Constructing a dental implant ontology for domain specific clustering and life span analysis

Constructing a dental implant ontology for domain specific clustering

and life span analysis

Charles V. Trappey

_{, Tong-Mei Wang}

_{, Sean Hoang}

_{, Amy J.C. Trappey}

a

Department of Management Science, National Chiao Tung University, Hsinchu, Taiwan

b

School of Dentistry, National Taiwan University, Taipei, Taiwan

c

Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan

a r t i c l e

i n f o

Article history:

Available online 15 May 2013 Keywords:

Clustering

Key phrase extraction Dental implant ontology Life span analysis

a b s t r a c t

Dental implant and prosthetics is a growing industry that follows the increasing aged populations that incur a higher percentage of tooth loss[1]. The dental implant sector is one of the most technical oriented fields in dentistry with many new techniques, devices, and materials being invented and put to clinical trials. Most innovations and technologies tend to be protected by intellectual property rights (IPRs) through patents. Thus, this research identifies the life spans of dental implant (DI) key technologies using patent analysis. Key patents and their frequently appearing phrases are analyzed for the construction of the DI ontology. Afterward, the life spans of DI technical clusters are defined based on the ontology schema. This research demonstrates the feasibility of using text mining and data mining techniques to extract key phrases from a set of DI patents with different patent classifications (e.g., UPC, IPC) as the basis for building a domain-specific ontology. The case study of ontological sub-clustering for dental implants demonstrates life span mapping of the technology and the ability to use clusters to represent stages of development and maturity in specific technology life cycles.

Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Dental implants are a unique technology with a very wide range of applications and a huge market of approximately seven billion US dollar in 2011[2]. Even though the technology for single tooth implants has been successfully used for over a decade, there are many conditions and uses of implants that are little understood and conditional. Many of the conditions of concern to dentists are long term survival and success rates that are influenced by many factors such as location of the implant, substitution (i.e., den-ture replacement), denden-ture anchoring, tissue health, bone density, age of recipient, prosthetic complications, implant and abutment types, as well as materials and post-operative medicines[3]. Thus, it is a medical technology field that requires the combination of continuous technical innovation and clinical trials for improving the implant survival rates and reliability as well as reducing failure rates[4].

Huang et al.[5]describe ontology as a model which contains the concepts and the relational links of concepts in a specific do-main that reflects the reality of the world. WordNet [6]defines ontology as a rigorous and exhaustive organization of some

knowl-edge domain that is usually hierarchical and contains all the rele-vant entities and their relations. Patent documents, and many technology oriented documents, contain domain specific terms which are not covered by common dictionaries. Therefore the advantage of ontology is that it defines a specific domain corpus to help analysts understand the meaning and relationships of the technical terms. Ontology can be seen as a hierarchical or network structure which abstracts domain concepts and relations ex-pressed in terms of domain terminologies using a standard knowl-edge representation language[7]to facilitate knowledge sharing. Since data is growing rapidly with the creation of new patents, ontology development processes based on patents help keep knowledge bases current.

High technology companies strive to orient and align R&D stra-tegic plans with emerging technologies. Patent documents are of-ten publicly available through government databases and provide information that forms the foundation for technology trend analy-sis. Patent analysis has been used to formulate economic indicators that relate technology development and economic growth[8]. Re-cently, it has become strategically important to use patent analysis as a means for high technology companies to evaluate technology trends[9]. Companies face technology information overload and need tools to analyze growth trends of complex innovations and the development of products with increasingly shorter product life cycles. The demand for the rapid creation of new technologies or designs is expected to accelerate as the world marketplace

1474-0346/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.aei.2013.04.003

⇑Corresponding author. Tel.: +886 223562508.

E-mail addresses:[email protected](C.V. Trappey),tongmeiwang@ ntu.edu.tw(T.-M. Wang),[email protected](S. Hoang),[email protected]. edu.tw(A.J.C. Trappey).

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Advanced Engineering Informatics

becomes more integrated and information access is facilitated by the Internet [10]. Granstrand [11] points out that, during the industrialized age, tangible assets such as land, factories, and nat-ural resources were the primary concern for management and growth. The knowledge-based age shows that intangible asset such as intellectual property, copyrights, and trademarks are now the focus of wealth creation and are used as leverage in the marketplace.

Clustering is a data analysis technique which classifies patterns of key phrases into categories based on the characteristics of rela-tionship[10]. The objective of clustering is to measure the similar-ity in data and categorize it into groups that maximize the similarity of specified variables within the same cluster. For this re-search, the objective is to create homogenous clusters with the same context of data from patent documents so that each patent document belonging to a cluster should be similar and express re-lated claims. Almeida et al.[12]notes that the presence of high connectivity among patent documents indicates a high association between the terms used in the documents. For patents, the chal-lenge is to characterize trends and development for a business or industry[13]. Some researchers[14]use patent data and clustering analysis to analyze technology trends and developments, to track the growth and frequency of patent applications, and to determine or forecast the growth or maturity of patents.

This research uses patent analysis techniques to cluster dental implant patents and analyze the life spans of the clusters. The case study inputs a set of dental implant patents acquired from the Uni-ted State Patent and Trademark Office (USPTO) to identify potential research opportunities for dental implants and to demonstrate the methodology’s utility for technology forecasting. An NTF-based key phrase analysis tool is used to create a domain specific ontology and a visualization schematic is created with Microsoft Visio. 2. Background and related research

This section discusses text mining, patent analysis, clustering, and technology life cycle analysis. The text mining section intro-duces the advantage of using computers to process data when the volume of data is growing too rapidly to transform into knowl-edge. The patent analysis section describes using patent docu-ments for the analysis of specific technologies, trend analysis, and the classification of technical terminology. The clustering sec-tion introduces the method of grouping patents into meaningful classifications. The technology life cycle analysis introduces the use of patent data to model technology life cycles.

2.1. Text mining

Text mining is a technique developed from data mining to ana-lyze unstructured text documents including patents [15]. Text mining contains techniques to label documents and link them to specific words that facilitate analysis and knowledge creation

[16]. A text document is often unstructured yet contains many types of information that can be ordered to represent facts and new knowledge[17]. Most information stored in a database is in the form of text documents. Text mining applies statistical algo-rithms for automatic knowledge discovery and pattern recognition

[18]. Text mining is a broad field that includes information retrie-val, text analysis, information extraction, clustering, categoriza-tion, visualizacategoriza-tion, machine learning, and data mining[19].

Recently, text mining has attracted researchers to use the tech-niques to study patents[9]. For example, text mining techniques have been used to create a patent maps for carbon nano-tubes

[20]. Other researchers applied text mining techniques to automat-ically create categorization features with greater efficiency than

human analysts[21]. One of the advantages of using text mining techniques is that large volumes of patent documents can be auto-matically sifted to extract useful information. Since patent docu-ments are lengthy and contain unique technical terms and document formats, automatic text mining better enables research-ers, engineers or managers to make decisions[15]. However, the extracted data should meet specific quality criteria to be under-stood by humans and to concisely represent the text concepts

[9]. Text mining techniques have been applied to text segmenta-tion, text summarizasegmenta-tion, feature selecsegmenta-tion, term associasegmenta-tion, clus-ter generation, topic identification, text mapping, technology trend analysis, and automatic patent classification.

Many researchers use specific indicators as determinants of pat-ent value. By using differpat-ent types of patpat-ent data sets including information about regional patent offices, particular technology sectors, or particular companies in a given country, new knowledge is acquired. Other researchers have studied patents and their im-pact on economic growth, technological innovation and develop-ment, and a country’s overall competiveness [8]. Since on average only about 1 out of 50 patents generates significant finan-cial returns, the identification and acquisition of high value patents with broad technical claims and high citation indexes can increase the financial value of a company. Companies with strong patent portfolios that conduct systematic and strategic patent planning activities are more successful than other companies especially in the fields of mechanical engineering and biotechnology[22]. Pat-ent analysis can be effectively used for companies to gain compet-itive advantages in the global marketplace[8]. Finally, patents are easily accessible (and often freely) available throughout the world through databases managed by governments that insure their accuracy.

2.2. Patent analysis

Patent documents contain rich and detailed information about research results that are expressed in complex technical and legal terms that is invaluable to the industry, legal practitioners, and policy makers[23]. The detailed content of patent documents, if carefully analyzed, can reveal areas of technology development, in-spire novel technical solutions, show technical relations, or stimu-late investment policies[21]. Tseng et al.[20]point out that patent analysis has become important at the government level for policy formation. Countries are investing resources to depict technical and commercial information that can be turned into knowledge

[23]. Patent documents are often lengthy and require time, effort and expertise to interpret. Tseng et al.[20]emphasize that patent analysts require expertise in information retrieval, knowledge of domain specific technologies, legal knowledge, and business intel-ligence to be effective.

Patent analysis can be divided into two levels; macro level re-search of national or industrial technology development and mi-cro-level research of specific technology development for claims analysis and forecasting [23]. Macro level analysis evaluates the economic effect of technological innovations, technological devel-opment and the competitiveness of countries[8]. Micro-level anal-ysis identifies the development of specific technologies, the advantages and disadvantages of competitors, aids the strategic planning of R&D activities, and identifies relations between compa-nies and technologies[24].

2.3. Ontology used to represent domain knowledge

Ontology structures concepts that reflect the reality of the world [5] and defines common terms in a domain of interest including the relationships among these terms. Ontology used for knowledge extraction via data mining has been applied to various

fields. For example, an ontology tree can be used for automatic pat-ent documpat-ent summarization which extracts key information into shortened abstracts describing the key concepts[10]. The goal is to use the ontology to create a knowledge base as input for a software program that improves the capturing of information and the crea-tion of knowledge. A biomedical gene ontology that helps research-ers accelerate knowledge acquisition, structure complex biological domains and relate data is now considered a significant competi-tive resource[25].

Ontology links the semantic data between concepts which makes it possible to perform pattern recognition, similarity analy-sis, and clustering of patent documents with respect to content

[26]. To create a domain specific ontology for patents requires key phrases that describe the concepts of patent documents[10]. A variety of methods have been proposed to create knowledge do-mains and one of the methods suggests a single ontology that inte-grates all knowledge domains. The potential drawback of this method is the lack of scalability which narrows the usefulness of the information. Researchers recommend creating a small or niche domain ontology and then integrating several into a top level ontology[27]. The same approach has been used to capture patent knowledge and enhance information retrieval.

2.4. Patent document clustering

Clustering facilitates key phrases into categories based on the characteristics of relationship[10]. The similarity in data is mea-sured to create the most suitable clusters. The clusters maximize the similarity of specified variables within and create homogenous content representing similar patent documents. There should be a high level of connectivity among these patent documents with a high association between technical terms[12]. The challenge of patent analysis is to characterize technical trends and develop-ment for a business or industry[13]. Researchers have developed clustering techniques for patent documents which analyze tech-nology trends and track the growth and frequency of patent appli-cations to forecast the life cycle of patents[14].

One way to mathematically define the similarity between two objects is based on the Euclidean distance[12]. Other researchers

[10]use an equation called the Manhattan distance. Patents may be used to cluster groups of technology based on their knowledge content rather their International Patent Codes (IPCs) or United States Patent Codes (UPCs). Patent technology clustering of this type uses a key phrase correlation matrix as input and applies the K-means algorithm to form the clusters[10]. A more complete discussion on applications of the K-means algorithm is provided by Han et al. [28]. The Root Mean Square Standard Deviation (RMSSTD) is the standard deviation of all variables and represents the minimum variance in the same cluster. Therefore, the value of RMSSTD should be as small as possible to gain optimal results. The R-Square (RS) value describes the maximum variance between dif-ferent clusters and the value of RS should be as large as possible since RS is the sum of squares between different clusters divided by the total sum of squares for the set of data. Thus, RMSSTD and RS are used to find the optimal number of clusters for a set of data. Patent document clustering uses the correlation matrix gener-ated from patent technology clustering as the K-means algorithm input [10]. Patent technology clustering splits patent documents into groups according to the similarity of key phrases in each pat-ent documpat-ent. The key phrases represpat-ent the dominant technology depicted in the patent documents. Finally, patent document clus-tering measures the internal relationship of the key points of the patent document and classifies patent documents based on the similarity of the technologies which enables patent analysts to identify the characteristics of the clusters.

2.5. Technology life cycle analysis

Life cycle analysis, as the name implies, assesses of the develop-ment of a product or service, from initial extraction of raw material to the final output or disposal of the product. When companies in-vest R&D capital on technologies, the inin-vestment decision often de-pends on the current life cycle stage of the technology[29]. Patent documents reveal the technical development and the life cycle stage of an industry[30]. A patent or patentability of a technology is also a precondition of commercial potential. In addition, patent documents contain data about the patent application date which relates to the life cycle of different products and the trends of com-mercialization and market development. The concept of technol-ogy life cycles is similar to product life cycles which include four stages including introduction, growth, maturity, and market de-cline. Regardless of the reference factor used to define the technol-ogy life cycle, patent based life cycles usually begin earlier than product development and commercial cycles[29].

The start of the patent life cycle introduction stage is often fraught with fundamental scientific problems that are not yet fully overcome. These technical problems have to be solved in order to advance and researchers often struggle to achieve radical innova-tions. At this stage, the patent applications are low but slowly increasing since there is a lot of uncertainty and few pioneer firms are willing to take the R&D risk [14]. During the early patent growth stage, the patent applications per applicant increase since the problems of the innovative technology are resolved. However, the cost may still be too high for customers’ acceptance or stan-dardization of the product. During the growth stage, when the fun-damental technical problems have been solved and the market uncertainty has been replaced with reliable products, many new competing products are likely to appear stemming from the earlier technological advances. Since the R&D risk has decreased, other inventors attempt to find competing alternative solutions and there is an increase of patent applications. The growing number of patent applications also decreases the patent application per applicant due to new competitors. The technology enters a mature stage when the number of patent applications is constant and all new features developed for this technology have been commercial-ized for the market. Thereafter, the technology enters the decline or saturation stage when new products and technologies are introduced.

Patent activity is an important indicator of the current technol-ogy life cycle[29]but verification requires a statistical survey of all patent applications of a given technological field[30]. In order to simplify analysis, the S-curve methodology can be used to study niche market developments such as pacemaker technology. All cumulative patent applications for a specific technology over a cer-tain period of time can be plotted as an S-curve and the different technology life cycle stages can be forecasted[30].

3. Methodology for dental implant patent ontology engineering This section describes the methodology and the research frame-work to achieve our case research objectives. This section describes the procedure from data selection to key phrase analysis, building the ontology and creating the domain-specific ontological cluster-ing of patents.

3.1. The framework for dental implant patent ontology engineering There are five steps for building the domain specific ontology based on patent data.Fig. 1 presents the procedural framework for systematic ontology building. This procedure is called Domain Specific Patent Ontology Engineering (DSPOE). The DSPOE is based

on domain specific patent data. The concepts, construction steps, and applications are described in the following sub-sections. The frame-work identifies the domain of interest and collects the domain spe-cific (DS) patent documents. Afterward, key phrases are extracted from the DS patent documents. The sub-domains are defined that identify the ontological sub-domain concepts and relationships. After building the initial DS ontology, it is verified and modified so that a case study can be conducted using a validated ontology. The Intellec-tual Property Defense-based Support System (IPDSS) software[31]

was used to automatically extract key phrases, build the key phrase matrix, and cluster patent technologies and documents.Fig. 1shows the steps of building and applying the DS ontology. The detailed pro-cedures are described in Section3.3.

3.2. Patent key phrase analysis

Most information stored in databases contains text documents. Extracting key phrases makes it possible to determine which ument is important and to identify the relation among several doc-uments. Key phrase extraction is useful for document or information retrieval, document clustering, summarization, and text mining[32]. There are many useful applications for key phrase extraction including highlighting key phrases in text, document classification, text compression, or constructing human readable text. Statistical approaches are used to measure the similarity of key phrases between textual documents. There are different ap-proaches for key phrase extraction and the most commonly used

are a lexical approach, natural language processing (NLP), or the term frequency approach. Some researchers divide key phrases extraction algorithms into two categories[33]that requires super-vised learning and are applied for single documents and unsuper-vised key phrase extraction using self learning which is also known as knowledge discovery (KDD).

Key phrases extraction has been applied in many different fields, although mainly for summarization purposes[34]. For example, re-search on the impact of automatic summarization systems based on key phrase extraction compared to human summarization showed that the key phrase frequency methodology generated sum-maries comparable with humans[35]. Other researchers use a hier-archy and semantic relationships to create a patent summarization system based on the specific domain of the patent document.

In this research, the key phrase analysis applies the normalized TF-IDF (NTF) methodology to extract key phrases. The TF-IDF method calculates weights for frequent key terms in a series of documents to determine relevance. Frequent key terms in one doc-ument cannot represent a domain but frequent key terms in a ser-ies of documents might represent the concept of the domain[36]. The formula for IDF[37]is defined as:

idfi¼ log2

n dfi

 

ð1Þ

where n is the total number of documents in the collection and dfiis the number of documents in the collection which contain term i. The variable idfirepresents the inverse document frequency (IDF)

of the term i. The equation describes idfias a value representing term i and if idfibecomes a significantly high value, then the term i represents a specific document.

The weighting of key phrases using TF-IDF in text documents where TF are weighted in IDF is expressed as:

wik¼ tfik idfi ð2Þ

where wikis defined as the weight of term i in document k of the collection, tfikis the number of terms i that occur in document k of the collection, and idfiis the inverse document frequency of term i. Therefore, the highest value of wikequals the most frequently occurring key phrases in a specific text document and are identified as the key phrases for any document k.

Furthermore, it is necessary to normalize TF-IDF because the TF-IDF method does not consider the difference between the num-ber of words in each document, therefore the frequency weights of key phrases are normalized by the number of words in each docu-ment. The normalized term frequency (NTF) is expressed as follows: NTF ¼ tfik Pn s¼1WNs n  1 WNk ð3Þ

where tfikis the number of term i that occurs in document k of the collection, WNkis the words number of document k, and r is the to-tal number of documents in the document collection.

The key phrase correlation matrix calculates the correlation of important key phrases (KPs) in each patent document which is used to create the logical link between concept and methodologies. The use of NTF-IDF to calculate the correlation between key phrases to create a key phrase correlation matrix using inner prod-uct of vectors is expressed as:

CorrelationðKPi;KPjÞ ¼ KPi KPj kKPikkKPjk ¼ Pn k¼1wik aw  wjk aw ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn k¼1w2ik aw2 Pn k¼1w2jk aw2 q ð4Þ

where KPi= aw(wi1, wi2, . . . , win) and aw ¼ Pn

s¼1WNs

nWNk is the average Word Number (WN). The algorithm consists of four stages. First, the algorithm transforms the patent document into a key phrases vector and analyzes the frequencies of key phrases. Second, it de-rives the key phrase vector by eliminating unnecessary phrases. Third, the correlation values between key phrases are calculated using Eq. (4). Fourth, the correlation coefficients are calculated based on the number of different key phrases occurring in each pat-ent documpat-ent.

The key phrase correlation matrix is used as an input for patent technology clustering. The key phrase correlation matrix repre-sents the technology in each patent document and thus reprerepre-sents the internal relationship among patent documents instead of clus-tering patents according to classification codes such as United States Patent Classification (UPC) or International Patent Classifica-tion (IPC).

For the key phrase and patent correlation matrix, the frequency (Fnm) of each key phrase (KP) appearing in each patent document is calculated as well as NTF, Rate (%) and NTFR. The Rate describes the percentage of KPnoccurring among Patent1to Patentn. NTFR is the product of NTF and Rate which expresses the relevance of KPn among the patent collection. The key phrase, KPn, is a representa-tive phrase in Patentn. If the frequency Fnm, is large enough across Patent1to Patentn, then KPnis a representative phrase of Patentn. The key phrase and patent correlation matrix are shown inTable 1.

3.3. The steps of DSPOE procedure

This section describes the DSPOE steps proposed in Fig. 1. In Section4, the DSPOE framework is applied for the dental implant patent analysis.

3.3.1. DSPOE 1: Define and collect domain specific range of patent documents

The first step of the proposed methodology is to identify the range of the ontological domain and focus on specific patents. The approach of using International Patent Classification (IPC) and United States Patent Classification (UPC) is applied to under-stand the scope of the ontology and define the sub-domains of the ontology. The patents are downloaded from United States Pat-ent and Trademark Office (USPTO) and domain specific patPat-ents are extracted based on the patent’s first listed UPC. These patents are uploaded to the IPDSS platform to automatically extract key phrases and provide statistical analysis of the document metadata.

3.3.2. DSPOE 2: Key phrase analysis and ontology construction After the domain range is defined, the next step is to establish a list of key phrases in the specified domain and define sub-domains based on the key phrase list. The key phrase analysis is based on the NTF-methodology which generates a list of the top 50 key phrases from the patent data collected.Table 2shows an example of the key phrase matrix. Patents are organized and grouped according to their UPC and WordNet is used to define keywords and the relationship between keywords in the key phrase list to organize and group sub-domain phrases. The final step is to clas-sify key phrases and UPCs in a key phrase matrix to generate an overview of phrases expressed in different UPCs. The goal is to form a preliminary matrix of domain specific knowledge and key-words for the ontology building process.

Based on the sub-domain list of key phrases from DSPOE 2, the domain specific ontology is engineered using Microsoft Visio as a building and visualization tool. The list of the top 50 sub-domain phrases are linked based on their concepts and relationships. Top-down classification starts from the upper phrases and then ex-tends to the lower phrases to establish an ontology tree with rela-tionships links.

Table 1

Key phrases and patent correlation matrix.

Patent1 Patent2 Patent3 . . . Patentn NTF Rate (%) NTFR

KP1 F1,1 F1,2 F1,3 . . . . KP2 F2,1 F2,2 . . . . KP3 F3,1 . . . . . . . . KPn . . . Fnm . . . . Table 2

Partial key phrase and patent correlation matrix.

Key phrase/PAT. no. Patent1 Patent2 Patent3 . . . Patentn NTFR

KP1: Implant 75 55 33 . . . 14,023 KP2: Dental 29 82 24 . . . 6422 KP3: Dental Implant 12 45 21 . . . 3808 KP4: Bone 10 0 67 . . . 3628 KP5: Screw 35 0 0 . . . 1872 KP6: Abutment 0 0 0 . . . 1056 KP7: Threaded 19 31 0 . . . 1575 KP8: Bore 0 37 0 . . . 932 KP9: Prosthesis 29 43 29 . . . 830 KP10: Cap 0 0 0 . . . 436 . . . . KPn . . . Fnm NTFRn

3.3.3. DSPOE 3: Validation and modification of the domain specific ontology

This research uses domain specific patent data for patent clus-tering to create sub-domains of the ontology. Key phrase analysis based on the NTF-methodology is applied to each sub-cluster to generate a sub-domain-specific list of the top 15 phrases. These phrases are used to validate the ontology and check that these phrases are included. If the phrases do not match, the ontology is modified and the process is repeated until the ontology created is strong enough to capture the domain specific knowledge of the sample of patent documents. In this step, domain experts are con-sulted to verify, validate, and modify the DS ontology schema.

3.3.4. DSPOE 4: A case study of ontological sub-domain clustering In order to test the domain specific ontology, the methodology requires the input of new domain specific patents that have not been utilized previously and 30 new domain specific patents are downloaded. The methodology applies key phrase analysis based on the NTF-methodology and generates a key phrase matrix with frequencies of each key phrase for each patent document. The list of phrases is used to classify patents into sub-domains for ontolog-ical sub-domain clustering. Only dental implant patents are used to create the ontological sub-domain clustering for life span analysis.

3.3.5. DSPOE 5: Life span analysis

After the ontological sub-domain clustering, the average appli-cation age is calculated for all patents in each cluster. The applica-tion date is the date when a patent applicaapplica-tion is officially handed into a government agency that issues patents. Each ontological sub-domain cluster is plotted against the average life span of the whole cluster. The age of the patent is calculated using the applica-tion date as a starting date and not the issuing date. The average age of each cluster is plotted against the ontological sub-domain clusters. Fig. 2 illustrates the analysis of potential emerging or declining clusters depending on average age. The size of each bub-ble represents the number of patents. The Y-axis plots the ontolog-ical sub-domain clusters and the X-axis is the average age of each cluster. Cluster 5 inFig. 2represents a young cluster of an ontolog-ical sub-domain which is a specific sub-domain of dental implants. The mapping method allows researchers to explore which sub-clusters have potential for further development or which sub-clus-ters may soon become outdated.

4. Ontology based clustering for dental implant patents In this section, dental implant patents are used as a case to dem-onstrate ontological sub-domain clustering based on patent data. The following discussion describes dental implants and the compo-nents. Various components of dental implants are the implant body, the cover screw (prevents bone access), the trans-mucosal abutment (links the implant body to the mouth), the healing abutment (tem-porarily placed on the implant to maintain the mucosal penetration), the healing caps (temporary covers for abutments), the crowns, bridges, and gold cylinder (to fit an abutment and form part of the prosthesis), and the laboratory analogue (a base metal replica of im-plant or abutment)[38,39]. The main components of a dental im-plant also include a screw that connects to a custom-made crown.

4.1. Dental patent document sampling

Patents under the same UPC may be entirely different in tech-nology. Therefore, a large sample including different UPCs is in-cluded when collecting data for building a domain specific ontology (Table 3). The IPDSS software completes the data prepro-cessing and key phrase extraction. Then the key phrase correlation measures are used to create a key phrase and patent correlation matrix. IPDSS uses K-means clustering as its algorithm for patent document clustering.

Fig. 2. Proposed life span analyses of dental implant patent clusters.

Table 3

List of dental implant patents in UPC classifications and dimensions. UPC Number of

patents

UPC definition

433/173 97 By fastening to jawbone: Subject matter wherein the denture is secured directly to the jawbone of the patient 433/174 24 By screw: Subject matter wherein the denture is secured to the jawbone by an elongated helically ribbed

member

433/175 4 Shape of removed tooth root: Subject matter wherein the lower portion of the denture that is secured to the jawbone is shaped to correspond to the configuration of the root of a natural tooth which had previously occupied the same position in the mouth

433/176 4 By blade: Subject matter wherein the denture is secured to the jawbone by a flat plate-like member extending from the bottom of an artificial tooth

433/172 13 Holding or positioning denture in mouth: Subject matter relating to locating or securing one or more artificial teeth in the mouth

433/201.1 5 Dental implant construction: Subject matter relating to either the structure or a process of making a dental prosthesis which is adapted to be fixed to the jawbone

433/169 7 Stress breaker: Subject matter wherein a denture includes means to redirect or absorb forces during mastication to protect the denture from damage

433/17 2 Having arch wire enclosing guide (e.g., buccal tube): Subject matter wherein the bracket includes an elongated member having a passage therein through which the arch wire is placed

4.2. Key phrase and patent correlation matrix

The key phrase and patent correlation matrix is derived from the dental implant patent data. The top 50 key phrases are chosen in chronological order from the highest NTF-value.Table 4shows a partial key phrase and patent correlation matrix with the top 28 key phrases and four different patents with frequency values for each key phrase in each patent. Key phrases extracted from these training patents match most of the dental implant main compo-nents [38,39]. For example, the abutment (support for crown) and the healing cap (covers abutments) are both listed in the ma-trix. The UPC 433/174 is described as fastening implants to the jawbone by screw and fromTable 4the key phrases listed are jaw-bone, threads, screw, and hole which conform to the UPC. Another example is UPC 433/172 – holding or positioning the denture in the mouth.Table 4lists key phrases including embodiment, bore, and implant fixture. Patents with the same classification code may not be expressed by the same set of key phrase which supports the rea-son to include patents from several different classifications to cre-ate an ontology that captures the main concepts of the domain. 4.2.1. Sub-domain definition of key phrases and the patent correlation matrix

The key phrases are sorted and grouped into four large groups.

Table 5shows 2 sub-domains for dental implant dimensions where

the key phrases are logically grouped to demonstrate their related concepts. For instance, dental, implant, prosthetic, and artificial are in one group, while screw, threads, and titanium are in another group. The grouping enables the creation of sub-domain clusters for the ontology schema.

4.3. Building the ontology

The proposed life span analysis of the dental implant patents uses the ontology as a variable for clustering dental implant pat-ents. The key phrases of each dimension are grouped as shown in

Table 5and represent the sub-domains of the ontology. The ontol-ogy in this research is an adapted version of Pritzek’s RFID ontolontol-ogy tree[40]. The ontology of dental implants, shown inFig. 3, only uses phrases from the key phrase matrix to link phrases to their concepts and relationships. Patent documents that contain detailed information about research results are written using complex tech-nical and legal terms, so it is preferred to extract data from patents to build a domain specific ontology. Building the ontology from health industry patents, particularly dental implants, has not yet been studied. Therefore, it is unique to analyze clusters using an ontology based on dental implant patents. The ontology inFig. 3

shows four preliminary sub-domains of the dental implant dimen-sions which are classified as geometry, implant fixture, biological, and dental components. The ontology is divided into sub-domains to separate and provide more specific concepts relevant to the den-tal implant domain.

The ontology is often built by domain experts and is subjective. In this research, part of building the ontology is subjective since the linking concepts and phrases are based on WordNet and the opinion of the researchers of this report. However, constructing a domain specific ontology in the dental implant area based on pat-ent data using objectively extract phrases by computer software creates an ontology that is more robust for the analysis of dental implant patents clusters.

4.3.1. Validation and modification of the ontology

One method to validate the ontology is to use key phrases de-fined by experts that are familiar with the domain. The experts may also compare illustrations in each patent document if the pat-ents include a figure of an implant body. This initial research only uses key phrases to group dental implant patents and the clusters are based on the similarity of technology. The key phrases ex-tracted for dental implants are shown inTable 6. Patent document clustering is applied and for each cluster, key phrase extraction is used to extract the top 15 key phrases based on NTF-values. If more phrases are extracted, it will only generate a larger and more com-plex ontology. Therefore, 50 phrases are used to build the ontology and these key phrases are used to validate and modify the ontology (Fig. 4).

The comparison of key phrases fromTable 6with the ontology inFig. 3shows that the ontology has to be modified since some key phrases from each cluster inTable 6do not match the sub-domains in the ontology fromFig. 3. The reason is that each sub-domain in-cludes repeated key phrases to describe the concept of that sub-do-main. InFig. 3, the sub-domain ‘‘screw’’ should also contain links to jaw bone, fixture, attachment, and crown which describe the con-cept and sub-domain of ‘‘screws’’ more accurately. Analyzing the phrases in cluster 1 (Table 6) shows that the terms are more likely to belong to the sub-domain of ‘‘screws’’ than other sub-domains. The ontology inFig. 4includes four sub-domains which are im-plant, implant assembly, screw device, and implant fixture. The validation of each dimension of the ontology was completed after repeated validation and modification by the domain expert.Fig. 3

depicts the initial ontology andFig. 4is the result modified to in-clude shared phrases that describe the core concept. However,

Table 4

Dental implant key phrase and patent correlation matrix (partial).

Key phrase/PAT. no. US6312260 US6039568 US5297963 US5362235 UPC 433/174 433/175 433/172 433/172 Implant 177.46 165.01 51.01 49.2 Dental 29.09 84.28 25.7 23.74 Bone 7.17 21.37 9.49 10.79 Screw 40.04 8.31 16.61 32.8 Abutment 0 26.12 34.4 71.64 Thread 42.15 14.25 24.91 26.33 Bore 14.75 0 32.03 26.76 Prosthetic 0 4.75 0 0 Cap 140.37 4.75 138.01 91.92 Healing 141.63 7.72 141.97 95.38 Root 0 3.56 12.65 6.9 Tissue 6.32 4.15 11.86 11.22 Healing cap 125.61 4.75 133.27 88.47 Fixture 0 0 45.87 44.88 Cavity 0 0 6.72 7.77 Hole 5.48 8.31 0 6.04 Jaw 5.06 0 8.7 9.93 Jawbone 18.13 20.18 0 0 Implant fixture 0 0 35.59 32.37 Table 5

Two sub-domains and representing key phrases (partial). Sub-domain Key phrase UPC 1 Implant 433/ 173 433/ 174 433/ 172 433/ 169 433/ 175 433/ 201.1 1 Dental 433/ 173 433/ 174 433/ 172 433/ 169 433/ 175 433/ 201.1 1 Artificial 433/ 173 433/ 174 433/ 172 433/ 169 433/ 201.1 1 Prosthetic 433/ 173 433/ 174 433/ 172 433/ 201.1 2 Screw 433/ 173 433/ 174 433/ 172 433/ 169 433/ 175 2 Threaded 433/ 173 433/ 174 433/ 172 433/ 169 433/ 175 433/ 201.1 2 Thread 433/ 173 433/ 174 433/ 172 433/ 169 2 Titanium 433/ 173 433/ 174 433/ 172 433/ 169 433/ 175 433/ 201.1

including too much detail and sharing too many phrases among technology sub-domains weakens the ontology and decreases the ability to build strong and unique clusters.

The implant sub-domain inFig. 4includes many shared phrases and includes few unique phrases that are distinct in the ontology whereas the implant assembly sub-domain includes several un-ique phrases which increase the cluster quality. The screw device sub-domain also has several unique phrases which build a stronger cluster compared with the implant sub-domain. The implant fix-ture sub-domain includes unique phrases. However, this sub-do-main includes distinctive phrases which are easily separated from the screw device or the implant assembly domain. For exam-ple, extending, anchoring, rotation, and angle may also be com-bined with embodiment, insertion and attachment.

4.4. Life span analysis of dental implant clusters

For this research, a case study of the life span analysis was based on the dental implant ontology. Key phrase analysis of 30 test patents created the key phrase and patent correlation matrix. For each individual patent, the frequency and list of key phrases are analyzed and compared with the sub-domains of the dental implant ontology (Fig. 4). Thereafter, each patent is assigned to the ontological sub-domain of implant, implant assembly, screw device, or implant fixture. This is called ontological sub-domain clustering.Table 7shows the results of the key phrase analysis of the test patents and the number of patents in each cluster. The analysis includes a column of ‘‘other patents’’ where the dental im-plant ontology failed (e.g., patents that include key phrases such as ‘‘dental implant package’’ in combination with ‘‘healing screw’’). The dental implant ontology requires minor modification to in-clude patents where there are potentially overlapping clusters.

Fig. 3. Preliminary dental implant ontology.

Table 6

Key phrases for validation of implant ontology.

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Key phrases Key phrases Key phrases Key phrases KP1: Implant KP1: Implant KP1: Implant KP1: Implant KP2: Dental KP2: Dental implant KP2: Dental KP2: Bone KP3: Dental implant KP3: Dental KP3: Dental implant KP3: Dental KP4: Tissue KP4: Bone KP4: Screw KP4: Dental

implant KP5: Bone KP5: Healing KP5: Bone KP5: Jaw KP6: Bone tissue KP6: Embodiment KP6: Prosthesis KP6: Tissue KP7: Crown-fixing KP7: Tissue KP7: Dental prosthesis KP7: Fixture KP8: Titanium (Ti)

KP8: Prosthetic KP8: Threads KP8: Implant fixture KP9: Device KP9: Screw KP9: Jaw KP9: Jaw bone KP10: Bristles KP10: Threads KP10:

Embodiment

KP10: Embodiment KP11: Powder KP11: Insertion KP11: Fixture KP11: Device KP12:

Attachments

KP12: Cavity KP12: Jawbone KP12: Crown KP13: Stabilizer KP13: Prosthesis KP13: Teeth KP13: Prosthesis KP14: Crown KP14: Teeth KP14: Cavity KP14: Teeth KP15: Teeth KP15: Jawbone KP15: Tissue KP15: Screw

However, the objective of this research is to focus on dental im-plants and specified UPCs which results in an initial ontology that captures the most relevant patents and excludes unnecessary patents.

InTable 7, there are new phrases which indicate that the ontol-ogy requires further improvements as a result of the initial training patent sample used for building the ontology. The 30 test patents in this case study did not restrict any UPC or IPC, and as long as the title included ‘‘dental implant’’ it was collected for analysis. Re-sults also depend on the age of the patent, for example, patent US5022860 inTable 8 is an expired patent that is 23 years old. Changes in terminology over the years for dental implants also have an impact on the analysis. All test patents use the first UPC that matches the training patents inTable 3and several relevant classification codes were included. Although the training patents have different UPCs, the dental implant ontology constructed is able to separate patents with the same UPC but different

technol-ogy sub-domains. This result supports previous research[14]that patents in the same classification codes may be entirely different in technology.

An example of the implant fixture sub-domain is shown in Ta-ble 8and the life span is calculated from the application date of current patents. The sub-domains implant assembly, screw device, and implant fixture include expired patents since the test patents samplings included random dental implant patents. The life span of the sub-domain implant assembly is about 14 years (excluding expired patents, about 11 years) and the life span for screw devices is about 13 years (excluding expired patents, about 12 years). Each ontological sub-domain patent cluster is plotted against their clus-ters average age including other patents and these are plotted without expired patents.Fig. 5shows that implant assembly and implant fixture are the two out of three sub-domains that are con-sidered to have potential. In this research the 30 test patents re-sulted in 7 patents being excluded since it was considered these

patents were inappropriate. According to the literature review, the sub-domain implant assembly shows potential due to a small number of patents at an early stage of development. The sub-do-main implant fixture can be seen as the dominant cluster in this

case and with an average age of about 11 years indicating potential opportunities for development and investment.Fig. 6shows a his-togram comparison of the sub-domain clusters.

The comparison inFig. 6shows that there are differences when mapping the average age of patents in clusters. For example, the sub-domain cluster screw device is a young cluster with an average age of about 12 years (without expired patents) and the field of im-plant fixtures has potential for further development. However, including the 23 year old expired patent affects the average age and makes the implant fixture cluster seem less attractive for R&D investments. From these test patents, the implant assembly sub-cluster is the youngest and is in the introductory stage with potential growth opportunities. The similarity of implant assembly and implant fixture might overlap in the ontology, hence, implant assembly focuses more on the surroundings like drilling holes or biological aspects including tissues or a device. Implant fixtures fo-cus more on the implant body attaching the implant crown (artifi-cial teeth) to the jawbone. In this sampling of test patents, it is clear that the implant assembly sub-cluster has great potential for development since it appears in the introductory stage and its ontological sub-domain includes several unique key phrases which support the strength of the dental implant ontology. However, the results require improvements of the ontology to capture several unique key phrases to better describe the sub-domain. The screw device and implant fixture sub-domains are also strong with sev-eral unique phrases. The implant sub-domain appears weak since it did not capture any patents but depends on the test patent sam-plings. Both screw device and implant assembly sub-domains re-sults demonstrate signs of growth. The small sampling of test patents makes it rather difficult to draw a conclusion whether the clusters are in the frontier or laggards in technology

develop-Table 7

Partial list of phrases for each ontological sub-domain of test patents. 7 Patents 4 Patents 5 Patents 14 Patents Other patents Implant

assembly

Screw device Implant fixture Implant Implant Implant Implant Dental Dental Dental Dental Dental implant Dental implant Dental implant Dental implant Screw Screw Screw Screw Fixture Fixture Fixture Fixture

Bone Bone Bone Bone

Cavity Implant fixture Implant fixture Implant fixture Healing Cavity Cavity Cavity Embodiment Healing Healing Healing Tissue Embodiment Embodiment Embodiment Prosthesis Tissue Tissue Tissue Healing screw Prosthesis Prosthesis Prosthesis Insertion Insertion Threads Threads Dental implant

package

Jawbone Extender Extender Package Dental

prosthesis

Healing screw Healing screw Dental prosthesis Crown Insertion Insertion Implant package Teeth Jawbone Jawbone Crown Device Dental

prosthesis

Barrel

Table 8

Implant fixture sub-domain patent information.

Patent No. Patent title UPC Filing date Age US5571016 Dental implant system 433/173;

433/169

January 24, 1995

16 US5752830 Removable dental implant 433/173;

433/169

June 20, 1996

15 US5863200 Angled dental implant 433/173 August 7,

1997

14 US5931674 Expanding dental implant 433/173 December

9, 1997 14 US6171106 Cover screw for dental

implant 433/173; 433/174 September 9, 1999 12 US6431867 Dental implant system 433/173 August 10,

2000

11 US6500003 Dental implant abutment 433/173 June 14,

2001

10 US7341453 Dental implant method

and apparatus

433/173 June 22, 2004

7 US7708559 Dental implant system 433/174 May 14,

2004 7 US6099312 Dental implant piece 433/174 July 15,

1999

12 US5951288 Self expanding dental

implant and method for using the same

433/173; 433/175; 433/201.1 July 3, 1998 13

US7112063 Dental implant system 433/174 August 11, 2004

7 US7396231 Flared implant extender

for endosseous dental implants 433/173; 433/172; 433/174 March 7, 2005 6 Average age (in years) 11.1

Expired patents in cluster

US5022860 Ultra-slim dental implant fixtures 433/174 December 13, 1988 23 Total average age 12.9 Other patents Implant assembly Screw device Implant fixture 0 2 4 6 8 10 12 14 16 18 20 8 11 12 11

Fig. 5. Life span of dental implant clusters without expired patents (years in reverse scale).

ment. One must also take into consideration that the number of test patents in each sub-cluster is different. A more objective anal-ysis requires a fair number of test patents and an almost equal number of test patents in each sub-cluster. Unquestionably, the ontology has to be taken into consideration since it defines the sub-clusters.

This research presents a new and valid means of clustering pat-ents and determining which clusters have the potential of growth or may be declining. Life span analysis of clusters is one of the many lifecycle analysis techniques and can be considered as an overview cluster analysis for mapping domain specific technolo-gies for further detailed analysis of the technology life cycle. Pat-ents have a lifetime of 20 years and depending on the clustering technique used, may reveal which cluster is moving towards growth or maturity. However, a mature cluster may enter the growth stage again if the patent activity increases for that cluster. Therefore, it requires the researcher must constantly update the clusters and create a timeline before concluding its stage of the life cycle and potential.

Life span analysis of domain-specific clusters includes patents within a limited time period which map the growth of each cluster and the change in average age. For example, by including patents from the years 1995 to 2005 and comparing with patents from 2000 to 2010, the historical development of technologies are ana-lyzed. Furthermore, it is possible to map historical technology bar-riers critical to overcome or avoid. As such, a cluster in the mature stage such as a screw device (Fig. 5) can return to the growth stage by increased patent activity in this domain.

5. Conclusion

This research studies the feasibility of using patent analysis techniques to build and verify a domain specific ontology using patent analysis techniques. A case study is used to cluster dental implant patents using the dental implant ontology and examine the life span of these clusters. The analysis supports the use of text mining techniques to extract key phrases to build a domain specific ontology. The validation methodology is reliable and feasible although it requires further research to gain increasingly signifi-cant results. The case study of the dental implant ontology demon-strates that the patent sample consisting of several patent classifications has similar technology even though classified in dif-ferent classes. The dental implant ontology also demonstrates a means to create specific sub-domains to sub-cluster dental implant patents with the same classification code including clustering pat-ents in other classifications even though not included as training patents. The construction of the dental implant ontology based on patent data provides a means of clustering patents based on their technology concepts. The ontology is flexible and new key phrases can be added, deleted and adapted for creating a more spe-cific domain ontology.

The life span analysis of patent clusters is based on the techno-logical life cycle and by mapping these clusters potential opportu-nities for future development can be identified. With a consistent clustering technique, the life span analysis of patent clusters pro-vides an overview of potential or future trends in technology development. However, a potential problem may possibly be that patents or technologies can overlap in clusters which will require developing a methodology to separate these overlaps. The ontology only captures relevant patents and excludes patents that did not match the sub-domain concepts. The results indicate that the den-tal implant ontology is robust and domain specific for denden-tal im-plants. Other domains may be included in the ontology for improvement since this research only focuses on the dental

im-plant body, abutment, crown, and fixture and excludes patents that focus on dental implant packages.

The life span analysis of ontological sub-domain clusters pro-vides an overview of the domain specific clusters and their current life span position to support R&D decision making. Each cluster can gain competitive advantage again through increased patent activ-ity which lowers the average age of each cluster. Consideration of expired patents in future research should provide a detailed analysis of sub-cluster development over time. The advantage is to provide a visualization of the development of technology barri-ers (historical and current) to determine if sub-clustbarri-ers gain com-petitive advantage through increased patent activity.

Acknowledgements

This research was partially supported by National Science Council research projects. The authors express their gratitude to Dr. Chun-Yi Wu for his assistance in running the case analysis using the IPDSS software tool (www.wheeljet.com.tw/edu/). References

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