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Towards Knowledge Handling in Ontology-Based Information Extraction Systems
Procedia Computer 00 (2018) 000–000 Available online atScience www.sciencedirect.com
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Available online at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
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ScienceDirect 22nd International Conference on Knowledge-Based and Intelligent Information & Procedia Computer Science 126 (2018) 2208–2218 Engineering Systems
Towards Knowledge Handling in Ontology-Based Information 22nd International Conference on Knowledge-Based and Intelligent Information & Extraction Engineering Systems Systems AgnieszkainKonys* Towards Knowledge Handling Ontology-Based Information West-Pomeranian University of Technology in Szczecin, Faculty of Computer Science and Information Technology, Żołnierska 49, 71-210 Extraction Systems Szczecin, Poland Abstract
Agnieszka Konys*
West-Pomeranian University of Technology in Szczecin, Faculty of Computer Science and Information Technology, Żołnierska 49, 71-210
Poland Ontologies proved to be efficient and powerful tools forSzczecin, gathering and sharing knowledge, providing explicit specifications of conceptualizations. However, the proper ontology construction processes and their updating of various data sources, require a huge effort and well-adjusted mechanisms to their extraction, positioning and sharing. In this context, the application of Abstract a proper Ontology-Based Information Extraction (OBIE) system may help in solving these problems. The paper is a successful attempt to purvey state-of-the-art of selected OBIE systems, followed by the process of taxonomy construction and aftermath Ontologies proved to be efficient and powerful tools for gathering and sharing knowledge, providing explicit specifications of knowledge systematization of particular OBIE approaches. conceptualizations. However, the proper ontology construction processes and their updating of various data sources, require a huge effort and well-adjusted mechanisms to their extraction, positioning and sharing. In this context, the application of a proper Ontology-Based Information Extraction © 2018 The Authors. Published by Elsevier Ltd. (OBIE) system may help in solving these problems. The paper is a successful attempt to purvey state-of-the-art of selected OBIE systems, followed by the process of taxonomy construction and aftermath This is an open access articleofunder the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) knowledge systematization particular OBIE approaches.
Selection andAuthors. peer-review underby responsibility © 2018 The Published Elsevier Ltd.of KES International. This is an open accessPublished article under the CC Ltd. BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. by Elsevier Selection and peer-review under responsibility of KES International. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Keywords: Ontology-Based Information Extraction, Information Extraction, ontology, knowledge management
Selection and peer-review under responsibility of KES International.
Keywords: Ontology-Based Information Extraction, Information Extraction, ontology, knowledge management
* Corresponding author. Tel.: +48-91-449-56-62; fax: +48-91-449-56-62. E-mail address: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) * Corresponding author. under Tel.: +48-91-449-56-62; fax:International. +48-91-449-56-62. Selection and peer-review responsibility of KES E-mail address: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International. 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International. 10.1016/j.procs.2018.07.228
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1. Introduction A huge number of the heterogeneity of the documents and their different types both structured, semi-structured and unstructured poses new challenges and requires adapting the new technologies. The process of retrieving information, especially from unstructured and semi-structured sources, is very demanding task [1]. The greater role is assigned to efficient data collection, analysis and processing. Nowadays, the constantly growing data becomes rather useless if it is impossible to extract meaningful, relevant and applicable information out of it [2]. Further, the problem concerns the extraction of the structured information from raw data. In many domains the unstructured data are the most frequent types of gathering documents, where in many cases it gains more than 80%. The process of these resources collection and processing may provide interesting knowledge from large collections of unstructured documents [3] and it has direct influence on the proper information management [4]. In last decade the terms related to Semantic Web (e.g. Information Extraction (IE), Knowledge Extraction, ontologies etc.) become significant elements in the efficient way of information retrieval, processing and supporting availability of machine readable data. This success, widespread usage and commercialization emphasizes their role of WWW community [5]. What is more, a close relation between Ontology-Based Information Extraction (OBIE) and the Semantic Web is noticeable [1]. OBIE is the process of identifying in text or other sources relevant concepts, properties, and relations expressed in an ontology. OBIE systems generate semantic content which is known as Semantic Annotation for the Web pages [3]. The advantage of OBIE over traditional IE is that the output (semantic metadata about the text) is linked to an ontology. Thus, semantic agents can directly process semantic content for Information Retrieval. Consequently, OBIE systems extract much more meaningful information about the text [6], especially making use of relational information or performing reasoning. The growing pervasiveness of Knowledge Management (KM) in various domains marks an important new watershed [7]. Moreover, the advent of tools and resources for the semantic web brings new challenges to the field of Information Extraction (IE), and in particular with respect to Ontology-Based IE (OBIE) [3]. A dynamic development of this field of research provides a plenty number of OBIE solutions [8]. Although the identification of the common architecture of OBIE systems is possible, a lot of details of individual OBIE systems causes they are different from each other. This paper presents the key characteristics of OBIE systems identified in the literature, concentrating on the factors that make OBIE systems different. In many cases, the OBIE systems can be a part of a larger system that answers user queries based on the information extracted by the OBIE system. Another difference may concern the output of OBIE systems. The comprehensive analysis of the available OBIE systems provides an insight into the field of OBIE systems, providing both the system categorization and knowledge handling. Accessing to relevant knowledge is critical for success at the proper OBIE system selection and implementation. The ontology-based approach (offered in this paper) helps in classification the existing OBIE systems along different dimensions. The aim of this study contains an attempt to knowledge conceptualization in OBIE systems domain, yielding up to date and comprehensive set of available OBIE approaches. The paper is constructed as follows: Section 2 offers a concise literature review of selected OBIE systems. In Section 3, the methodological aspects of OBIE knowledge modelling is provided. It is a foundation to elaborating the authors’ taxonomy, and in the aftermath of the knowledge systematization in the form of ontology. The concluding section provides the main outcomes of the paper and proposes some points for further discussion. 2. Literature review 2.1. Information extraction The massive amount of information available to business analysts makes information extraction and other natural language processing tools key enablers for the acquisition and use of that semantic information [3]. Without an Information Extraction (IE) system, it is obliged to read hundreds of textual documents, web sites, and other data to manually dig out the necessary information. In general, extraction of information consists on the identification of all mentions of concepts, instances, and properties in text or other sources. IE is a key Natural Language Processing (NLP) technology to introduce complementary information and knowledge into a document [2, 8]. It can be defined
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also as a task of identifying, collecting and normalizing relevant information from NL text and skipping irrelevant text passages. In a nutshell, IE aims to retrieve certain types of information from natural language text by processing them automatically [1]. The general aim of this is to process natural language text and to retrieve occurrences of a particular class of objects or events and occurrences of relationships among them [4], employing various algorithms and methods for information retrieval [9]. The IE system functionality is described in the Figure 1. The possible values for IE input model contain the specification of lexical knowledge, extraction rules, and an ontology, whereas a set of NL texts covers technical reports press releases, online-documents, or email [1]. Expecting output enfolds target knowledge structure, i.e. a set of instantiated and related concepts and attributes [10].
Fig. 1. IE system functionality.
The detailed process of IE is composed of the particular elements, ordered as follows: sentence segmentation, tokenization, part of speech (POS) tagging, entity detection, relation detection [1, 3, 8]. At the beginning, the selected input data provided especially in textual form is required. Then, the process of sentence segmentation takes place. The sentences are tokenized and aftermath, they are POS-tagged. As a result, the process of POS tagging delivers the list of lists of tuples [3]. Creating lists of trees is the outcome of chunking sentences [1]. After the relation detection process, the final list of tuples is delivered (Figure 2).
Fig. 2. Ontology-Based Information Extraction.
The application of ontologies in the last steps (entity detection and relation detection) performs IE process into Ontology-Based Information Extraction process [11]. The presented combination of ontology with IE system populates the information extraction process, which guided by the ontology to extract things such as classes, properties and instances. 2.2. Ontology-based Information Extraction The term ontology-based information extraction (OBIE) has recently emerged as a subfield of information extraction [1, 5]. OBIE is different from traditional IE because it finds type of extracted entity by linking it to its semantic description in the formal ontology [6]. Moreover, ontologies are used by the information extraction process and the output is generally presented through an ontology [12, 13]. The definition presented by [1] describes OBIE
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as a system that processes unstructured or semi-structured natural language text through a mechanism guided by ontologies to extract certain types of information and presents the output using ontologies. Although the individual OBIE systems are different from each other, a common architecture of such systems can be identified from a higher level (Figure 3). In many cases OBIE systems do not include or exploit all of these components [6]. In the presented procedure, the most common components are identified, especially focusing on textual input data processing. The architecture is divided into three main parts: input, modules and output. On the input there are following modules: domain expert knowledge referring to the knowledge of a specialist or expert in a particular domain area [14], various types of data sources may contain different types of data: structured and semistructured, unstructured data commonly treated as a main input, and unstructured user query [15]. The architecture also contains the following modules: knowledge editor, knowledge generation, text pre-processor, information extractor and search engine and query answering [8]. The knowledge editor module includes the essential tools to maintain the knowledge base (e.g. ontology editors, dictionary search engines etc.), whereas the knowledge generation module populates KB repository. The aim of information extraction module is to use annotated text and extraction rules to detect conceptual instances, properties and relations between them [1,3]. No matter what information extraction technique is used, it is guided by an ontology. The storage of the results of unstructured data analysis takes place in extracted information module. Finally, the text-based information extraction result must be represented in a structured form. The third part yields the knowledge base, encapsulating the ontology repository, thesaurus repository, lexical dictionary repository, extraction rules. The knowledge base aim is to contain the information in a form of a repository, conveying a means for information [9]. Concluding, the output of the OBIE system consists of the information extracted from the text. In addition, the output might also include links to text documents from which the information was extracted [10].
Fig. 3. A general architecture of OBIE system.
Oftentimes, the OBIE system can be a part of a larger query-answering system, where the output of the OBIE process is often stored in a database or a knowledge base [2]. The search engine and query answering mechanisms explore and make use of the extracted information, and aftermaths provide the answers for user queries [10]. This part may contain a reasoning component [11]. This architecture is typical for OBIE systems with slight variations, but it is worth to remember that individual OBIE systems are different from each other. An analysis of literature provides a description of different OBIE systems, methods, approaches and projects supporting them. The investigated solutions are various in terms of information extraction processes, available types of sources, extraction of ontology components, the update processes, offered support and knowledge base, text preprocessor, and data format [15]. It is worth to emphasize that OBIE method only guides the system that how to pull out efficient and relevant information using the Information Extraction methods, whereas a tool offers a wide range of possibilities [8]. Most of them are public under creative common licence with the full support. Meanwhile, the detailed analysis of the selected OBIE systems and tools is provided in the next section.
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3. Ontology-Based Information Extraction Knowledge Modelling 3.1. Methodology of knowledge conceptualization A basement for an ontology construction process is an adaptation of methodology proposed by Noy and McGuiness [16]. This process is divided into the following steps: (1) defining a set of criteria; (2) taxonomy construction; (3) ontology construction; (4) formal description; (5) defined classes creation; (6) reasoning process; (7) consistency verification; and (8) a set of results (Figure 4). Initially, the selection of ontological domain and range takes place. Performing the profound domain analysis is the first step of the ontology construction process. It is conducted by selecting the set of OBIE approaches. Then, on base of the analysis, the final set of properties and sub-properties is defined, in the aftermath forming the class hierarchy. This is a general foundation for a taxonomy construction for the existing OBIE systems and tools. Based on it, the set of criteria and sub-criteria is demarcated, extracting the following elements as: concepts, relations, and the properties from the scattered sources to a taxonomy form [4].
Fig. 4. A general procedure of an ontology construction.
Thereafter, the taxonomy is a basement for the ontology construction. To implement the ontology, Ontology Web Language (OWL) is used. Apart from providing a formal and structured method to gather, organize, and share data, it offers a lot of perquisites and plugins to manage the ontology in an efficient way. Verifying the coherence of the author’s ontology, the set of defined classes is constructed [4]. The application of reasoning mechanism allows validating the correctness both of the constructed defined classes and the whole ontology. The accurateness of obtained results after reasoning process confirms the ontology consistency and correctness [2]. It is worth to emphasize that this process was investigated using some validation queries. 3.2. Taxonomy Performing the deep domain analysis is the first step of the taxonomy, and further ontology construction process. The analysis encompasses a set of available OBIE systems and tools [1, 3, 5, 8, 10, 11, 17, 18, 19, 20, 21, 22], especially including: GATE, UIMA, sProUT, SOBA, Text-To-Onto, OntoX, Musing, TRIPS, OntoText, Evolva (Table 1). Each set of approaches contains the established set of properties. The detailed comparative analysis was presented in Table 1. It is a basement to elaborating authors’ taxonomy. The taxonomy contains the set of distinctive features. They were organized in hierarchical form including main criteria and sub-criteria. Thus, gathering
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knowledge in a holistic approach [23, 24] may support the selection of OBIE system or tool, and provide a specified guidelines and information of requirements related to given solutions [25]. Thereafter, the taxonomy contains 10 OBIE solutions. It includes the set of 8 properties and 38 sub-properties. The number of properties is constructed as follows: Information Extraction method, ontology construction and update, components of the ontology extracted, types of sources, offered support, knowledge base, text pre-processor and data format. Each set of properties comprises the sub-sets, according to the following frequency: Information Extraction method: linguistic rules, gazetteer lists, analyzing tags, partial parse trees; ontology construction and update: off-the-shelf, not updated, constructed by process, manually defined, n/a, generator, population, auto selection, multiple; components of the ontology extracted: instances, property values, classes, taxonomy, datatype property values, other relationships; types of sources: HTML files from a domain, XML files from a domain; offered support: documents from a domain, tool, other supporting solutions, components, providing an infrastructure for developing components, temporal information; knowledge base: knowledge base, semantic lexicon, WordNet, extraction rules; text pre-processor: text pre-processor, NLP, sentence split, text annotation, POS tagging; data format: XML/OWL, databases.
+
Gazetteer lists
+
+
+
Analyzing tags
+
+
+
Partial parse trees Ontology construction and update
Off-the-shelf
+
+
+
+ +
+ + +
+
+
+
Constructed by process
+ +
+
not updated
Manually defined
+
Evolva
+
OntoText
+
TRIPS
SOBA
+
Musing
sProUT
Information Extraction method
Sub-criteria
OntoX
UIMA
Linguistic rules
Criteria
Text-To-Onto
GATE
Table 1. A comparative analysis of selected OBIE systems and tools.
+
+
+
+ +
+
+
+
N/A
+
+
generator
+
population
+
+
+
auto selection
+
+
+
+
multiple Components of the ontology extracted
+
+
Instances
+
+
+
Property values
+
+
+
Classes
+
+
+
Taxonomy
+
+
+
Datatype property values
+
+
Other relationships
+
+
+
+
+
+
+
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Types of sources
Offered support
HTML files from a domain
+
+
XML files from a domain
+
+
Documents from a domain
+
+
+
Tool
+
+
+
Other supporting solutions
+
+
Components
+
+
Providing an infrastructure for developing components
+
+
+
+
+
+
+
+
+
+
Text preprocessor
Data format
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
WordNet
+
+
Extraction rules
+
Text pre-processor
+
NLP
+
Sentence split
+
Text annotation
+
POS tagging
+
XML/OWL
+
+
+
+
Semantic Lexicon
+
+
+
+
+
+
+
Knowledge base
Databases
+
+
+
Temporal information Knowledge Base
+
+ +
7
+
+
+
+
+
+
+
+ +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
The taxonomy offers a comprehensive and detailed approach of OBIE solutions. It provides both knowledge systematization for OBIE systems and tools and offers complex view of them. The taxonomy is a basis for the ontology construction, which is presented in details below. 3.3. Ontology The proposed authors’ ontology is developed with an on-going viewpoint based on literature review and a specified analysis of key factors of the selected OBIE systems and tools. A general procedure of an ontology construction is described in section 3.1 as well as depicted on Figure 4. Using Protégé application allows building the ontology. To implement this, OWL standard was employed. On base of the extraction of concepts, relations, object and datatype properties, the class hierarchy was detailed. The class hierarchy is presented in the figure 5.
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Fig. 5. Design of the class hierarchy.
The ontology contains the following object properties: “has Criterion” and “is Criterion of”. The domain and range was defined for the first object property as follows: OBIE systems and Criteria, whereas an inversion is applied to the second object property called: “is Criterion of”. Moreover, “has Criterion” is determined by inverse functional characteristic unlike “is Criterion of”, which is functional. The is-a relationships are showed also in the Figure 5. From the technological point of view, each approach has distinguished features, implemented in the ontology. For example, OntoX approach has the following set of the characteristics: Components of the ontology extracted: Datatype property values, Instances; Types of sources: Documents from a domain; Information Extraction method: Linguistic rules; Ontology construction and update: Manually defined, not updated, Population; Offered support: Other supporting solutions, Temporal information; Tool; Knowledge base: KB, Extraction rules; Text PreProcessor: Text Pre-Processor; Data format: XML/OWL. The graphical representation of this class is shown on the Figure 6. It is worth to emphasize that is only an example of a detailed description of the chosen OBIE solution. The ontology contains the similar implementations of features for each of OBIE systems and tools.
Fig. 6. Exemplary sub-class called OntoX with its characteristics.
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3.4. Validation - competency questions This section introduces the exemplary competence questions and verifies their utility in retrieving information with response to the user needs. Rightness of obtained results confirms the validation process and consequently provides the set of results (OBIE systems and tools). Following the presented ontology construction methodology on the Figure 4, validation of the proposed ontology checks its formal structure and coherence with the domain knowledge. Thus, the whole process requires using query algorithms to extract the valuable knowledge from ontology at the end of reasoning process. To investigate the correctness of the proposed ontology, the competence questions are constructed and implemented using Description Logic query mechanism. The case study focuses on the choosing the OBIE systems and tools including the basic set of features: Offered support: Components, Tool; Information Extraction method: Gazetteer lists, Linguistic rules; Types of sources: HTML files from a domain, XML files from a domain; Components of the ontology extracted: Instances; Ontology construction and update: Off-the-shelf. Thus, the definition containing the set of criteria (CQ 1) was constructed as follows (Figure 7). This is an essential issue to fulfill each of the criteria and sub-criteria in attempt to belong to the final ranking. The OntoGraf tool is used to present the graphical representation of the results. In this case 2 OBIE solutions fulfill this defined set of criteria (GATE and UIMA).
Fig. 7. Results of using Description Logic query to extract OBIE systems and tools for the first competence question.
Additional experimental query has been posed to find the relevant OBIE systems and tools including the set of preferable features important for ontological support and text pre-processing aspects: Knowledge base: KB, Semantic Lexicon, Extraction rules; Information Extraction method: Gazetteer lists; Text Pre-Processing: Sentence split, NLP, POS tagging; Components of the ontology extracted: Instances; Data format: XML/OWL; Ontology construction and update: Generator, Population, Off-the-shelf. The ranking of results is acquired from a number of implemented instances of OBIE approaches and by queries on capabilities offered by Description Logic Query, implemented in Protégé software. Defining the necessary and sufficient conditions, and afterwards implementing it into the Protégé software permits on starting the reasoning process. Visualising the final ranking of the solutions presents on Figure 8, using OntoGraf tool. Based on the experimentation results, the final set of the approaches is obtained. In accordance with the constructed definition, only one solution matches these requirements (GATE).
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Fig. 8. Results of using Description Logic query to extract OBIE systems and tools for the second competence question.
The exemplary competence questions confirm the rightness of obtained results and the validation process and consequently provide the set of results. The proposed classification enclosed in ontology, supports the rapid identification of the main attributes according to the OBIE systems and tools. It is worth to mention that there is a lot of possibility to construct and implement various types of competency questions. 4. Conclusions The OBIE systems are important tools in processing data and information. The deep analysis provided in this paper confirms both the timeliness of the problem and the complexity of the proper selection and exploitation of that tool. The analysis of the literature confirmed the lack of knowledge systematization in this field. The paper is an attempt to knowledge systematization in OBIE domain. To sum up, this attempt should be considered as an effective. The author developed knowledge base focused on OBIE systems in the form of ontology. To elaborate the ontology, the reference literature sources were investigated, and aftermath they were deeply analyzed, providing the reliable source of expert knowledge. Validation, coherency and correctness of the authors ontology was made on base of Noy and McGuiness methodology [6] and using competency questions. Presented ontology allows to capture and formalize OBIE domain of knowledge. The author’s ontology is designed in a manner to be useful for the researchers, i.e. allow them to rapidly and intuitively find, any OBIE approaches in any of the major or minor model properties. Technically, ontology provides knowledge that can be incorporated into any database, knowledge base or information system holding knowledge associated to OBI. The formalized structure of the ontology offers a machine-readable access and handling semantic data is an interesting step to enhance the searching capacity and knowledge sharing of the proposed ontology. This form of problem solving ensures semantic interoperability for knowledge and data collected. During the research, some possible areas of improvement of the presented knowledge model and future work directions were identified. It seems to be interesting to extend the presented knowledge base using the methodological background of particular OBIE approaches. Moreover, the presented knowledge model could be bridged with others ontologies. References [1] Wimalasuriya DC, Dejing Dou. Ontology-based information extraction: An introduction and a survey of current approaches. Journal of Information Science 2010;36:306–23. [2] Konys A. Knowledge-Based Approach to Question Answering System Selection. In: Núñez M, Nguyen NT, Camacho D, Trawiński B, editors. Computational Collective Intelligence, vol. 9329, Cham: Springer International Publishing; 2015, p. 361–70. [3] Maynard D, Peters W, Li Y. Metrics for Evaluation of Ontology-based Information. WWW 2006 Workshop, Scotland: 2006.
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[4] Konys A. An Ontology-Based Knowledge Modelling for a Sustainability Assessment Domain. Sustainability 2018;10:300. [5] Shah R, Jain S. Ontology-based Information Extraction: An Overview and a Study of different Approaches. International Journal of Computer Applications 2014;87:6–8. [6] Ali F, Kim EK, Kim Y-G. Type-2 fuzzy ontology-based opinion mining and information extraction: A proposal to automate the hotel reservation system. Applied Intelligence 2015;42:481–500. [7] Faizi S, Rashid T, Sałabun W, Zafar S, Wątróbski J. Decision Making with Uncertainty Using Hesitant Fuzzy Sets. International Journal of Fuzzy Systems 2018;20:93–103. [8] Bontcheva K, Tablan V, Maynard D, Cunningham H. Evolving GATE to meet new challenges in language engineering. Natural Language Engineering 2004;10:349–73. [9] Riloff E. Information extraction as a stepping stone toward story understanding. In: Ram A, Moorman K, editors. Understanding language understanding: computational models of reading, Cambridge, Mass.: MIT Press; 1999. [10] Wimalasuriya DC, Dou D. Components for information extraction: ontology-based information extractors and generic platforms, ACM Press; 2010, p. 9. [11] Gutierrez F, Dou D, Fickas S, Wimalasuriya D, Zong H. A hybrid ontology-based information extraction system. Journal of Information Science 2016;42:798–820. [12] Konys A, Wątróbski J, Różewski P. Approach to Practical Ontology Design for Supporting COTS Component Selection Processes. In: Selamat A, Nguyen NT, Haron H, editors. Intelligent Information and Database Systems, vol. 7803, Berlin, Heidelberg: Springer Berlin Heidelberg; 2013, p. 245–55. [13] Flouris G, Manakanatas D, Kondylakis H, Plexousakis D, Antoniou G. Ontology change: classification and survey. The Knowledge Engineering Review 2008;23. [14] Wątróbski J, Ziemba P, Jankowski J, Zioło M. Green Energy for a Green City—A Multi-Perspective Model Approach. Sustainability 2016;8:702. [15] Konys A. A Framework for Analysis of Ontology-Based Data Access. In: Nguyen NT, Iliadis L, Manolopoulos Y, Trawiński B, editors. Computational Collective Intelligence, vol. 9876, Cham: Springer International Publishing; 2016, p. 397–408. [16] Noy N, McGuiness D. Ontology Development 101: A Guide to Creating Your First Ontology. Stanford, CA, USA: Stanford Knowledge Systems Laboratory; 2001. [17] Ferrucci D, Lally A. UIMA: an architectural approach to unstructured information processing in the corporate research environment. Natural Language Engineering 2004;10:327–48. [18] Bontcheva K, Cunningham H, Roberts I, Roberts A, Tablan V, Aswani N, et al. GATE Teamware: a web-based, collaborative text annotation framework. Language Resources and Evaluation 2013;47:1007–29. [19] Drozdzynski W, Becker M, Krieger H-U, Piskorski J, Schäfer U, Xu F. SProUT (Shallow Processing with Unification and Typed Feature Structures). Proceedings of the International Conference on Natural Language Processing, 2002. [20] Buitelaar P, Cimiano P, Racioppa S, Siegel M. Ontology-based Information Extraction with SOBA. Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy: LREC; 2006. [21] Maedche A, Staab S. The Text-To-Onto Ontology Learning Environment. Software Demonstration at the Eighth International Conference on Conceptual Structures, Berlin: Springer-Verlag; 2000. [22] Yildiz B, Miksch S. ontoX - A Method for Ontology-Driven Information Extraction. In: Gervasi O, Gavrilova ML, editors. Computational Science and Its Applications – ICCSA 2007, vol. 4707, Berlin, Heidelberg: Springer Berlin Heidelberg; 2007, p. 660–73. [23] Ziemba P, Wątróbski J, Zioło M, Karczmarczyk A. Using the PROSA Method in Offshore Wind Farm Location Problems. Energies 2017;10:1755. [24] Adrian B. Incorporating ontological background knowledge into Information Extraction. Doctoral Consortium. ISWC 2009, 2009. [25] Faizi S, Sałabun W, Rashid T, Wątróbski J, Zafar S. Group Decision-Making for Hesitant Fuzzy Sets Based on Characteristic Objects Method. Symmetry 2017;9:136.
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ScienceDirect 22nd International Conference on Knowledge-Based and Intelligent Information & Procedia Computer Science 126 (2018) 2208–2218 Engineering Systems
Towards Knowledge Handling in Ontology-Based Information 22nd International Conference on Knowledge-Based and Intelligent Information & Extraction Engineering Systems Systems AgnieszkainKonys* Towards Knowledge Handling Ontology-Based Information West-Pomeranian University of Technology in Szczecin, Faculty of Computer Science and Information Technology, Żołnierska 49, 71-210 Extraction Systems Szczecin, Poland Abstract
Agnieszka Konys*
West-Pomeranian University of Technology in Szczecin, Faculty of Computer Science and Information Technology, Żołnierska 49, 71-210
Poland Ontologies proved to be efficient and powerful tools forSzczecin, gathering and sharing knowledge, providing explicit specifications of conceptualizations. However, the proper ontology construction processes and their updating of various data sources, require a huge effort and well-adjusted mechanisms to their extraction, positioning and sharing. In this context, the application of Abstract a proper Ontology-Based Information Extraction (OBIE) system may help in solving these problems. The paper is a successful attempt to purvey state-of-the-art of selected OBIE systems, followed by the process of taxonomy construction and aftermath Ontologies proved to be efficient and powerful tools for gathering and sharing knowledge, providing explicit specifications of knowledge systematization of particular OBIE approaches. conceptualizations. However, the proper ontology construction processes and their updating of various data sources, require a huge effort and well-adjusted mechanisms to their extraction, positioning and sharing. In this context, the application of a proper Ontology-Based Information Extraction © 2018 The Authors. Published by Elsevier Ltd. (OBIE) system may help in solving these problems. The paper is a successful attempt to purvey state-of-the-art of selected OBIE systems, followed by the process of taxonomy construction and aftermath This is an open access articleofunder the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) knowledge systematization particular OBIE approaches.
Selection andAuthors. peer-review underby responsibility © 2018 The Published Elsevier Ltd.of KES International. This is an open accessPublished article under the CC Ltd. BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. by Elsevier Selection and peer-review under responsibility of KES International. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Keywords: Ontology-Based Information Extraction, Information Extraction, ontology, knowledge management
Selection and peer-review under responsibility of KES International.
Keywords: Ontology-Based Information Extraction, Information Extraction, ontology, knowledge management
* Corresponding author. Tel.: +48-91-449-56-62; fax: +48-91-449-56-62. E-mail address: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) * Corresponding author. under Tel.: +48-91-449-56-62; fax:International. +48-91-449-56-62. Selection and peer-review responsibility of KES E-mail address: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International. 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International. 10.1016/j.procs.2018.07.228
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1. Introduction A huge number of the heterogeneity of the documents and their different types both structured, semi-structured and unstructured poses new challenges and requires adapting the new technologies. The process of retrieving information, especially from unstructured and semi-structured sources, is very demanding task [1]. The greater role is assigned to efficient data collection, analysis and processing. Nowadays, the constantly growing data becomes rather useless if it is impossible to extract meaningful, relevant and applicable information out of it [2]. Further, the problem concerns the extraction of the structured information from raw data. In many domains the unstructured data are the most frequent types of gathering documents, where in many cases it gains more than 80%. The process of these resources collection and processing may provide interesting knowledge from large collections of unstructured documents [3] and it has direct influence on the proper information management [4]. In last decade the terms related to Semantic Web (e.g. Information Extraction (IE), Knowledge Extraction, ontologies etc.) become significant elements in the efficient way of information retrieval, processing and supporting availability of machine readable data. This success, widespread usage and commercialization emphasizes their role of WWW community [5]. What is more, a close relation between Ontology-Based Information Extraction (OBIE) and the Semantic Web is noticeable [1]. OBIE is the process of identifying in text or other sources relevant concepts, properties, and relations expressed in an ontology. OBIE systems generate semantic content which is known as Semantic Annotation for the Web pages [3]. The advantage of OBIE over traditional IE is that the output (semantic metadata about the text) is linked to an ontology. Thus, semantic agents can directly process semantic content for Information Retrieval. Consequently, OBIE systems extract much more meaningful information about the text [6], especially making use of relational information or performing reasoning. The growing pervasiveness of Knowledge Management (KM) in various domains marks an important new watershed [7]. Moreover, the advent of tools and resources for the semantic web brings new challenges to the field of Information Extraction (IE), and in particular with respect to Ontology-Based IE (OBIE) [3]. A dynamic development of this field of research provides a plenty number of OBIE solutions [8]. Although the identification of the common architecture of OBIE systems is possible, a lot of details of individual OBIE systems causes they are different from each other. This paper presents the key characteristics of OBIE systems identified in the literature, concentrating on the factors that make OBIE systems different. In many cases, the OBIE systems can be a part of a larger system that answers user queries based on the information extracted by the OBIE system. Another difference may concern the output of OBIE systems. The comprehensive analysis of the available OBIE systems provides an insight into the field of OBIE systems, providing both the system categorization and knowledge handling. Accessing to relevant knowledge is critical for success at the proper OBIE system selection and implementation. The ontology-based approach (offered in this paper) helps in classification the existing OBIE systems along different dimensions. The aim of this study contains an attempt to knowledge conceptualization in OBIE systems domain, yielding up to date and comprehensive set of available OBIE approaches. The paper is constructed as follows: Section 2 offers a concise literature review of selected OBIE systems. In Section 3, the methodological aspects of OBIE knowledge modelling is provided. It is a foundation to elaborating the authors’ taxonomy, and in the aftermath of the knowledge systematization in the form of ontology. The concluding section provides the main outcomes of the paper and proposes some points for further discussion. 2. Literature review 2.1. Information extraction The massive amount of information available to business analysts makes information extraction and other natural language processing tools key enablers for the acquisition and use of that semantic information [3]. Without an Information Extraction (IE) system, it is obliged to read hundreds of textual documents, web sites, and other data to manually dig out the necessary information. In general, extraction of information consists on the identification of all mentions of concepts, instances, and properties in text or other sources. IE is a key Natural Language Processing (NLP) technology to introduce complementary information and knowledge into a document [2, 8]. It can be defined
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also as a task of identifying, collecting and normalizing relevant information from NL text and skipping irrelevant text passages. In a nutshell, IE aims to retrieve certain types of information from natural language text by processing them automatically [1]. The general aim of this is to process natural language text and to retrieve occurrences of a particular class of objects or events and occurrences of relationships among them [4], employing various algorithms and methods for information retrieval [9]. The IE system functionality is described in the Figure 1. The possible values for IE input model contain the specification of lexical knowledge, extraction rules, and an ontology, whereas a set of NL texts covers technical reports press releases, online-documents, or email [1]. Expecting output enfolds target knowledge structure, i.e. a set of instantiated and related concepts and attributes [10].
Fig. 1. IE system functionality.
The detailed process of IE is composed of the particular elements, ordered as follows: sentence segmentation, tokenization, part of speech (POS) tagging, entity detection, relation detection [1, 3, 8]. At the beginning, the selected input data provided especially in textual form is required. Then, the process of sentence segmentation takes place. The sentences are tokenized and aftermath, they are POS-tagged. As a result, the process of POS tagging delivers the list of lists of tuples [3]. Creating lists of trees is the outcome of chunking sentences [1]. After the relation detection process, the final list of tuples is delivered (Figure 2).
Fig. 2. Ontology-Based Information Extraction.
The application of ontologies in the last steps (entity detection and relation detection) performs IE process into Ontology-Based Information Extraction process [11]. The presented combination of ontology with IE system populates the information extraction process, which guided by the ontology to extract things such as classes, properties and instances. 2.2. Ontology-based Information Extraction The term ontology-based information extraction (OBIE) has recently emerged as a subfield of information extraction [1, 5]. OBIE is different from traditional IE because it finds type of extracted entity by linking it to its semantic description in the formal ontology [6]. Moreover, ontologies are used by the information extraction process and the output is generally presented through an ontology [12, 13]. The definition presented by [1] describes OBIE
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as a system that processes unstructured or semi-structured natural language text through a mechanism guided by ontologies to extract certain types of information and presents the output using ontologies. Although the individual OBIE systems are different from each other, a common architecture of such systems can be identified from a higher level (Figure 3). In many cases OBIE systems do not include or exploit all of these components [6]. In the presented procedure, the most common components are identified, especially focusing on textual input data processing. The architecture is divided into three main parts: input, modules and output. On the input there are following modules: domain expert knowledge referring to the knowledge of a specialist or expert in a particular domain area [14], various types of data sources may contain different types of data: structured and semistructured, unstructured data commonly treated as a main input, and unstructured user query [15]. The architecture also contains the following modules: knowledge editor, knowledge generation, text pre-processor, information extractor and search engine and query answering [8]. The knowledge editor module includes the essential tools to maintain the knowledge base (e.g. ontology editors, dictionary search engines etc.), whereas the knowledge generation module populates KB repository. The aim of information extraction module is to use annotated text and extraction rules to detect conceptual instances, properties and relations between them [1,3]. No matter what information extraction technique is used, it is guided by an ontology. The storage of the results of unstructured data analysis takes place in extracted information module. Finally, the text-based information extraction result must be represented in a structured form. The third part yields the knowledge base, encapsulating the ontology repository, thesaurus repository, lexical dictionary repository, extraction rules. The knowledge base aim is to contain the information in a form of a repository, conveying a means for information [9]. Concluding, the output of the OBIE system consists of the information extracted from the text. In addition, the output might also include links to text documents from which the information was extracted [10].
Fig. 3. A general architecture of OBIE system.
Oftentimes, the OBIE system can be a part of a larger query-answering system, where the output of the OBIE process is often stored in a database or a knowledge base [2]. The search engine and query answering mechanisms explore and make use of the extracted information, and aftermaths provide the answers for user queries [10]. This part may contain a reasoning component [11]. This architecture is typical for OBIE systems with slight variations, but it is worth to remember that individual OBIE systems are different from each other. An analysis of literature provides a description of different OBIE systems, methods, approaches and projects supporting them. The investigated solutions are various in terms of information extraction processes, available types of sources, extraction of ontology components, the update processes, offered support and knowledge base, text preprocessor, and data format [15]. It is worth to emphasize that OBIE method only guides the system that how to pull out efficient and relevant information using the Information Extraction methods, whereas a tool offers a wide range of possibilities [8]. Most of them are public under creative common licence with the full support. Meanwhile, the detailed analysis of the selected OBIE systems and tools is provided in the next section.
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3. Ontology-Based Information Extraction Knowledge Modelling 3.1. Methodology of knowledge conceptualization A basement for an ontology construction process is an adaptation of methodology proposed by Noy and McGuiness [16]. This process is divided into the following steps: (1) defining a set of criteria; (2) taxonomy construction; (3) ontology construction; (4) formal description; (5) defined classes creation; (6) reasoning process; (7) consistency verification; and (8) a set of results (Figure 4). Initially, the selection of ontological domain and range takes place. Performing the profound domain analysis is the first step of the ontology construction process. It is conducted by selecting the set of OBIE approaches. Then, on base of the analysis, the final set of properties and sub-properties is defined, in the aftermath forming the class hierarchy. This is a general foundation for a taxonomy construction for the existing OBIE systems and tools. Based on it, the set of criteria and sub-criteria is demarcated, extracting the following elements as: concepts, relations, and the properties from the scattered sources to a taxonomy form [4].
Fig. 4. A general procedure of an ontology construction.
Thereafter, the taxonomy is a basement for the ontology construction. To implement the ontology, Ontology Web Language (OWL) is used. Apart from providing a formal and structured method to gather, organize, and share data, it offers a lot of perquisites and plugins to manage the ontology in an efficient way. Verifying the coherence of the author’s ontology, the set of defined classes is constructed [4]. The application of reasoning mechanism allows validating the correctness both of the constructed defined classes and the whole ontology. The accurateness of obtained results after reasoning process confirms the ontology consistency and correctness [2]. It is worth to emphasize that this process was investigated using some validation queries. 3.2. Taxonomy Performing the deep domain analysis is the first step of the taxonomy, and further ontology construction process. The analysis encompasses a set of available OBIE systems and tools [1, 3, 5, 8, 10, 11, 17, 18, 19, 20, 21, 22], especially including: GATE, UIMA, sProUT, SOBA, Text-To-Onto, OntoX, Musing, TRIPS, OntoText, Evolva (Table 1). Each set of approaches contains the established set of properties. The detailed comparative analysis was presented in Table 1. It is a basement to elaborating authors’ taxonomy. The taxonomy contains the set of distinctive features. They were organized in hierarchical form including main criteria and sub-criteria. Thus, gathering
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knowledge in a holistic approach [23, 24] may support the selection of OBIE system or tool, and provide a specified guidelines and information of requirements related to given solutions [25]. Thereafter, the taxonomy contains 10 OBIE solutions. It includes the set of 8 properties and 38 sub-properties. The number of properties is constructed as follows: Information Extraction method, ontology construction and update, components of the ontology extracted, types of sources, offered support, knowledge base, text pre-processor and data format. Each set of properties comprises the sub-sets, according to the following frequency: Information Extraction method: linguistic rules, gazetteer lists, analyzing tags, partial parse trees; ontology construction and update: off-the-shelf, not updated, constructed by process, manually defined, n/a, generator, population, auto selection, multiple; components of the ontology extracted: instances, property values, classes, taxonomy, datatype property values, other relationships; types of sources: HTML files from a domain, XML files from a domain; offered support: documents from a domain, tool, other supporting solutions, components, providing an infrastructure for developing components, temporal information; knowledge base: knowledge base, semantic lexicon, WordNet, extraction rules; text pre-processor: text pre-processor, NLP, sentence split, text annotation, POS tagging; data format: XML/OWL, databases.
+
Gazetteer lists
+
+
+
Analyzing tags
+
+
+
Partial parse trees Ontology construction and update
Off-the-shelf
+
+
+
+ +
+ + +
+
+
+
Constructed by process
+ +
+
not updated
Manually defined
+
Evolva
+
OntoText
+
TRIPS
SOBA
+
Musing
sProUT
Information Extraction method
Sub-criteria
OntoX
UIMA
Linguistic rules
Criteria
Text-To-Onto
GATE
Table 1. A comparative analysis of selected OBIE systems and tools.
+
+
+
+ +
+
+
+
N/A
+
+
generator
+
population
+
+
+
auto selection
+
+
+
+
multiple Components of the ontology extracted
+
+
Instances
+
+
+
Property values
+
+
+
Classes
+
+
+
Taxonomy
+
+
+
Datatype property values
+
+
Other relationships
+
+
+
+
+
+
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Types of sources
Offered support
HTML files from a domain
+
+
XML files from a domain
+
+
Documents from a domain
+
+
+
Tool
+
+
+
Other supporting solutions
+
+
Components
+
+
Providing an infrastructure for developing components
+
+
+
+
+
+
+
+
+
+
Text preprocessor
Data format
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
WordNet
+
+
Extraction rules
+
Text pre-processor
+
NLP
+
Sentence split
+
Text annotation
+
POS tagging
+
XML/OWL
+
+
+
+
Semantic Lexicon
+
+
+
+
+
+
+
Knowledge base
Databases
+
+
+
Temporal information Knowledge Base
+
+ +
7
+
+
+
+
+
+
+
+ +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
The taxonomy offers a comprehensive and detailed approach of OBIE solutions. It provides both knowledge systematization for OBIE systems and tools and offers complex view of them. The taxonomy is a basis for the ontology construction, which is presented in details below. 3.3. Ontology The proposed authors’ ontology is developed with an on-going viewpoint based on literature review and a specified analysis of key factors of the selected OBIE systems and tools. A general procedure of an ontology construction is described in section 3.1 as well as depicted on Figure 4. Using Protégé application allows building the ontology. To implement this, OWL standard was employed. On base of the extraction of concepts, relations, object and datatype properties, the class hierarchy was detailed. The class hierarchy is presented in the figure 5.
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Fig. 5. Design of the class hierarchy.
The ontology contains the following object properties: “has Criterion” and “is Criterion of”. The domain and range was defined for the first object property as follows: OBIE systems and Criteria, whereas an inversion is applied to the second object property called: “is Criterion of”. Moreover, “has Criterion” is determined by inverse functional characteristic unlike “is Criterion of”, which is functional. The is-a relationships are showed also in the Figure 5. From the technological point of view, each approach has distinguished features, implemented in the ontology. For example, OntoX approach has the following set of the characteristics: Components of the ontology extracted: Datatype property values, Instances; Types of sources: Documents from a domain; Information Extraction method: Linguistic rules; Ontology construction and update: Manually defined, not updated, Population; Offered support: Other supporting solutions, Temporal information; Tool; Knowledge base: KB, Extraction rules; Text PreProcessor: Text Pre-Processor; Data format: XML/OWL. The graphical representation of this class is shown on the Figure 6. It is worth to emphasize that is only an example of a detailed description of the chosen OBIE solution. The ontology contains the similar implementations of features for each of OBIE systems and tools.
Fig. 6. Exemplary sub-class called OntoX with its characteristics.
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3.4. Validation - competency questions This section introduces the exemplary competence questions and verifies their utility in retrieving information with response to the user needs. Rightness of obtained results confirms the validation process and consequently provides the set of results (OBIE systems and tools). Following the presented ontology construction methodology on the Figure 4, validation of the proposed ontology checks its formal structure and coherence with the domain knowledge. Thus, the whole process requires using query algorithms to extract the valuable knowledge from ontology at the end of reasoning process. To investigate the correctness of the proposed ontology, the competence questions are constructed and implemented using Description Logic query mechanism. The case study focuses on the choosing the OBIE systems and tools including the basic set of features: Offered support: Components, Tool; Information Extraction method: Gazetteer lists, Linguistic rules; Types of sources: HTML files from a domain, XML files from a domain; Components of the ontology extracted: Instances; Ontology construction and update: Off-the-shelf. Thus, the definition containing the set of criteria (CQ 1) was constructed as follows (Figure 7). This is an essential issue to fulfill each of the criteria and sub-criteria in attempt to belong to the final ranking. The OntoGraf tool is used to present the graphical representation of the results. In this case 2 OBIE solutions fulfill this defined set of criteria (GATE and UIMA).
Fig. 7. Results of using Description Logic query to extract OBIE systems and tools for the first competence question.
Additional experimental query has been posed to find the relevant OBIE systems and tools including the set of preferable features important for ontological support and text pre-processing aspects: Knowledge base: KB, Semantic Lexicon, Extraction rules; Information Extraction method: Gazetteer lists; Text Pre-Processing: Sentence split, NLP, POS tagging; Components of the ontology extracted: Instances; Data format: XML/OWL; Ontology construction and update: Generator, Population, Off-the-shelf. The ranking of results is acquired from a number of implemented instances of OBIE approaches and by queries on capabilities offered by Description Logic Query, implemented in Protégé software. Defining the necessary and sufficient conditions, and afterwards implementing it into the Protégé software permits on starting the reasoning process. Visualising the final ranking of the solutions presents on Figure 8, using OntoGraf tool. Based on the experimentation results, the final set of the approaches is obtained. In accordance with the constructed definition, only one solution matches these requirements (GATE).
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Fig. 8. Results of using Description Logic query to extract OBIE systems and tools for the second competence question.
The exemplary competence questions confirm the rightness of obtained results and the validation process and consequently provide the set of results. The proposed classification enclosed in ontology, supports the rapid identification of the main attributes according to the OBIE systems and tools. It is worth to mention that there is a lot of possibility to construct and implement various types of competency questions. 4. Conclusions The OBIE systems are important tools in processing data and information. The deep analysis provided in this paper confirms both the timeliness of the problem and the complexity of the proper selection and exploitation of that tool. The analysis of the literature confirmed the lack of knowledge systematization in this field. The paper is an attempt to knowledge systematization in OBIE domain. To sum up, this attempt should be considered as an effective. The author developed knowledge base focused on OBIE systems in the form of ontology. To elaborate the ontology, the reference literature sources were investigated, and aftermath they were deeply analyzed, providing the reliable source of expert knowledge. Validation, coherency and correctness of the authors ontology was made on base of Noy and McGuiness methodology [6] and using competency questions. Presented ontology allows to capture and formalize OBIE domain of knowledge. The author’s ontology is designed in a manner to be useful for the researchers, i.e. allow them to rapidly and intuitively find, any OBIE approaches in any of the major or minor model properties. Technically, ontology provides knowledge that can be incorporated into any database, knowledge base or information system holding knowledge associated to OBI. The formalized structure of the ontology offers a machine-readable access and handling semantic data is an interesting step to enhance the searching capacity and knowledge sharing of the proposed ontology. This form of problem solving ensures semantic interoperability for knowledge and data collected. During the research, some possible areas of improvement of the presented knowledge model and future work directions were identified. It seems to be interesting to extend the presented knowledge base using the methodological background of particular OBIE approaches. Moreover, the presented knowledge model could be bridged with others ontologies. References [1] Wimalasuriya DC, Dejing Dou. Ontology-based information extraction: An introduction and a survey of current approaches. Journal of Information Science 2010;36:306–23. [2] Konys A. Knowledge-Based Approach to Question Answering System Selection. In: Núñez M, Nguyen NT, Camacho D, Trawiński B, editors. Computational Collective Intelligence, vol. 9329, Cham: Springer International Publishing; 2015, p. 361–70. [3] Maynard D, Peters W, Li Y. Metrics for Evaluation of Ontology-based Information. WWW 2006 Workshop, Scotland: 2006.
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