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SMOL: a systemic methodology for ontology learning from heterogeneous sources

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Abstract

Organizations are demanding an efficacious knowledge management. Consequently, they are increasing their system innovation investments to turn information into useful knowledge for decision making obtained from heterogeneous Knowledge Sources (KSOs) such as databases, documents, and even ontologies. Methodological Resources (MRs) for the required knowledge discovering and recovering purposes have gradually become more elaborated and mature in the framework of Knowledge Engineering. Particularly, in the Ontology Learning (OL) field, there is a lack of integrated and open methodologies that could involve all the optional KSOs. In this sense, a systemic perspective is introduced combining MRs associated to diverse KSOs to improve the quality of an integral and continuous Knowledge Acquisition (KA) process. The main contributions provided by this work are on one hand, a novel Systemic Methodology for OL (SMOL) from heterogeneous KSOs which is applied for a case study and on the other hand, an evaluation of SMOL.

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Authors and Affiliations

  1. University of Granada, Granada, Spain

    Richard Gil & Maria J. Martin-Bautista

Authors
  1. Richard Gil
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  2. Maria J. Martin-Bautista
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Correspondence to Maria J. Martin-Bautista.

Appendix A: a specific description of the SMOL phases

Appendix A: a specific description of the SMOL phases

1.1 Phase I: Methodology strategy selection

  1. Objective:

    In this phase, the best methodological strategy to follow is selected according to the available or recoverable data from the KSO related to the specific domain.

  2. Input:

    Information about the KSO and user’s domain: First, the information which is more strongly related to the explicit and implicit knowledge from previously stored or new KSOs; secondly, information coming from the opinion of Expert Users to characterize the domain and KSO.

  3. Output:

    Methodological strategy drafted and selected (MR for each KSO) among the possible options (inductive/deductive) for OL: Top–down, Bottom–up, Middle-out, or a combination of them.

  4. Steps

    Methods and techniques:

    1. 1.

      Identifying domain-complexity, assessing partial characteristics of that domain according to the cited Zhou’s proposal. A Rule-based decision approach is applied about the domain-complexity attributes (Established, Conventional, Technological independence, and Interdisciplinary features). Users could select the Methodology strategy applying an optional heuristic suggested in two ways: a) based on the user’s expertise assessing the domain-attributes in Fig. 3, or b) using any decision-rules about the availability of KSOs related to previously developed ontologies or RDBs shown in Algorithm A.1, under the premise that OL from texts are mostly available.

    2. 2.

      Identifying and selecting the KSO available in the own DB (based on phase II cycles). The potential knowledge already available (both explicit and implicit) as well as new knowledge coming from accredited Internet sites is obtained.

    3. 3.

      Obtaining previous system recommendations for users about optional methodological strategies, showing the possible options. The key point is that, just as more domain information and knowledge is available and able to be integrated, the uncertainty is reduced consequently; in this case, the recommendation is to use top–down strategies.

    4. 4.

      Recording (in the user profile) the strategy selected by users for each potential knowledge source, which would be later reused as resource candidates for learning purposes.

  5. Tools:

    Protégé, Swoogle (and others), Google/AltaVista, Wikipedia, (Euro)WordNet, SUMO, Cyc, ERwin case, RDBToOnto, etc.

  6. Decision-point:

    < a > Selected strategy.

1.2 Phase II: Knowledge discovery

  1. Objective:

    Identifying and pre-selecting potential knowledge from previously structured and recorded KSOs in the FDB catalogues of the associated KBS. Likewise, other potential knowledge can be recovered from other accredited sources through the Internet (e.g., documents, ontology-catalogues, public databases, websites, and so on).

  2. Input:

    a) Knowledge regarding the KSO (explicit and implicit) previously recorded in DBs of the associated KBS (Corpora, DBs, ontologies, procedures, etc). b) Public knowledge that may be recoverable from KSOs (ontologies, corpus-texts, RDB-Schemas, websites) associated with the domain. Some of these sources could be identified and preselected in Phase I.

  3. Output:

    Potential knowledge preselected as a candidate KSO (from one of the three types of KSOs cited above) regarding: taxonomic and non-taxonomic relationships, semantic correspondences (thesaurus), RDB-schemas and their cell-data values, some procedures as agents of knowledge processing and updating, and any other useful MR considered as well.

  4. Steps

    Methods and techniques:

    1. 1.

      For each type of structured KSO (ontology, corpus, DB, agent, or any MR), some potential explicit and implicit knowledge (content, MR, or agent) must be identified and recovered.

    2. 2.

      For each KSO which is not processed by users yet (such as texts, ontologies or DBs-schemas, found on the Internet), new knowledge can be incorporated.

    3. 3.

      Some recovered knowledge resources (either structured or not) may be reorganized in any structured format (such as taxonomies and RDB-schemas). For instance, selecting parts of the domain ontologies using tools such as Text2Onto or Protégé.

    4. 4.

      Asking users to validate the knowledge recovered according to their correspondence with the case or domain study. The suggestions of these users are stored in the user’s profile.

    5. 5.

      The potential knowledge considered by users as suitable for the selected strategy in Phase I is registered in the KSO catalogues.

  5. Tools:

    OL from ontologies: ASMOV, GeRMeSMB, MapPSO, RiMOM, Prompt, TaxoMap, etc. OL from texts: Asium, SVELAN, Text2Onto, etc. And, OL from databases: S-Match, Cupid, COMA, and RDBToOnto.

1.3 Phase III: Query requirements

  1. Objective:

    Allowing users to make different queries (through a standard format as a browser) such as the structured knowledge already acquired on the host-ontology as well the one available in the KSO catalogued into the associated KBS. Likewise, users would be able to turn these queries into general search options on the Internet that could lead to obtaining additional knowledge.

  2. Input:

    Some users’ queries and requirements that could be interesting, expressed in natural or pseudo-Natural language (uses cases) regarding some information, knowledge, or taxonomic structure.

  3. Output:

    Some tentative text corpora or ontologies about sub-domains, database candidates, users’ agreements, and consensus among several MRs.

    Steps Methods and techniques:

    1. 1.

      Querying about knowledge structures, terms, and meanings to match the actual ontology with previous versions.

    2. 2.

      Consulting on the potential knowledge, according to each KSO (ontology, text, RDB-schema) to add new knowledge to structures.

    3. 3.

      Validating the existence of reusable, similar, or equivalent queries previously carried out by users (in users’ profiles) for the same domain or for a similar one.

    4. 4.

      Registering in the associated KSO repositories, the potential knowledge considered useful by users according to the selected strategy in Phase I.

    5. 5.

      Registering user’s profiles and ontology versioning for new queries and changing requirements demanded by users.

  4. Tools:

    Google, Journal subscriptions, LabelTranslator, Protégé, QuicRDF, GATE, ERWin, PowerDesigner and others.

1.4 Phase IV: Knowledge selection

  1. Objective:

    Selecting a ranking of potential knowledge based on the previous queries and the knowledge discovered in Phase III.

  2. Input:

    Tentative Corpus, optional sub-domain ontologies, some RDB-scheme candidates and some agreements and consensus among tentative users.

  3. Output:

    Selected Corpus, ontologies, RDB-schemes and any other MR.

  4. Steps

    Methods and techniques:

    1. 1.

      Selecting structured potential knowledge (ontologies, corpus, RDB-schemes) from the registries in the KSO of the KBS previously registered as useful in the users’ profile.

    2. 2.

      Selecting non-structured potential knowledge (content of texts, other DBs, other ontologies) from the previously preselected registries.

    3. 3.

      The Expert Users check the consistency of the format and data meaning of the already identified and selected data from the KSOs. This data could be used for OL to update the host-ontology objects.

    4. 4.

      Selecting the preselected MR definitively from the KSO to be used in the workflow of SMOL. Occasionally, the selected tools can include some user’s options for (semi-)automatic format conversion and consistency checking of the potential selected data for OL.

    5. 5.

      Showing to the Expert Users a rank of “potential knowledge level” to improve the OL process once that this potential level is calculated by the system.

    6. 6.

      Asking users about their opinions and decisions of the KSO (content and MR). According to the specific case, some of these opinions and decisions may be considered by users as an appropriate recommendation to face similar cases by other users, using user’s profiles. This recommendation system is not included in this work, but will be developed in the future

  5. Tools:

    GATE, Text2Onto, Asium, OntoLern, Terminae, RapidMiner, ERWin.

1.5 Phase V: Knowledge structure construction

  1. Objective:

    Selecting potential novel ontology-objects such as concepts, relations, and instances regarding structured knowledge correspondence, from potential knowledge selected as a candidate one.

  2. Input:

    Selected corpora and ontologies, RDB-schemes. Terminology verified as significant by users for the domain and context (re-validated).

  3. Output:

    Structured knowledge updated, validated corpus, ontologies and RDB-schemes, and tested MR.

  4. Steps

    Methods and techniques:

    1. 1.

      Applying the selected MR for each possible and potential KSO.

    2. 2.

      Validating by Expert Users the data/information format obtained through the conversion-tools associated to each KSO to guarantee the compatibility needed for the OL processes (e.g., mapping or populating). Also, the Knowledge engineers have to support any other possible data adjusting associated with formats.

    3. 3.

      Consistency verification of the semantic result or proposing knowledge structures (according to previous steps) using any reasoning tool for testing ontology consistencies.

    4. 4.

      Validating the consistency of the structure of the resulting knowledge (as ontology), querying the users and registering their opinions in user’s profiles

    5. 5.

      Applying ontology quality evaluation tools (e.g., Racer-pro).

    6. 6.

      The ontologies’ changes are registered for rollback and versioning purposes. Likewise, the user’s profile updated with the user’s actions.

  5. Tools:

    OL from ontologies: ASMOV, GeRMeSMB, MapPSO, RiMOM, Prompt, TaxoMap, etc. OL from texts: Asium, SVELAN, Text2Onto, etc. OL from databases: Cupid, COMMA, RDBToOnto, ODEMapster.

1.6 Phase VI: Knowledge exploration and search

  1. Objective:

    Exploring the structured knowledge (both acquired and potential) to be reviewed by users for verification, whether the requirements are satisfied and if they are still valid. Besides, testing of consistent results from the changes added to the structured knowledge.

  2. Input:

    Structured knowledge updated, corpus tested, ontologies validated, RDB-schemes, selected MR and user’s queries recorded.

  3. Output:

    Structured knowledge updated and evaluated (verified and validated), user’s consensus, agents for discovering and updating purpose, etc.

  4. Steps

    (Methods and techniques):

    1. 1.

      Browsing the novel structured knowledge (updated), exploring its semantic and new meaning (using Word-Net tool, for instance).

    2. 2.

      Comparison and evaluation of the novel structured knowledge. Comparing it, for instance, with the meaning of other upper level or domain ontologies, supported by the opinion of users (by validation), or using other automated forms (by verification) such as pattern validating, agents for consistency checking, etc.

  5. Tools:

    Protégé-Prompt, OntoStudio, Text2Onto, GATE, RacerPro, WordNet.

  6. Decision-Point:

    < b > Satisfied requirements. < d > Query and requirement reformulation.

1.7 Phase VII: Knowledge structure reorganization

  1. Objective:

    Reorganizing the structured knowledge, checking that both the acquired and the potential one are correct.

  2. Input:

    Structured knowledge updated and evaluated (verified and validated). Criteria of users registered in the user’s profiles.

  3. Output:

    Structured knowledge updated and evaluated. Some quality issues are tested. Some novel agents/procedures are registered as KSOs for future reorganization purposes.

  4. Steps

    Methods and techniques:

    1. 1.

      Reorganizing knowledge structures (e.g., the host ontologies, their connections/links with texts, with other KSOs, etc).

    2. 2.

      Comparing different ontology versions (visually or automatic tool).

    3. 3.

      Applying MRs for quality OE (e.g., based on tools such as Racer-pro and, OntoQA). Some details of the OE workflow for the case has been included in Appendix B.

    4. 4.

      Registering the user profiles and the ontology changes of the previous version.

  5. Tools:

    OL from ontologies: ASMOV, GeRMeSMB, MapPSO, RiMOM, Prompt, TaxoMap, etc. OL from texts: Asium, SVELAN, Text2Onto, GATE, OL from databases: COMMA, ODEMapster, RDBToOnto.

  6. Decision-Point:

    < c > Knowledge structure updating.

1.8 Phase VIII: Knowledge-based system configuration

  1. Objective:

    Establishing functionality parameters for the different modules related to the KBS and their components (regarding KSOs, user profiles, logs, etc). Essentially, the configuration of these parameters helps users to manage the KBS and the KSO appropriately.

  2. Input:

    The structured knowledge expected by user. Some additional system functionalities required by users.

  3. Output:

    System improvement and adaptation (user interface setup, procedures as agents’ inclusions, and so on.)

  4. Steps:

    Configuration of some Knowledge-base system modules such as:

    1. 1.

      Graphic User Interface options.

    2. 2.

      Updating/Recovering from any KSO options (ontologies, text, RDB-schemes).

    3. 3.

      Profiles Coordination/administration of (users, security, management).

  5. Tools:

    Text2Onto, GATE, Agents as possible RM tool.

  6. Decision-Point:

    < e > Configuration parameter setup.

Fig. A.3
figure a

Methodology strategy selection according to domain complexity assessment

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Gil, R., Martin-Bautista, M.J. SMOL: a systemic methodology for ontology learning from heterogeneous sources. J Intell Inf Syst 42, 415–455 (2014). https://doi.org/10.1007/s10844-013-0296-x

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