Patent Publication Number: US-2021174216-A1

Title: Signaling concept drift during knowledge base population

Description:
TECHNICAL FIELD 
     The present invention relates to systems and methods for populating a knowledge base. More specifically, the invention relates to systems and methods for signaling concept drift during knowledge base population. 
     BACKGROUND 
     Concept drift refers to the phenomenon that concepts change their meaning over time. When populating a knowledge base or knowledge graph, concept drift can result in a divergence between the intentional meaning of the types of entities and their relationships in the graph and the extensional meaning of the examples of entities and their relationships in documents being used to populate the graph. The clusters of entities and relationships can become less cohesive, thus changing the meaning of the types of entities and their relationships in the graph. This may disadvantageously result in the knowledge graph being less accurate over time. Known knowledge base population techniques provide no way of determining when concept drift occurs over time, and further provide no way of signaling to a user when concept drift occurs. 
     SUMMARY 
     An embodiment of the present invention relates to a method, and associated computer system and computer program product for signaling concept drift during knowledge base population. One or more processors of a computer system receive a knowledge graph and a collection of text. The one or more processors of the computer system build a vector space of the collection of text. The one or more processors of the computer system further receive a sequence of data items associated with: A) a type of entity; or B) a relation, in the knowledge graph. The one or more processors of the computer system embed entities or relations from the knowledge graph into the vector space to generate entity or relation vectors. The one or more processors of the computer system further embed data items associated with: A) the type of entity; or B) the relation into the vector space to generate data item vectors. The one or more processors of the computer system compute an emerging entity or relation concept vector by determining a centroid of the data item vectors and further compute an entity or relation concept vector by determining a centroid of the entity or relation vectors. The one or more processors of the computer system further generate a signal when a distance between the emerging entity or relation concept vector and the entity or relation concept vector is greater than a concept drift threshold. 
     The aforementioned embodiment advantageously signals when new entities and new relationships between entities begin to diverge from the types of entities and relations already in the knowledge graph. The present invention, for the first time, provides a user with an indication when an emerging concept is diverging significantly from a concept embodied by a knowledge graph. This improvement to the art of knowledge graphs utilizes vector space models to compute emerging concepts for new data items being added to a knowledge graph and to compute concepts for the existing entities and relations in the knowledge graph to provide insight to users related to concept drift. 
     In one optional aspect of the aforementioned embodiment, the building the vector space of the received sequence of data items uses embeddings and autoencoders. In another optional aspect of the aforementioned embodiment, the generating the signal includes highlighting data items according to a degree of difference with the centroid of the data item vectors. In another optional aspect of the aforementioned embodiment, the generating the signal includes presenting a data item outlier. In another optional aspect of the aforementioned embodiment, the distance is computed as a cosine distance or dot product distance. In another optional aspect of the aforementioned embodiment, the building the vector space of the received sequence of data items further includes building a graph of data item tuples where the magnitude of the vector is computed using a graph metric. In another optional aspect of the aforementioned embodiment, the sequence of data items corresponds to at least one of a recent time window or a data entry, and is generated by a program. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts a block diagram of a system for signaling concept drift during knowledge base population, in accordance with embodiments of the present invention. 
         FIG. 2  depicts a knowledge graph created by the system for signaling concept drift during knowledge base population of  FIG. 1 , in accordance with embodiments of the present invention. 
         FIG. 3  depicts a process flow performed by the system for signaling concept drift during knowledge base population of  FIG. 1 , in accordance with embodiments of the present invention. 
         FIG. 4  depicts a method for signaling concept drift during knowledge base population, in accordance with embodiments of the present invention. 
         FIG. 5  depicts another method for signaling concept drift during knowledge base population, in accordance with embodiments of the present invention. 
         FIG. 6  depicts a block diagram of an exemplary computer system that may be included in the system for signaling concept drift during knowledge base population of  FIG. 1 , capable of implementing process flows and methods for signaling concept drift during knowledge base population of  FIGS. 3-5 , and creating the knowledge graph vector space of  FIG. 2 , in accordance with embodiments of the present invention. 
         FIG. 7  depicts a cloud computing environment, in accordance with embodiments of the present invention. 
         FIG. 8  depicts abstraction model layers, in accordance with embodiments of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present invention are configured to warn a user when concept drift occurs in a knowledge graph or during knowledge base population. When populating a knowledge graph, concept drift can result in divergence from the intended purpose of the types of entities and relations in the knowledge graph. Specifically, the present invention is advantageously configured to signal when new entities and new relationships between entities begin to diverge from the types of entities and relations already in the knowledge graph. The present invention, for the first time, provides a user with an indication when an emerging concept is diverging significantly from a concept embodied by a knowledge graph. This improvement to the art of knowledge graphs utilizes vector space models involving relations to compute emerging concepts for new data items being added to a knowledge graph and to compute concepts for the existing entities and relations in the knowledge graph to provide insight to users related to concept drift. 
     Embodiments of the present invention relates to methods, and associated computer system and computer program product, for signaling concept drift during knowledge base population. One or more processors of a computer system receive a knowledge graph and a text collection. For example, the knowledge graph may include one or more of:
         1) entities i1, i2, i3, etc. linked via relations r1, r2, r3, etc. to form a graph of relationships p1=&lt;i1, r1, i3&gt;, p2=&lt;i2, r2, i2&gt;, etc.;   2) a set of entity type links between instance entities and their entity types &lt;i1, t1&gt;, &lt;t2, t1&gt;, etc.;   3) a set of relation type links between relationships and relations &lt;p1,r1&gt;, &lt;p2,r2&gt;,etc.   4) a list of definitions of each relation as a tuple relating entity types and relations &lt;t1, r1, t2&gt;, &lt;t2, r2, t4&gt;, etc.; and   5) surface forms of the entities that are alternate ways of expressing the entities in a natural language.
 
The entities are each a sequence of characters. The instance entities in knowledge graphs are the extensional definition of entity types to which they are linked. For example, “university” may be extensionally defined in terms of examples that are instance entities: “University of Florida”, “Harvard University”, and “Stevens Institute of Technology”. Similarly, the relation “president of” may be extensionally defined in terms of examples that are relationships such as “Macron, president of, France”, “Bacow, president of, Harvard”. The text collection includes, for example, a collection of documents. The one or more processors may find mentions of the surface forms of the instance entities in the text collection as tokens. The one or more processors may further find additional tokens as sequences of characters (e,g, words, phrases, nouns, noun phrases, etc.) in the documents in the text collection. The one or more processors may then build knowledge graph vector spaces of the text collection (e.g. using a shallow neural network) using the tokens and portions of text surrounding the mentions of the surface forms of the instance entities. One knowledge graph vector space may be a vector space for entities. Another knowledge graph vector space may be a vector space for relationships as tuples of entities. The one or more processors embed the instance entities and relationships in the knowledge graph vector spaces to compute an instance entity vector for each instance entity in the knowledge graph and/or compute a relationship vector for each relationship in the knowledge graph. The one or more processors may compute an entity type concept vector for each entity type which may be a function of the instance entity vectors for each instance entity linked to the entity type. Alternatively or additionally, the one or more processors may compute a relation concept vector for each relation which may be a function of the relationship vector for each relationship linked by a relation type link to the relation.
       

     Once the concept vector for each entity type and/or relation are computed, embodiments of the invention contemplate the one or more processors receiving a new sequence of data items to populate the knowledge graph. The one or more processors receive an association between the data items and an entity in the knowledge graph or between tuples of data items and relations in the knowledge graph. The one or more processors may then find mentions of each data item in the text collection and mentions of each relationship between data items, for example using surface forms. The one or more processors embed the data items and relationships between data items in knowledge graph vector space. The one or more processors may then compute a data item vector for each data item and compute an emerging entity type concept vector for each entity type. The emerging entity type concept vector is a function of the instance entity vectors for each instance entity linked to the entity type. Additionally or alternatively, the one or more processors may also compute an emerging relation concept vector for each relation. The relation concept vector is a function of all the relationship vectors for each relationship linked by a relation type link to the relation. For entities, the one or more processors generate a signal for concept drift of an entity type when the distance in the knowledge graph vector space between the entity type concept vector and the emerging entity type concept vector is greater than a concept drift threshold. For relations, the one or more processors generate a signal for concept drift of a relation when the distance in the knowledge graph vector space between the emerging relation concept vector and the relation concept vector is greater than a relation concept drift threshold. 
       FIG. 1  depicts a block diagram of a system for signaling concept drift during knowledge base population  100 , in accordance with embodiments of the present invention. Embodiments of the system for signaling concept drift during knowledge base population  100  may be conducted by a computer system  120 . Embodiments of the computing system  120  may be a computer system, a computer, a server, one or more servers, a cloud computing device, a hardware device, a remote server, and the like. The system for signaling concept drift during knowledge base population  100  and/or computer system  120  may be configured to receive a knowledge graph, receive a text collection, receive a sequence of data item entities (e.g. &lt;“Macron”, “Harvard”&gt;) and/or a sequence of sets of data items as relationships (e.g. &lt;“Macron, France”, “Bacow, Harvard”&gt;), parse tokens from the text collection using the knowledge graph, build a vector space of the tokens, link data items and/or relationships composed of the data items in the text to knowledge graph entity type and/or relation, and generate a signal, warning, or other indication to a user when concept drift is detected. 
     Furthermore, embodiments of system for signaling concept drift during knowledge base population  100  may include one or more data sources  110  and one or more user devices  111  communicatively coupled to the computing system  120  of the system for signaling concept drift during knowledge base population  100  over a network  107 . 
     The network  107  may be a 4G, 5G and/or LTE based cellular data network or system. The network  107  may be a cloud network or system. Further embodiments of network  107  may refer to a group of two or more computer systems linked together. Network  107  may be any type of computer network known by individuals skilled in the art. Examples of computer networks  107  may include a LAN, WAN, campus area networks (CAN), home area networks (HAN), metropolitan area networks (MAN), an enterprise network, cloud computing network (either physical or virtual) e.g. the Internet, a cellular communication network such as GSM or CDMA or a mobile communications data network. The architecture of the computer network  107  may be a peer-to-peer network in some embodiments, wherein in other embodiments, the network  107  may be organized as a client/server architecture. 
     Embodiments of the one or more data sources  110  of the system for signaling concept drift during knowledge base population  100  may be any document corpus provided by a user, entity of other system. The one or more data sources  110  may be text data in a single language or multiple languages. The one or more data sources  110  may be configured to provide the computer system  120  with one or more large or structured set of texts. For example, the one or more data sources  110  may be a web entity, such as Wikipedia. The one or more data sources  110  may be an academic journal database, a historical newspaper database, or any other database with textual data. The one or more data sources  110  may be configured to provide some or all of the data stored therein. For example, the one or more data sources  110  may be interacted with by a user to determine the exact subset of data from the one or more data sources  110  to provide to the computer system  120 . 
     Embodiments of the one or more user devices  111  may be a device operated by a user that is configured to interact with the computer system  120  and both provide information or inputs to the computer system  120  and receive outputs from the computer system  120 . The one or more user devices  111  may be operated by a user in providing data from the one or more data sources  110  to the computer system  120  for analysis therein, in accordance with the methods described herein. The one or more user devices  111  may be configured to provide the computer system  120  with a selected subset of data from the data that has been provided to the computer system  120  from the one or more data sources  110 . For example, the one or more user devices  111 , operated by a user, may be configured to provide the computer system  120  with a corpus of 300,000 technology notes from a technology journal database. The one or more user devices  111  may enable to user to make a selection of a subset of entities for the computer system  120  to populate a knowledge graph. For example, the one or more user devices  111  may select “database” as a type of entity and the computer system  120  may populate a knowledge graph with a plurality of databases (e.g. Oracle®, DB2 @, etc.) found in the 300,000 technology notes. The one or more user devices  111  may thus be configured to receive a knowledge graph output from the computer system  120  for consumption by a user of the one or more user devices  111 . The one or more user devices  111  may further be configured to receive notifications, signals, or other outputs from the computer system  120  for signaling when concept drift may occur in a knowledge graph created by the computer system  120 . 
     The knowledge graph may include entities and/or relations connecting entities. The entities may be of a given type. For example, “database” and “operating system” might be the entity types, “runsOn” might be the relation, “DB2” and “SQL Server” might entities of type “database”. “DB2 runsOn Unix” and “SQL Server runsOn Windows” are the relationships. 
     Embodiments of the computing system  120  include a module structure  130  that includes a receiving module  131 , a vector space building module  132 , an embedding module  134 , and a signal generating module  135 . A “module” herein refers to any hardware-based module, software-based module, or combination thereof. Embodiments of hardware-based modules may include self-contained components such as chipsets, specialized circuitry and one or more memory devices, while a software-based module may be part of a program code or linked to the program code containing specific programmed instructions, which may be loaded in the memory device of the computer system  120 . A module (whether hardware, software, or a combination thereof) may be designed to implement or execute one or more particular functions or routines. 
     Embodiments of the receiving module  131  may include one or more components of hardware and/or software program code for receiving information and/or data from the one or more data sources  110  and the one or more user devices  111 . The receiving module  131  may be configured to receive a sequence of data items. The receiving module  131  may also be configured to receive a sequence of pairs, triples, or other tuples of data items. The receiving module  131  may be configured to accept a knowledge graph. The receiving module  131  may be configured to accept an association between the sequence of data items and a type of entity in the knowledge graph, including a link between the sequence of data items and the entity. The receiving module  131  may be configured to accept an association between the sequence of tuples and a relation in the knowledge graph, including a link between the sequence of tuples and the relation. The receiving module  131  may be configured to accept a corpus of text-based information from the one or more data sources  110 . The new sequence of data items or sequence of tuples of data items may be new information, or a modified entity or relationship example from a user or automated system (i.e. streaming news) that is added to a list of existing entities and/or relationships. The receiving module  131  may be configured to add this new sequence of data items or sequence of tuples of data items to the list of already received sequence of data items. Still further, the receiving module  131  may be configured to receive entity and/or relationship inputs from a user related to the received data. The receiving module  131  may be still further configured to receive various other setting or customization inputs from the one or more user devices  111  and/or the users. 
     Embodiments of the vector space building module  132  may include one or more components of hardware and/or software program code for building a vector space for the received corpus of text-based information. The vector space building module  132  may be configured to build a vector space related to the received textual data. The vector space building module  132  may be configured to split a document collection into context windows (e.g. sentences or paragraphs), and parse or extract entities or relations (including more than one entity) as natural language chunks (e.g. noun phrases, named entities, etc.) from each context window. The vector space building module  132  may be configured to index each of the context windows by entities and relations. The vector space building module  132  may be configured to build the vector space for the received sequence of data items, and any new sequence of data items related to an emerging concept that are later received and added to an existing vector space. 
     Embodiments of the embedding module  134  may include one or more components of hardware and/or software program code for embedding entities and relations in the vector space. The embedding module  134  may be a sub-module of the vector space building module  132 . The embedding module  134  may be configured to map an entity or relation to a vector of continuous numbers. This may allow the vector space building module  132  to create the vector space. 
     Embodiments of the signal generating module  135  may include one or more components of hardware and/or software program code for generating a signal when the cosine distance or dot product distance between a centroid vector of entities and relationships defining the concept from the knowledge graph that are in the vector space and a centroid vector of the received new sequence of data items or sequence of tuples of data items is greater than a concept drift threshold. Specifically, the signal generating module  135  may be configured to compute both a knowledge graph concept vector and an emerging concept vector. The knowledge graph concept vector may be computed by the embedding module  132  as a function of the vectors for entities and/or relations in the knowledge graph. In some embodiments, the knowledge graph concept is a connected graph of entities and relations. In such embodiments, the embedding module  132  uses measure of the magnitude of the centrality of the entities and/or relations in the connected graph combined with the magnitude of the vectors for entities and/or relations. Similarly, the emerging concept vector may be a function of the vectors of the entities and/or relation examples and the graph centrality). 
     The signal generating module  135  may be configured to highlight data items according to a distance from the vector of knowledge graph concept. The signal generating module  135  may be configured to present a data item outlier to a user and/or a user device, such as the one or more user devices  111 . The signal generating module  135  may be configured to alert a user or system, such as the one or more user devices  111 , of the concept drift if the concept of the type of entities and/or relations selected from the corpus of documents newly received starts to diverge sufficiently from the concept of the type of entities and/or relations in the existing vector space. 
     The signal generating module  135  may utilize dot product or cosine similarity to determine each of the knowledge graph concept vector and the emerging concept vector. The signal generating module  135  may be configured to color each example corresponding to the cosine distance or dot product distance or dot product distance. The signal generating module  135  may be configured to enable the user to add, remove, or edit the documents in the document corpus of the received sequence of data items, thus rebuilding the knowledge graph vector space. The signal generating module  135  may further be configured to crowd source the sequence of data items by having a set of users provide data items. Still further, the threshold for determining when a signal is generated (the concept drift threshold) may be customized by a user, or learned by supervised machine learning methods (e.g. through positive and/or negative feedback by a user). 
     Further, embodiments of the computing system  120  may be equipped with a memory device  142  which may store various data/information/code, and a processor  141  for implementing the tasks associated with the system for signaling concept drift during knowledge base population  100  and perform processing associated with the functionality of the module structure  130 . 
       FIG. 2  depicts a knowledge graph vector space  150  created by the system for signaling concept drift during knowledge base population of  FIG. 1 , in accordance with embodiments of the present invention. The knowledge graph vector space  150  is shown including a concept vector  160 . The knowledge graph vector space pertains to entities, but it should be understood that the same concepts may be applied to relations. Each entity has an entity vector. In knowledge graphs plotting relation vectors, each relationship may similarly have a relationship vector. Each entity type has an entity type vector computed from the entity vectors for each of the entities for which it is a type. In knowledge graphs pertaining to relations, each relation would have a relation vector computed from the relationship vectors of each of the relationships for which it is a relation. The concept vector  160  is shown as the centroid of the vectors of the entities in the knowledge graph 1 , f 2 , f 3 , f 4  within an existing vector space built and trained from a corpus of documents; the emerging concept vector  170  is shown as a centroid of the vectors for received entities e 1 , e 2 , e 3  that are received as data items associated with entity types. 
       FIG. 3  depicts a process flow  200  performed by the system for signaling concept drift during knowledge base population  100  of  FIG. 1 , in accordance with embodiments of the present invention. As shown, a user first provides a document corpus at a step  210  of the process to the computer system  120 , which is received by the receiving module  131 . The computer system  120 , and specifically the vector space building module  132 , extracts entities, relations and context from the received text at a step  220  of the process. The computer system uses the knowledge graph of entities and relations located in a data storage location  240 . Specifically, the system locates the surface form variant spellings of entities from the knowledge graph in the document corpus. From there, at a step  230 , the computer system  120 , and the embedding module  134 , generates and/or trains embeddings from the text in the document corpus. Entities of knowledge graphs of entity and/or relation types may be utilized by the modules  132 ,  134  at a step  240 . From here, the knowledge graph concept vectors (entity type concept vectors and relation concept vectors)  160  may be generated or otherwise computed, along with the emerging concept vectors (emerging entity type concept vectors and emerging relation type concept vectors)  170  and the cosine of the angles between these two vectors may be computed at step  250 . Finally, at a step  260 , a signal is generated and sent to a user device such as the user device  111  if the cosine distance or dot product distance or dot product distance between the knowledge graph concept vector  160  and the emerging concept vector  170  is greater than a threshold. This threshold can be learned from user feedback as it is determined through knowledge base population how much cohesion is desired by the user. The threshold can be tuned or set for each entity type and relation or can be global across the knowledge graph with one setting for all relations and one setting for all entity types. The default setting is 0.20. It may be set by the user. It may be associated with the tool that the user uses to input data items. Some entities may be more central in the knowledge graph than others as measured by a graph centrality measure. The signal for concept drift may be attenuated by the centrality of the entities in the knowledge graph and/or the centrality of the graph of data items. 
       FIG. 4  depicts a method  300  for signaling concept drift during knowledge base population, in accordance with embodiments of the present invention. The method  300  may be performable by the system for signaling concept drift during knowledge base population  100 . The method  300  may include a step  310  of receiving, accepting, or otherwise obtaining, by one or more processors of a computer system such as computer system  120 , a knowledge graph. The knowledge graph may define a concept that is, for example, an entity. The method  300  includes a step  320  of receiving a collection of text. The collection of text may be a corpus of documents from one or more data sources, such as the data sources  110 . The method  300  may include a step  330  of building, by the one or more processors of the computer system, an entity vector space of the received collection of text. 
     The method  300  may include another step  340  of receiving, by the one or more processors of the computer system, a sequence of data items associated with a type of entity in the knowledge graph. The sequence of data items may be a collection of text, corpus of additional documents from one or more data sources, a window or a data entry generated by program, or the like. The method  300  includes another step  350  of embedding, by the one or more processors of the computer system, entities from the knowledge graph into the entity vector space to generate entity vectors. The method  300  includes a step  360  of embedding, by the one or more processors of the computer system, data items associated with the type of entities into the entity vector space to generate data item vectors. 
     The steps  350 ,  360  may use embeddings and autoencoders. The steps  350 ,  360  may further include building a graph of data item tuples where the difference is computed as a dot product. Still further, the steps  350 ,  360  may include building a graph of data item tuples where the magnitude of the vector is computed using a graph metric. The steps  350 ,  360  may include splitting a document collection into context windows (e.g. sentences or paragraphs), and extracting entities as natural language text chunks (nouns, phrases, named entities, etc.) from each context window. The steps  350 ,  360  may include indexing each of the context windows by the entities and learning an embedding space (reduced dimensional representation) for each entity. The steps  350 ,  360  may further include computing entities for each of the entity examples in the knowledge graph using the embedding vector space. 
     Finally, the method  300  includes a step  390  of generating, by the one or more processors of the computer system, a signal when the cosine distance or dot product distance between the emerging entity concept vector and the entity concept vector is greater than a concept drift threshold. The step  390  may include highlighting data items according to a degree of difference between the emerging entity concept vector and the entity concept vector. The step  390  may include presenting a data item outlier to a user and/or a user device, such as the one or more user devices  111 . The method step  390  may include signaling concept drift if the emerging concept starts to diverge sufficiently from the knowledge graph concept. This step  390  may include alerting a user or system, such as the one or more user devices  111 , of the concept drift if the concept of the type of entities selected from the corpus of documents newly received starts to diverge sufficiently from the concept of the type of entities in the existing knowledge graph vector space. The method step  390  may utilize degree centrality, betweenness centrality, Kartz centrality, or other graph centrality measures in addition to the magnitude of each of the emerging concept entity vector and the entity concept vector. The step  390  may include highlighting colors of each of example corresponding to the cosine distance or dot product distance or graph centrality. The method step  390  may include enabling the user to change the document corpus of the received sequence of data items by editing documents or adding and removing documents from the list being utilized to generate a knowledge graph vector space. The step  390  may further include crowd sourcing the creation of the original knowledge graph vector space. Still further, the threshold for determining when a signal is generated (the concept drift threshold) may be customized by a user, or learned by supervised machine learning methods (e.g. through positive and/or negative feedback by a user). 
       FIG. 5  depicts a method  400  for signaling concept drift during knowledge base population, in accordance with embodiments of the present invention. The method  400  may be performable by the system for signaling concept drift during knowledge base population  100 . The method  400  may include a step  410  of receiving, accepting, or otherwise obtaining, by one or more processors of a computer system such as computer system  120 , a knowledge graph. The knowledge graph may define a concept that is, for example, a relation. The method  400  includes a step  420  of receiving a collection of text. The collection of text may be a corpus of documents from one or more data sources, such as the data sources  110 . The method  400  may include a step  430  of building, by the one or more processors of the computer system, a relation vector space of the received collection of text. 
     The method  400  may include another step  440  of receiving, by the one or more processors of the computer system, a sequence of data items associated with a type of relation in the knowledge graph. The sequence of data items may be a collection of text, corpus of additional documents from one or more data sources, a window or a data entry generated by program, or the like. The method  400  includes another step  450  of embedding, by the one or more processors of the computer system, relations from the knowledge graph into the relation vector space to generate relation vectors. The method  400  includes a step  460  of embedding, by the one or more processors of the computer system, data items associated with the type of relations into the relation vector space to generate data item vectors. 
     The steps  450 ,  460  may use embeddings and autoencoders. The steps  450 ,  460  may further include building a graph of data item tuples where the difference is computed as a dot-product. Still further, the steps  450 ,  460  may include building a graph of data item tuples where the magnitude of the vector is computed using a graph metric. The steps  450 ,  460  may include splitting a document collection into context windows (e.g. sentences or paragraphs), and extracting entities (or pairs of entities) as natural language text chunks (nouns, phrases, named entities, etc.) from each context window. The steps  450 ,  460  may include indexing each of the context windows by the entities and learning an embedding space (reduced dimensional representation) for each relation. The steps  450 ,  460  may further include computing relation example vectors for each of the relation examples in the knowledge graph using the embedding vector space. 
     The method  400  may thereby include a step  470  of computing, by the one or more processors of the computer system, an emerging relation concept vector by computing the centroid of the data item vectors. The method  400  may thereafter include a step  470  of computing, by the one or more processors of the computer system, a relation concept vector by computing the centroid of the relation vectors. 
     Finally, the method  400  includes a step  490  of generating, by the one or more processors of the computer system, a signal when the cosine distance or dot product distance between the emerging relation concept vector and the relation concept vector is greater than a concept drift threshold. The step  490  may include highlighting data items according to a degree of difference between the emerging relation concept vector and the relation concept vector. The step  490  may include presenting a data item outlier to a user and/or a user device, such as the one or more user devices  111 . The method step  490  may include signaling concept drift if the emerging concept starts to diverge sufficiently from the knowledge graph concept. This step  490  may include alerting a user or system, such as the one or more user devices  111 , of the concept drift if the concept of the type of relations selected from the corpus of documents newly received starts to diverge sufficiently from the concept of the type of relations in the existing knowledge graph vector space. The method step  490  may utilize cosine similarity to determine the central magnitude of each of the emerging concept relation vector and the relation concept vector. The step  490  may include highlighting colors of each of example corresponding to the cosine distance or dot product distance. The method step  490  may include enabling the user to change the document corpus of the received sequence of data items by editing documents or adding and removing documents from the list being utilized to generate a knowledge graph vector space. The step  490  may further include crowd sourcing the creation of the original knowledge graph vector space. Still further, the threshold for determining when a signal is generated (the concept drift threshold) may be customized by a user, or learned by supervised machine learning methods (e.g. through positive and/or negative feedback by a user). 
       FIG. 6  depicts a block diagram of an exemplary computer system that may be included in the system for signaling concept drift during knowledge base population  100  of  FIG. 1 , capable of implementing methods and process flows for signaling concept drift during knowledge base population of  FIGS. 3-5 , in accordance with embodiments of the present invention. The computer system  500  may generally comprise a processor  591 , an input device  592  coupled to the processor  591 , an output device  593  coupled to the processor  591 , and memory devices  594  and  595  each coupled to the processor  591 . The input device  592 , output device  593  and memory devices  594 ,  595  may each be coupled to the processor  591  via a bus. Processor  591  may perform computations and control the functions of computer  500 , including executing instructions included in the computer code  597  for the tools and programs capable of implementing method and processes for signaling concept drift during knowledge base population in the manner prescribed by the embodiment of  FIGS. 3-5  using one, some or all of the system for signaling concept drift during knowledge base population  100  of  FIG. 1 , wherein the instructions of the computer code  597  may be executed by processor  591  via memory device  595 . The computer code  597  may include software or program instructions that may implement one or more algorithms for implementing the methods and processes for signaling concept drift during knowledge base population, as described in detail above. The processor  591  executes the computer code  597 . Processor  591  may include a single processing unit, or may be distributed across one or more processing units in one or more locations (e.g., on a client and server). 
     The memory device  594  may include input data  596 . The input data  596  includes any inputs required by the computer code  597 . The output device  593  displays output from the computer code  597 . Either or both memory devices  594  and  595  may be used as a computer usable storage medium (or program storage device) having a computer-readable program embodied therein and/or having other data stored therein, wherein the computer-readable program comprises the computer code  597 . Generally, a computer program product (or, alternatively, an article of manufacture) of the computer system  500  may comprise said computer usable storage medium (or said program storage device). 
     Memory devices  594 ,  595  include any known computer-readable storage medium, including those described in detail below. In one embodiment, cache memory elements of memory devices  594 ,  595  may provide temporary storage of at least some program code (e.g., computer code  597 ) in order to reduce the number of times code must be retrieved from bulk storage while instructions of the computer code  597  are executed. Moreover, similar to processor  591 , memory devices  594 ,  595  may reside at a single physical location, including one or more types of data storage, or be distributed across a plurality of physical systems in various forms. Further, memory devices  594 ,  595  can include data distributed across, for example, a local area network (LAN) or a wide area network (WAN). Further, memory devices  594 ,  595  may include an operating system (not shown) and may include other systems not shown in  FIG. 6 . 
     In some embodiments, the computer system  500  may further be coupled to an Input/output (I/O) interface and a computer data storage unit. An I/O interface may include any system for exchanging information to or from an input device  592  or output device  593 . The input device  592  may be, inter alia, a keyboard, a mouse, etc. or in some embodiments the touchscreen of a computing device. The output device  593  may be, inter alia, a printer, a plotter, a display device (such as a computer screen), a magnetic tape, a removable hard disk, a floppy disk, etc. The memory devices  594  and  595  may be, inter alia, a hard disk, a floppy disk, a magnetic tape, an optical storage such as a compact disc (CD) or a digital video disc (DVD), a dynamic random access memory (DRAM), a read-only memory (ROM), etc. The bus may provide a communication link between each of the components in computer  500 , and may include any type of transmission link, including electrical, optical, wireless, etc. 
     An I/O interface may allow computer system  500  to store information (e.g., data or program instructions such as program code  597 ) on and retrieve the information from one or more computer data storage units (not shown). The one or more computer data storage units include a known computer-readable storage medium, which is described below. In one embodiment, the one or more computer data storage units may be a non-volatile data storage device, such as a magnetic disk drive (i.e., hard disk drive) or an optical disc drive (e.g., a CD-ROM drive which receives a CD-ROM disk). In other embodiments, the one or more computer data storage unit may include a knowledge base or data repository  125 , such as shown in  FIG. 1 . 
     As will be appreciated by one skilled in the art, in a first embodiment, the present invention may be a method; in a second embodiment, the present invention may be a system; and in a third embodiment, the present invention may be a computer program product. Any of the components of the embodiments of the present invention can be deployed, managed, serviced, etc. by a service provider that offers to deploy or integrate computing infrastructure with respect to identification validation systems and methods. Thus, an embodiment of the present invention discloses a process for supporting computer infrastructure, where the process includes providing at least one support service for at least one of integrating, hosting, maintaining and deploying computer-readable code (e.g., program code  597 ) in a computer system (e.g., computer  500 ) including one or more processor(s)  591 , wherein the processor(s) carry out instructions contained in the computer code  597  causing the computer system to perform the method for signaling concept drift during knowledge base population. Another embodiment discloses a process for supporting computer infrastructure, where the process includes integrating computer-readable program code into a computer system including a processor. 
     The step of integrating includes storing the program code in a computer-readable storage device of the computer system through use of the processor. The program code, upon being executed by the processor, implements a method for signaling concept drift during knowledge base population. Thus, the present invention discloses a process for supporting, deploying and/or integrating computer infrastructure, integrating, hosting, maintaining, and deploying computer-readable code into the computer system  500 , wherein the code in combination with the computer system  700  is capable of performing a method for signaling concept drift during knowledge base population. 
     A computer program product of the present invention comprises one or more computer-readable hardware storage devices having computer-readable program code stored therein, said program code containing instructions executable by one or more processors of a computer system to implement the methods of the present invention. 
     A computer system of the present invention comprises one or more processors, one or more memories, and one or more computer-readable hardware storage devices, said one or more hardware storage devices containing program code executable by the one or more processors via the one or more memories to implement the methods of the present invention. 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer-readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer-readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer-readable program instructions described herein can be downloaded to respective computing/processing devices from a computer-readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the respective computing/processing device. 
     Computer-readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. 
     These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer-implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics areas follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly release to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models areas follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring now to  FIG. 7 , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers or users, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A,  54 B,  54 C and  54 N shown in  FIG. 7  are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG. 8 , a set of functional abstraction layers provided by cloud computing environment  50  (see  FIG. 7 ) are shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 8  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provides pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 , data analytics processing  94 ; transaction processing  95 ; and signaling concept drift during knowledge base population  96 . 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.