and any data elements are sent along at the same time.
Properties
Example JSON
Data Elements
The same five types as outlined in above, but they use a simplified format for adding new data.
General
All elements are added with two properties, Type which defines the type of the data element being added, and Values which define the properties.
Date
To add a DDOE the type is set to Date and the Values must contain at least StartDate but it can also include EndDate to represent a date range.
Time
To add a TDOE the type is set to ‘Time’ and the Values must contain at least StartTime but it can also include EndTime to represent a time range.
Measurement
To add a MDOE the type is set to ‘Measurement’ and the Values has to include the Unit and Value properties.
Location (2D and 3D)
To add a L2DDOE or an L3DDOE the type is the same ‘Location’, if only the Latitude and Longitude values are set then you get a 2D element, if Ellipsoid is also set, you'll get a 3D element.
Multiples
Any UOE can have multiple data elements attached, simply add all the elements to the Data property of the UOE.
Example JSON
In this JSON example we have two Data elements, one Location and one Measurement.
Deleting a UOE
When deleting a UOE all that is required is the element's ID.
Properties
Example JSON
Updating a UOE
A UOE can have three properties updated, the Value property, the IsHidden property (whether or not the element is private—in terms of restricted from the master ONTY), and the Data Elements property. All three can be updated at the same time, or just one.
Properties
If any of the properties are left as null, it is assumed that they are not being changed.
Example JSON
Search API Elements
There are two main elements for searching, the Query that is used to create the search, and the Results which contain the results.
Query
A Query contains everything the bots need to be able to search in the system.
Properties
Stored Search
A Stored Search contains all the information about the search that was made, and the results.
Properties
Stored Search Result
A Stored Search Result (SSR) contains information about the specific search instance.
Properties
Search Results
The Search Results object wraps the results with a status message and any other messages returned from the system (including bots).
Properties
Results
The results object represents the results from all the bots that were used in a particular search.
Properties
Result
A result object represents the result from a specific bot.
Properties
Query Completion Information
The Query Completion Information (QCI) object contains the information about whether or not a search has completed, when it completed, but not the actual results.
Properties
System Operations
Exemplary operations provided by the system will now be described:
sd Create Onty Element
The sd Create Onty Element diagram shown in
This process is asynchronous.
sd Delete Onty Element
The sd Delete Onty Element diagram shown in
This process is asynchronous.
sd Delete Onty Element Chain
This method as shown in
This process is asynchronous.
sd Update Onty Element
The sd Update Onty Element diagram shown in
This process is asynchronous.
The below sections follow on from above:
Private UOE Staying Private
Private UOE Becoming Public
Public UOE Staying Public
Public UOE Becoming Private
sd Move Onty Element
Moving a UOE involves changing one of the prior or subsequent links, only one may be changed by a user at a time.
Description
Searching Sequence Diagrams
Searching within the system is shown via two diagrams, as the process diverges depending on the time taken for a search to complete. All searches begin with the website but if a search takes longer than 1000 ms it is sent to a specific application to do the search.
sd Search From Website
If the search completes within 1000 ms then the process is completed by flow of the diagram shown in
Description
The process is asynchronous.
sd Search Long
This process as shown in
Description
cmp Bot Hoard
The bot hoard contains and manages the various Bots in the system. It's hosted in the website itself, and in the search worker.
Components
The components shown in
MatchBots
These are the actual bots, the hoard can contain any number of bots, as long as each bot implements the IMatchBot interface. The individual bots are described in greater detail later in this document.
DictionaryManager
Handles interaction between any components (e.g. bots) and the Dictionaries.
MasterOntyRepository
Manages all access to the Onty Store relating to the Master Onty Elements.
DataOntyRepository
Manages all access to the Onty Store relating to the Data Onty Elements.
cmp Search Service
The cmp Search Service diagram in
Diagram
SearchService
The public facing REST service to allow a user to search. The website hosts this API to interact with the system when attempting search related actions (such as executing a search and retrieving results).
Search Manager
The search manager initially deals with the searches by a user. It will attempt to match, but should the search hit a timeout limit it will cancel the search and pass it to the Search Queue (via the CloudQueueManager) to be read by the Searcher.
Search Bots Hoard
The hoard is the store of the Bots. It has a collection of Bots and each search is run past each bot (unless specified by a user). Each bot will firstly say whether it can parse the query, and if so will attempt a match.
SearchRepository
The search repository manages all access to the Search Store, any request to add, update, delete from the store is done via the Search Repository.
CloudQueueManager
The Cloud Queue Manager manages all access to the Search Queue, any attempt to read from or write to the queue is done via the CloudQueueManager.
Searcher
The searcher runs in a separate process and is used for executing long running searches (searches which have hit the timeout limit for the search manager)
Search Queue
A queue to put long running searches on to. The SearchManager will put searches onto this queue if they take longer than a set timeout time. The Searcher will read off the queue and run the search independently.
Search Repository
The search repository manages all access to the Search Store.
Search Store
A database to store the queries made by users and the results. A search is stored into the database as soon as it is executed (without results), when results are found they are attached to the search in the database for retrieval later.
cmp Example Match Box Bot and ad Example Match Bot
The example MBB demonstrates an abstract bot design where one MBB hosts many (in this case seven) other bots.
Component wise—MatchBot A-G are exemplary only.
Description
Inferers
Inferers are components of the system that infer onty elements based on a user's Onty. Two such inferer type are Additive Inferers and Complementary Inferers.
Additive Inferers
Additive inferers add elements to a user's Onty. Generally these work when a user enters a new value for their Onty and that value is parsed to see if any inferences can be gleamed from it. As an example, a user adds an element ‘Born’ with a Date Data Onty Element of ‘1980-01-14’, an additive inferer might add an ‘Age’ element with a Dynamic Measurement Data Onty Element with a Value of ‘35’ and Units of ‘Years’
Complementary Inferer
A Complementary Inferer (CI) infers elements but doesn't add to the user's Onty.
Inferences are driven by the content of a uOnty, and will look for specific patterns to be able to perform inference, the initial adding/removal of an element merely begins the search for inference possibilities, generally a single element will not be enough to infer with.
Complementary inferers wouldn't typically be run whenever a user adds any element but whenever a keyword is added to (or deleted from) the uOnty. Each inferer would define a list of keywords it is interested in, whenever an action is committed that affects an element, the inferers are asked if it's something they're interested in or not.
If an inferer is interested it would attempt to perform inference. It's quite possible (likely even) that the inferer won't be able to do an inference initially, as it will require more information.
Adding and Removing
A CI has a more complicated role than that of an Additive Inferer as its inferences are generally made from more complex patterns involving multiple elements.
Adding
If a CI is adding then location needs to be well thought out, and careful checks need to be made to ensure no duplicate information is added. The addition of an element by a user that was previously inferred (so, an inferer has added ‘Female’ and the user subsequently adds ‘Female’ as well) should upgrade the inferred element to a fully-fledged uOnty element.
Removing
A CI needs to remember what it's added to a uOnty, and remove any invalid inferences as and when a user removes/renames an element. It should treat each change as if it is the first time it has seen the uOnty—not base its decisions on prior knowledge of previous uOnty states. Importantly it should only remove inferred elements that haven't been upgraded.
Implementation
There are a few implementation options, depending on performance of the inferers and how up-to-date inferences are required to be.
Implementation 1
To save excessive workload, the inferers will store their relative success/fail scores against the user. The format of the information would contain the following fields:
Implementation 2
One option is to store a secret inferred set of Onty elements (not visible to the user), but allowing searches to incorporate the hidden elements. In the following example, we'll be discussing a ‘sexuality’ inferer (SexInf), which makes inferences about connected uOnty elements based on information provided by a user, so the user starts with the uOnty shown in
At present, searching for the sOnty in
Would return a lot of results, some of which would be undesirable—the user doesn't want to find Females looking for Males, or Males looking for Males, or indeed Females looking for Females. All of which are potential matches. The SexInf reads the users uOnty and is looking for something that matches the pattern shown in
Where [SEX] is a keyword like ‘Male’, ‘Man’, ‘Woman’, ‘Female’ etc and [SEXUALITY] is something like ‘Heterosexual’, ‘Straight’, ‘Gay’, ‘Homosexual’, ‘Lesbian’ etc. With 1 . . . n connections between. In practical terms the pattern would need to be more complex, this is just an example.
In the example case, this matches with the highlighted values shown in
As it's matched with the uOnty, a secret inferred set of elements are added as shown in
The user doesn't see the inferred elements but searches could take it into account (not all bots would use/know about the inferred elements). So if the user now searches for the sOnty shown in
For some bots, that's all they'll see, but other bots will see the inferred elements as shown in
Implementation 3
This takes the first and second implementation methods and does both of them. This is the most likely scenario as it provides the best performance.
Aside from the dating/sexuality example, others would include inferences about things like the meaning of the word ‘Summer’—for example in the Northern Hemisphere summer is (roughly) June to September, but in the Southern Hemisphere it's from December to March.
Another example (again revolving around hemisphere difference) are Star Signs (astrological), which differ between northern and southern hemispheres.
Sequence Diagram
The diagram illustrating the complementary inferrer as shown in
System User Interface
An exemplary user interface provided by the system will now be described:
The UI consists of a main edit screen showing a localised, single level view of the user Onty. The single level view consists of a main root node, surrounded by a number (n) of child nodes. The number and nature of the child nodes can be altered under user settings.
The nature of the connections between root nodes and child nodes, is determined by the LinkedTo and LinkedFrom data supplied as part of the Onty node object.
The fundamental (lowest node count) user Onty structure is one formed of a single (root) node. This node is given the value of ‘Me’, with an associated unique elementId, on the creation of a new user. The fundamental UI construct is therefore as shown in
An example of the node (root/child) system where the number of child nodes (n)>0 is therefore as shown in
The user is presented with the following navigation UI constructs which are discussed in detail below:
Node Design
Each node within the UI construct has the possibility of displaying within itself a number of additional pieces of information. Currently normal (non-master nodes), display the number of child nodes from the selected node at their centre. Master nodes display an additional piece of information, which is the usage count for the master node, within the master Onty. This can be seen in
Each node is connected to other nodes by a single line. This denotes the LinkedTo relationship as defined in the user Onty Object Definition.
Navigation
Traversal of the user Onty is bi-directional (up and down). The main navigational concept can be seen as:
In each UI construct the UI performs an animation to bring the selected node to the centre of the UI, thus highlighting it. Additional rollover states are also applied to the root nodes (centre nodes), in order to signify that an event has been triggered.
Selection of the Root Node
If there are no parent nodes to the current root node, which occurs when the root node is equal to the user Onty Me node (top level node), then only the edit dialog is displayed, no upward traversal is allowed.
If there are parent nodes from the current root node, the user is presented with the following UI construct. Consecutive actions are then displayed in the flow shown in
The user can decide in stage two to select the root node and traverse upwards, or click on any part of the UI construct to cancel the action.
The root node of phase one and two becomes the child node of phase three.
Selection of a Child Node
The selection of child nodes to the root, is used to display a view of the nodes in the next level, and subsequently the ability to traverse down to that level. It is the reverse route to the selection of the root node. Consecutive actions are displayed in the flow shown in
In the case of downward traversal through the user Onty, the middle step displays a view of the next level down, so the user can make a decision on whether to traverse from the selected node. If they decide not to they can click on any part of the UI construct to cancel the action.
The child node of phase one and two becomes the root node of phase three.
User Onty Manipulation
Insertion
Insertion of new nodes into the user Onty has two main modes. These are:
Both insertion types are triggered by rolling over the requisite node. This can be a root node (centre node in the UI), or a child node (on the first outer circle). The difference in insertion between the root and child nodes is only apparent in the redrawing of the UI post insertion.
If a new node is inserted on a root node, post insertion the UI redraws using the current root node as the starting point. This is because the insertion is related to the current root node.
If a new node is inserted on child node, post insertion the UI redraws using the child node as the starting point. This is because the UI requires to view the child node as the new root node, in order to view the newly inserted node.
The flow in
The number of master Onty suggestions is less than or equal to 10, giving the current highest ranking suggestions in terms of master Onty usage counts.
In choosing to add a net new node, or to add a master Onty suggestion, the user is presented with a modal dialog. This dialog (shown in the centre of the diagram below), is pre-populated with a value for the insertion of master node suggestions, and is empty for net new nodes.
Update
Updates to nodes follow a similar pattern to inserts, and are triggered when any node is selected (see clicking on nodes in the navigation section).
As this flow (as shown in
Any changes to the node value or data elements, are saved on selecting save. The user can cancel the update action by choosing cancel.
Delete
Deletion of user onty nodes again follows a similar pattern to insertion. The main difference being the choice of sub menu UI construct being the delete construct (−). Deletion is illustrated in
On choosing the delete sub menu UI construct the user is presented with the delete modal dialog. The dialog displays the pre-populated information related to the chosen node.
There are two delete options, delete and chain delete.
The normal delete function will delete only the selected node. The system will then reform relationships between the parent node of the deleted node, and the child nodes of the deleted node, if they exist within the user Onty.
Deletion of nodes from the user Onty, does not delete the nodes from the master Onty structure.
The chain delete, will delete the selected node, along with any child nodes of the selected node as shown in
Move Relationship
In addition to the function for insert, update and delete, it is possible to reform the relationships between nodes within the user Onty. It is to be seen as a moving of relationships, not as nodes.
Each node within the user and master Onty has two properties; LinkedFrom and LinkedTo. These define the relationships between nodes. On forming the move functionality, is it based on the reclassification of the LinkedTo and LinkedFrom properties.
The move function requires a new user interface construct to provide a means of seeing multiple levels of the user Onty all at once. This construct is the Story Map.
The story map interface as shown in
On starting the drag operation, the relationship is broken between the chosen node, and its parent. If the drag operation is cancelled at this point, the UI construct will reform the original relationship, and no update will take place.
If the user starts the drag operation, and in so doing drags the selected node to within a few pixels of a different parent node, a new relationship link will appear as a preview. On dropping the node, when the new link is active, the relationship will be reformed and the user Onty updated. This is an automated process, with no user confirmation dialogs.
If the drag drop operation completes with the formation of a new relationship, the main UI construct will redraw using the current root node as the starting point.
Data element UI constructs
There are currently four UI constructs for the addition of data elements to Onty nodes. These UI constructs cover:
Date
The user can select dates within the UI shown in
Time
The user can click on the time input field (11:11 by default) within the UI shown in
Location
The user can pan and zoom the map within the UI shown in
Measurement
The user can select a unit type (feet, inches, kilogrammes etc.), and then enter a numerical value in the input box within the UI shown in
Exemplary screenshots of the user interface
Initial layout is shown in
Main elements:
Node interface is shown in
Main elements:
Node selection (click on node) is shown in
Main elements:
Insertion UI is shown in
Main elements:
Story Map
Organic view for the Story Map is shown in
Main elements:
Tree view for the Story Map is shown in
Main elements:
Matching Engines
Further detail will now be provided above the different matching engines or Onty Bots that can be used to search for matches across the User Ontys.
The following Onty Bot will be explained with references to
Exact Bot
The Exact Bot (EB) is the simplest bot in the system. It searches for the same Onty elements in another users Onty (uOnty).
User searches for the elements shown in
This matches with the rOnty on the following elements shown in
User searches for the elements shown in
There are no elements that match in the rOnty.
Exact Skip Bot
The Exact Skip Bot (ESB) runs in a similar fashion to the EB, but it has the ability to ‘skip’ or ‘jump’ over elements, making it more forgiving when searching.
Parameters
User searches for the elements shown in
This matches with the rOnty on the elements shown in
User searches for the elements shown in
This matches with the rOnty on the elements shown in
Thesaurus Bot
The thesaurus bot builds on the ESB and adds the ability for a user to look for synonyms as opposed to just the words selected. The synonyms are brought in from a dictionary/thesaurus. Parameters
User searches for the elements shown in
This matches with the rOnty on the elements shown in
In this example, A′ is a synonym of A and H′ is a synonym of H.
User searches for the elements shown in
This matches with the rOnty on the elements shown in
The bot has in this case looked up the synonyms (which may be limited to those synonyms which exist in the mONTY and the total set of hidden nodes) for A and H and then proceeded to search for matches (including steps as described in the ESB) for all possible combinations. So the rOntys shown in
Data Bot
The data bot is the most complicated bot at present, it will work in a similar way to the Thesaurus Bot, but works with the data elements attached to elements. This allows a searcher to vary the specificity of their query from something like the exact date, to 2 weeks either side of a given date (for example).
Data Elements
There are 4 types of data elements in the system:
Date
The date element is just a date, with no time information stored.
Time
The time element is just a time, with no date information stored.
Measurement
The measurement elements are for storing any SI (International System) units. The units are defined by the user (for example Centimetres, Centigrade, Stones) and stored in the base SI unit, so if someone stored −273.15° C. the underlying structure would store the actual value entered by the user and 0° K as well.
Location
The location element is an element with Latitude and Longitude properties.
Parameters
If any of the parameters are not supplied (which would be the normal case) then the defaults are used for any data elements. Parameters are only applied to data elements that make sense e.g. the Diameter parameter would not be applied to a Date data element.
The examples for this bot use the rOnty shown in
The colours for the relationships stays the same throughout the examples.
In the examples, the searcher is the one who has found a paddle, and is looking to see if the person who lost it has put the fact that they have lost it into Onty.
User searches for the elements shown in
With a ‘DaysBefore’ parameter of 2, this means Onty will look for dates from the 2nd of October 2012 to October the 4th 2012 (inclusive).
This matches on the rOnty as shown in
User searches for the element shown in
With HoursBefore set to 17 and HoursAfter set to 7 (covering the full 24 hours), which matches the rOnty shown in
This example shows the ability for the system to translate between SI units. The searcher queries using feet, but the rOnty is actually in Centimetres. In both cases the actual underlying data element is stored as the SI unit (in the case of length, this is Metres).
User searches for the elements shown in
With parameters of UpperBoundPercent and LowerBoundPercent of 10. This means that the user is searching for a range of 5 feet±10%—or in centimetres: 144≤size≤167.2, which matches the rOnty shown in
In this example, the searcher doesn't know the exact beach they found the paddle, so they've added a local town nearby, and by setting the parameters to allow a diameter of 0.2 they encompass the rOnty value as well as shown in
This matches the rOnty shown in
In a closer approximation to real life—a user would likely combine all the searches together, and so could perform a search shown in
Which with the parameters set as above would match to the rOnty. The user might initially make the query with different parameters—get no (or too many) results—and so tweak them, making them more/less specific as required.
Of extra note is the use of the Thesaurus toolset to match Lost and Missing in the first Element.
LinguaBot
The LinguaBot (LB) allows matches to be found when the languages of the various Onty users don't match. For example, someone in Sweden might search for ‘kyckling’-‘Wyandotte’, and be matched with a chicken breeder in the UK by: ‘Chicken’-‘Breeds’-‘Wyandotte’.
Parameters
A couple of examples to show the basic working of the LinguaBot.
User 1 (Swedish) searches for (with a Steps value of 2) as shown in
The LinguaBot translates ‘Kyckling’ into ‘Chicken’ and there are no translations for Wyandotte, so matches with User 2, who has elements as shown in
The ‘Breeder’ element is ignored as the user has a steps parameter value of 2.
User 1 searches for the same elements as above, but this time matches the rOnty shown in
As the bot works out that Kyckling (Swedish) is Chicken in English, and Rooster is a suitable match for it. The ‘Sale’ element is ignored as the searcher has a steps parameter of 2.
Inference Bot
An inference bot performs searches using inferences generally acquired from the Complementary Inferers. Given a query, the bot would need to investigate The elements of the query would be passed to the Complementary Inferers which will assess whether they can attempt inference. The Inferers will require full access to the user's uOnty, to be able to infer properly.
The end goal is to augment the user's original search query to get better results, but this needs to be done shrewdly as you need to add inferred elements in the right place, to prevent searches failing.
User A searches for elements as shown in
The bot passes the query elements to the Complementary Inferers which assess whether or not they can infer anything. If the inferer can infer, it adds its inferences to the query.
The bot interprets the ‘INFERRED’ relationships as ‘RELATED_TO’ to allow matching to occur, and a search is made.
Learning Bots
As bots are created independently of each other they can be written to do almost anything, learning is a big part of this, and the Onty system provides facilities to allow a bot to learn in a few ways.
Bot Memory API
To learn, a bot should be able to remember. Bots themselves have no direct access to the file system and so need another way to store their memory. The system provides a Memory API which lets a bot save its state, or any other data it wants to.
The data is only readable by the given Bot, and should be in the form of a JSON object, but other than that there are no limitations.
Feedback Based
Generally, bot learning will come from user interaction. A user will rate the results the bot has returned, and the bot can learn from the feedback how well it's doing. Ratings can be just on the results themselves—as soon as a user sees them, or it could be later, when a user has connected with someone, and discovered them to be either appropriate, or inappropriate.
A user searches for something with the element (Train) in it, the first time the bot returns a mixture of ‘train’ elements (train as in learning and train as in transport), and the user says the learning elements are invalid.
The bot remembers this, and over a period of time starts to promote ‘train-transport’ results over ‘train-learn’ results.
Of course, someone else could say the opposite, and the bot would react accordingly. There is nothing to stop a bot training itself for specific users, so it might only promote ‘train-transport’ for a particular user.
Self-Learning (Natural)
This is a more complicated than feedback based learning, as it requires a bot to assess itself, this could be in the form of keeping track of the performance of its querying, the positioning of inferred elements etc.
A bot executes a query and finds it takes longer than a predefined timescale (defined by the bot-creator). The bot continues to execute the query, but also moves it to an ‘Analysis’ queue where it can analyse the query and see if it can improve the performance. This might be by maybe splitting it into two and running independent queries, or some other method.
The next time the bot receives a query it checks to see if has analysed something like it before, if it has (maybe by some metric as simple as the number of elements) then it may parse the query to see if it can search in a more efficient way.
Long Term Search Triggers
A Long Term Search (LTS) is a search that is triggered on a schedule.
In a normal context—searches are instigated by a user. A user wants to search for something, they create their search, and execute it. For a LTS the initial steps are the same, but once the search has completed the search is saved and can then be periodically run. The system can cope with running a LTS from every minute to once a year (or even longer).
Once an LTS has been configured the user need not pay any attention to it until they get a notification that the search has completed and found a result (they can if they wish check the status of the search and see when it last ran at any time). The system will execute the search whenever schedule dictates.
It is entirely possible that a search might never return a match.
Interface
The beginning interface is the same as for the normal search, once a search has been executed the user can then look at their search (via My Searches) and then set the schedule.
The schedule only contains the information about the time to run the search, any other data (the content of the search for example) is all contained within the original saved query.
The sequence for the long term search is shown in
Sphere of Influence
The Sphere Of Influence (SOI) is a metric to improve (and rank) the results returned by a bot.
The following show basic examples of what the SOI calculates, in the examples, the user searching for others (User A) has the uOnty shown in
In this example, the user searches for a single element. In practice this would be discouraged as the number of matches is likely to be so high that the results would be useless.
User A searches for the 3400 shown in
This matches against the equivalent element 3401 in another user's (User B) uOnty as shown in
The 3401 and 3402 give a 100% percent match, but the sphere of influence is designed to take into account surrounding elements.
If we look one node out (ignoring direction) we end up comparing User A's elements as shown in
To User B's elements as shown in
A combination of the two is shown in
Ignoring the central node, as we only care about the nodes one node out, we get the graph shown in
Of the 5 nodes, 2 are matches, and the other 3 are not shared. This gives us a score of:
2/5=0.4
We next examine the next nodes out (so 2 nodes out from the root search) as shown in
In this case, there are no common elements, so the rating is:
0/4=0
These can be displayed as circle representation as shown in
Or simpler as shown in
Allowing User A to see how close a match is.
Multiple search items means that the circle representation as shown in
For this example, the user (User A) has searched for the elements shown in
And that has matched with a user's (User C) onty shown in
Again, we can ignore the initial ‘circle’ as that has a 100% match, and can concentrate on the nodes outside the initial match, so 1 node out:
For User A is shown in
For User C is shown in
And combined is shown in
We have 5 elements, 3 of which match:
3/5=0.6
We then expand to 2 nodes out, for User A as shown in
For User C as shown in
In this case, we have a 100% match as shown in
So the circle diagram is as shown in
Interpreting the Circle Diagram
Each ring represents a node out from the central (searched for) point. Generally the central point will always be 100% where a match has been made (potentially, some bots could provide matches based on partial matches).
Colours could be used to represent the match quality, allowing the diagram to be even smaller, with a mouse over showing details as shown in
At a glance you can see the quality of the match, the greener it is, the better the surrounding matches.
Privacy
The circle diagram doesn't show to the searcher what the matches are, merely whether there are matches and what percentage of the surrounding elements are matches.
Implementation
There are two ways to implement the SOI in the system.
Implementation 1—Helper
The helper method involves putting an SOI calculator into the Match Bot Hoard, so that all results are parsed by it and the SOI is attached to any search.
A Sequence Diagram is shown in
Implementation 2—Bot
The bot method involves creating a new Match Bot that takes in Results and outputs Results, in the process augmenting the results with SOI. The bot would need to be used in a Match Box form. This way allows a user to generate results without the SOI if they wish, as they could select bots without any SOI capability.
A Sequence Diagram is shown in
Data Design
The Sphere Of Influence values would be added to the Result objects returned from a search. The format would be Dictionary with a Key of Integer, and a value of Double. The key represents the number of nodes out from the centre, the value represents the actual SOI score.
Relationships Between Nodes
Some embodiments of the system may also provide for the definition of different types of relationships between nodes.
In these embodiments, a user can also set the type of their relationships, by default the relationship is ‘RELATED_TO’ as shown in
But a user can select from a predefined list of relationship types (including, but not exclusively):
Which results in the graph shown in
The LOST relationship type can be used as a substitute for using a separate node to indicate loss (as in
The relationship isn't needed (or indeed desirable) for subsequent elements as shown in
But there is nothing to prevent a user doing so, as shown in
Sequence Diagram
The sequence diagram for this is the same as the Create Onty Element diagram shown in
Data Type
The only difference between the existing Create data type would be the addition of a ‘Relationship’ property to the Create New UOE data type.
Search Bots
Use of different relationship types have an effect on the way search bots work, in particular generating queries would also contain the relationship information. So a query for two (or more) elements would have additional relationship elements defined.
Potential advantages of some embodiments of the present invention are that users are able to define information for a graph-based architecture in a way that guides them in providing information but does not constrain them; the closer correlation within the user interface between the information and the data structure assists users in comprehending the utility and purpose of graph-based architectures, for example, in search; and the user interface improves organically through use rather than by design providing an optimal environment for information recordal and, eventually, retrieval.
While the present invention has been illustrated by the description of the embodiments thereof, and while the embodiments have been described in considerable detail, it is not the intention of the applicant to restrict or in any way limit the scope of the appended claims to such detail. Additional advantages and modifications will readily appear to those skilled in the art. Therefore, the invention in its broader aspects is not limited to the specific details, representative apparatus and method, and illustrative examples shown and described. Accordingly, departures may be made from such details without departure from the spirit or scope of applicant's general inventive concept.
| Number | Date | Country | Kind |
|---|---|---|---|
| 1508629.1 | May 2015 | GB | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/GB2016/051473 | 5/20/2016 | WO | 00 |
