The activities of organizing knowledge recorded in texts and obtaining knowledge from human experts - the knowledge acquisition process are essential for scientific development. In this article, we propose methodological steps for knowledge acquisition, which have been applied to the construction of biomedical ontologies. The methodological steps are tested in a real case of knowledge acquisition in the domain of the human blood. We hope to contribute to the improvement of knowledge acquisition for the representation of scientific knowledge in ontologies.
Ontologies have been proposed as an alternative for creating
representations of reality suitable for computers. At least
four activities are essential in the development of
ontologies: specification, knowledge acquisition, conceptualizatio
and formalization. In knowledge acquisition (KA), the
experience available in the literature of diverse fields mentions
difficulties in communication between experts and
professionals who deal with information
This article investigates the activity of KA within the scope of biomedicine. In order to explore the activity, we propose procedures for KA employing the best practices referenced in the literature. We systematize these procedures in a list of methodological steps with the aim of testing their feasibility in a real case.
The empirical research is conducted within the scope of a biomedical project, focused on human blood. The knowledge acquisition results have been used in the development of a knowledge base for scientific and educational applications related to the human blood. Descriptions of different stages of research are provided as examples throughout the article. The main contributions are the aforementioned list of steps and observations made in real situations with the aim of improving the KA performance.
The remainder of this paper is organized as follows: section 2 reviews the literature on KA. Section 3 explains the theoretical rationale, the systematization and tools that compose the KA methodology. Section 4 presents comments of interest during the next phases of the research. Finally, section 5 puts forward our final remarks.
2.1
The KA activity generally includes the collection, analysis,
structuring and validation of knowledge for representation
purposes
KA techniques can be classified into manual techniques and
computer-based techniques
Protocol-generation techniques include interviews. The
most well-known technique for interviews is the teachback
technique
Natural Language Processing (NLP) techniques are
common in the biomedical domain
A comparison between the two methods involved the
testing of systems using both approaches to the automatic
categorization of MEDLINE abstracts
This work explores the best practices in an ongoing KA
scenario applied within the scope of the Blood Project
In this section, we describe the list of steps for KA. Then, we present a synoptic table summarizing the tasks involved and systematizing the steps in the list, which was divided into four main phases: extraction, elicitation, validation and refinement.
In the extraction phase we applied NLP techniques and tools in order to obtain candidate terms for the ontology. KA from texts consists of three main activities: construction of a corpus related to blood transfusion, codification of this corpus and information retrieval from the corpus.
The subset of the corpus related to blood transfusion uses the manual of the American Association of Blood Banking (AABB) as a source. From the AABB website1 we downloaded thirty-two chapters that comprise the seventeenth edition of the manual. From this material, twenty-seven chapters were processed by the tool used for codification. This material was select as a sample according to the stage of the research underway when writing this paper. Certainly, in future works, diseases processes and clinical finding will be considered. 1 Available at: <http://www.aabb.org>. Accessed: July 23, 2010
In the activity of codification we employed Sketch Engine2, an online tool for the creation and analysis of linguistic corpora. The fragmentation of the text into morphemes and the identification of the grammatical classes are automatically performed.
After the codification activity, we proceeded with the
information retrieval from the corpus with the aim of
identifying terms used to describe blood transfusion procedures. In
order to do so, we used word suffixes common of medical
terms
As a final task of the extraction phase, we analyzed the morphological productivity of the terms obtained using the British National Corpus (BNC)3 as a reference. The analysis consisted of comparing the frequency of each term in the corpus with its frequency in the reference corpus. In order to proceed with the morphological productivity analysis we used the AntConC4 tool.
In the elicitation phase, we made use of the terms obtained
in the extraction phase, which were employed as guidelines
to start the contact with experts. This phase consisted of
holding interviews and the application of KA techniques
with experts, doctors, biologists and researchers. During the
course of the interviews, sorting and matrix techniques were
applied. The cycle that characterizes the clinical process,
ranging from the development of an infectious disease
through its treatment, was adopted to guide the approach
taken with the experts. For modeling the domain, we
adopted the disease as disposition approach, as proposed by
In the stage called etiological process, there is a healthy
human body with characteristics that are normal according
to medical parameters. In the pre-clinical manifestation of
the disease, the body develops disorders, which are bearers
of dispositions. Such dispositions are naturally associated
with the entities’ existence, for example, the disposition of
the human body to get sick
In the therapeutic response phase, a sample is taken from the infected part of the body in order to perform laboratory tests. At this point, it is possible to establish a treatment plan so that the body may return to normality. The plan is the result of a diagnosis founded in the interpretative process of a clinical framework. The clinical framework is composed of symptom representation records as well as physical and laboratory exam results. The therapeutic response stage can be represented as follows: ABNORMAL BODY CONDITION => recognized as => SIGN AND SYMPTOM => used in => INTERPRETATIVE PROCESS.
The third phase of the proposed list of steps for KA, called the validation phase, uses wiki science tools for collaborative validation of candidate terms for an ontology. After the elicitation phase, according to the knowledge obtained, candidate terms are transferred to a wiki to then be validated by experts online.
The fourth stage of the proposed list of topics, called the refinement phase, uses a second template, also created using Protégé-Frames. The goal was to record information about how to integrate the different levels of granularity required to understand a disease and its manifestations. This integration involves obtaining the relations between parts of the body that a certain disease affects, the related genes and the related proteins.
Finally, the steps put forward so far are gathered together, thus creating the list of steps for KA.
Phase
(1) Extraction (2) Contact
(3) Validation
Task 1.1 build a corpus 1.2 codification 1.3 information retrieval 2.1 obtain knowledge 2.2 know the terminology 2.3 see adhoc organization 3.1 validate knowledge
Description Create a corpus from texts Automatically fragment texts Obtain terms through suffixes Hold interviews with experts Identify experts’ rationale Understand how experts sort concepts Obtain approval of terms acquired
Resources and people involved -Medical texts -K. engineer -Sketch Engine tool -K. engineer -Sketch Engine tool - K. engineer -Template Protégé and teachback; -K. engineer, experts -Matrix Techniques -K. engineer and expert -Sorting techniques -Experts -Wiki Page -Expert
Update data 3.2 updating after each vali
dation 4.1 integra- Characterize Rme(fe4inn)te- ttg4iir.oo2annncubwoleniattnrwhieteticoee-spn- rpCweroilotanhtteneoiedntchsgt,eedernatoectans,
level tologies Table 1: KA list of steps proposed 4
Wiki Page K. engineer -Template Protégé -K. engineer -Template Protégé- K. engineer One evident result is the methodological list of steps described in the previous section, which has been tested and improved over the course of the research (Table 1). In the codification activity (extraction phase), from the texts selected 369,741 tokens were automatically identified and related to parts-of-speech. Subsequently, in the information retrieval phase, 57 terms related to blood transfusion procedures were identified. Table 2 depicts the top-five terms from the set of 57 terms retrieved, which were used as a basis for starting interviews with experts:
Term Frequency apheresis 124 phlebotomy 32
cytometry 20 cordocentesis 16 plasmapheresis 15 Table 1: top-five terms retrieved and absolute frequency
The rationale applied in the elicitation phase made it possible to understand the major stages of the disease manifestation. Table 2 presents an example of blood disease analysis following this rationale for Bernard-Soulier Syndrome: Etiological process Disorder Disposition Pathological process Symptoms inheritance of a defect in the platelet membrane receptor that affects the hemostasis platelets with a glycoprotein Ib complex (GP Ib) abnormality, either quantitative (absence of GP Ib) or qualitative (mutation of GP1BA, GP1BB, GP9) Bernard-Soulier Syndrome (A, B or C) abnormal platelet adhesion to the extracellular matrix during the initial phase of plug formation bleeding, hematomas
Signs epxucrpesusriav,eepbilseteadxiinsg,,ggaisntrgoiivnatlesbtlieneadlibnlge,emdienngorrhagia, Table 2: KA reasoning applied to a blood disease
An example of a Protégé-Frames template related to Bernard-Soulier syndrome is depicted in Fig. 4.
Finally, it is worth mentioning that at the time this article was being written, the ontology developed in OWL had more than 300 classes and 50 properties, and practically all the methodological steps were up and running, providing data for different ontology parts. 5
In each stage of the KA process, as depict in Table 1, it is possible to identify issues to be discussed:
i) The extraction stage was undertaken mainly by a knowledge engineer using NLP tools applied to sources suggested by experts. As a means of producing a list of relevant terms in a domain, the extraction was useful in preventing the knowledge engineers from having to start interviews from scratch. In general, the terms selected were useful for describing the domain according to the opinions of experts.
ii) The contact stage is the heart of KA processes, since it is within this stage that experts share their knowledge. This stage was conducted as a cycle that involved interviews interspersed with attempts to understand the rationale used by experts to understand the phenomena in the domain. As part of this attempt, the knowledge engineer employed sorting and matrix techniques. Regarding the interview based on an ontological disease model, it is worth reporting that the results were very reasonable, insofar as the experts approved of the framework organized in the etiological process, course of disease and therapeutic response proposed by Scheuermann, Ceusters, & Smith (2009).
iii) The validation stage was conducted, in many cases, during the interviews, mainly in the beginning of the process when experts didn´t have experience with Wiki pages. In general, the validation confirmed the interviews and the teachback technique performed previously. It´s worth noticing that the difficulties in the validation stage did not occur among experts validating their own prior knowledge. Rather, the majority of cases of non-validity occurred when an expert evaluated the knowledge provided by another expert. However, the differences did not seem irreconcilable. In many cases, experts suggested referring to their own scientific publications to resolve outstanding issues.
iv) The refinement stage was conducted in the same way as the contact stage. Indeed, it was conducted as an interview merged with work to understand the rationale behind and organization of the experts’concepts. When analyzing the results, one can conclude that this stage provides useful insights into the building of ontologies in terms of interoperability. This is because the refinement stage is based on the premise of connection to top-level ontologies.
Observations made over the course of all these stages allowed us to identify problems that occur in the KA process for which solutions have been sought as the research has continued. These problems are the result of the influence of the following factors:
i) factors related to the expert profile, such as: training, experience and previous participation in similar projects, limitations in expertise;
ii) contextual factors, such as: cultural, geographical, political and financial issues, lack of access to information sources and deficiency in organizational structure; iii) factors related to the interaction between expert and knowledge engineer, such as: short-term outlook (KA is seen as “additional work”) and domain complexity; iv) factors that make recording results difficult, such as: non-approval by the expert of the results of the activity and constant advancement in the scientific field.
Concerning the proposed elicitation technique (section 3.2), which is based on Scheuermann, Ceusters & Smith (2009), one can argue that there is a methodological pitfall when using a formal disease model to acquire knowledge. It could be argued that relevant domain knowledge could be missed by doing so, because what would be acquired is something of a pre-conceived frame of meaning. However, we observed that some sort of structure was required to conduct the activity and save time, mainly considering the limited availability of the experts. According to our experience in this study of case, knowledge missed for this reason may be dealt with using complementary techniques. The interviewees were not constrained when talking and teachback techniques were employed to give them the chance to clear up misunderstandings and flaws. In addition, the ontological disease model was used only to organize the interview and to make notes, not in an attempt to formalize knowledge directly.
The NLP techniques applied aimed at collecting candidate terms for the ontology, instead of trying to populate it directly. In this sense, the use of those techniques was important to obtain a first list of candidate terms. Even though NLP is not considered a good source for ontological knowledge, it may be useful when dealing with a large volume of material. Another issue when using NLP was the size of our sample: in order to build a significant corpus, one should have at least 10 million words, which are not available to us.
This article has proposed a list of steps for KA, which are based on techniques found in the literature. The steps in the list has been tested, proving their viability. The work described includes a project in which research was conducted to identify the best practices for and difficulties in performing the KA activities with hematology experts within the scope of creating an ontology. The list of steps is a partial result that has been improved based on direct observation.
One conclusion we could draw from the overall experience is that KA is a very time-consuming and expensive process. This may explain why it is neglected in many cases. In future work, we intend to further clarify in which context each technique is most suitable. This could be done with assistance from experts, taking in account their time limitations. Regardless, in this case study, some techniques were chosen, as was mentioned in last column of Table 1. The list of topics has been successfuly applied in other related domains. It appears to be a systematized alternative for creating ontologies using a rational means of approaching experts.
This work is partially supported by Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), Governo do Estado de Minas Gerais, Brazil, Rua Raul Pompéia, nº101 - São Pedro, Belo Horizonte, MG, 30.330-080, Brazil. This work partially supported by CNPQ (Conselho Nacional de Pesquisa e Desenvolvimento)