Reblogging 2007: AI and cultural heritage workshop at AI*IA’07

From the “10 years of keetblog – reblogging: 2007”: a happy serendipity moment when I stumbled into the AI & Cultural heritage workshop, which had its presentations in Italian. Besides the nice realisation I actually could understand most of it, I learned a lot about applications of AI to something really useful for society, like the robot-guide in a botanical garden, retracing the silk route, virtual Rome in the time of the Romans, and more.

AI and cultural heritage workshop at AI*IA’07, originally posted on Sept 11, 2007. For more recent content on AI & cultural heritage, see e.g., the workshop’s programme of 2014 (also collocated with AI*IA).

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I’m reporting live from the Italian conference on artificial intelligence (AI*IA’07) in Rome (well, Villa Mondrogone in Frascati, with a view on Rome). My own paper on abstractions is rather distant from near-immediate applicability in daily life, so I’ll leave that be and instead write about an entertaining co-located workshop about applying AI technologies for the benefit of cultural heritage that, e.g., improve tourists’ experience and satisfaction when visiting the many historical sites, museums, and buildings that are all over Italy (and abroad).

I can remember well the handheld guide at the Alhambra back in 2001, which had a story by Mr. Irving at each point of interest, but there was only one long story and the same one for every visitor. Current research in AI & cultural heritage looks into solving issues how this can be personalized and be more interactive. Several directions are being investigated how this can be done. This ranges from the amount of information provided at each point of interest (e.g., for the art buff, casual American visitor who ‘does’ a city in a day or two, or narratives for children), to location-aware information display (the device will detect which point of interest you are closest to), to cataloguing and structuring the vast amount of archeological information, to the software monitoring of Oetzi the Iceman. The remainder of this blog post describes some of the many behind-the-scenes AI technologies that aim to give a tourist the desired amount of relevant information at the right time and right place (see the workshop website for the list of accepted papers). I’ll add more links later; any misunderstandings are mine (the workshop was held in Italian).

First something that relates somewhat to bioinformatics/ecoinformatics: the RoBotanic [1], which is a robot guide for botanical gardens – not intended to replace a human, but as an add-on that appeals in particular to young visitors and get them interested in botany and plant taxonomy. The technology is based on the successful ciceRobot that has been tested in the Archeological Museum Agrigento, but having to operate outside in a botanical garden (in Palermo), new issues have to be resolved, such as tuff powder, irregular surface, lighting, and leaves that interfere with the GPS system (for the robot to stop at plants of most interest). Currently, the RoBotanic provides one-way information, but in the near-future interaction will be built in so that visitors can ask questions as well (ciceRobot is already interactive). Both the RoBotanic and ciceRobot are customized off-the shelf robots.

Continuing with the artificial, there were three presentations about virtual reality. VR can be a valuable add-on to visualize lost or severely damaged property, timeline visualizations of rebuilding over old ruins (building a church over a mosque or vice versa was not uncommon), to prepare future restorations, and general reconstruction of the environment, all based on the real archeological information (not Hollywood fantasy and screenwriting). The first presentation [2] explained how the virtual reality tour of the Church of Santo Stefano in Bologna was made, using Creator, Vega, and many digital photos that served for the texture-feel in the VR tour. [3] provided technical details and software customization for VR & cultural heritage. On the other hand, the third presentation [4] was from a scientific point most interesting and too full of information to cover it all here. E. Bonini et al. investigated if, and if yes how, VR can give added-value. Current VR being insufficient for the cultural heritage domain, they look at how one can do an “expansion of reality” to give the user a “sense of space”. MUDing on the via Flaminia Antica in the virtual room in the National Museum in Rome should be possible soon (CNR-ITABC project started). Another issue came up during the concluded Appia Antica project for Roman era landscape VR: behaviour of, e.g., animals are now pre-coded and become boring to the user quickly. So, what these VR developers would like to see (i.e., future work) is to have technologies for autonomous agents integrated with VR software in order to make the ancient landscape & environment more lively: artificial life in the historical era one wishes, based on – and constrained by – scientific facts so as to be both useful for science and educational & entertaining for interested laymen.

A different strand of research is that of querying & reasoning, ontologies, planning and constraints.
Arbitrarily, I’ll start with the SIRENA project in Naples (the Spanish Quarter) [5], which aims to provide automatic generation of maintenance plans for historical residential buildings in order to make the current manual plans more efficient, cost effective, and maintain them just before a collapse. Given the UNI 8290 norms for technical descriptions of parts of buildings, they made an ontology, and used FLORA-2, Prolog, and PostgreSQL to compute the plans. Each element has its own interval for maintenance, but I didn’t see much of the partonomy, and don’t know how they deal with the temporal aspects. Another project [6] also has an ontology, in OWL-DL, but is not used for DL-reasoning reasoning yet. The overall system design, including use of Sesame, Jena, SPARQL can be read here and after server migration, their portal for the archeological e-Library will be back online. Another component is the webGIS for pre- and proto-historical sites in Italy, i.e., spatio-temporal stuff, and the hope is to get interesting inferences – novel information – from that (e.g., discover new connections between epochs). A basic online accessible version of webGIS is already running for the Silk Road.
A third different approach and usage of ontologies was presented in [7]. With the aim of digital archive interoperability in mind, D’Andrea et al. took the CIDOC-CRM common reference model for cultural heritage and enriched it with DOLCE D&S foundational ontology to better describe and subsequently analyse iconographic representations, from, in this particular work, scenes and reliefs from the meroitic time in Egypt.
With In.Tou.Sys for intelligent tourist systems [8] we move to almost-industry-grade tools to enhance visitor experience. They developed software for PDAs one takes around in a city, which then through GPS can provide contextualized information to the tourist, such as the building you’re walking by, or give suggestions for the best places to visit based on your preferences (e.g., only baroque era, or churches, or etc). The latter uses a genetic algorithm to compute the preference list, the former a mix of RDBMS on the server-side, OODBMS on the client (PDA) side, and F-Logic for the knowledge representation. They’re now working on the “admire” system, which has a time component built in to keep track of what the tourist has visited before so that the PDA-guide can provide comparative information. Also for city-wide scale and guiding visitors is the STAR project [9], bit different from the previous, it combines the usual tourist information and services – represented in a taxonomy, partonomy, and a set of constraints – with problem solving and a recommender system to make an individualized agenda for each tourist; so you won’t stand in front of a closed museum, be alerted of a festival etc. A different PDA-guide system was developed in the PEACH project for group visits in a museum. It provides limited personalized information, canned Q & A, and visitors can send messages to their friend and tag points of interest that are of particular interest.

Utterly different from the previous, but probably of interest to the linguistically-oriented reader is philology & digital documents. Or: how to deal with representing multiple versions of a document. Poets and authors write and rewrite, brush up, strike through etc. and it is the philologist’s task to figure out what constitutes a draft version. Representing the temporality and change of documents (words, order of words, notes about a sentence) is another problem, which [10] attempts to solve by representing it as a PERT/CPM graph structure augmented with labeling of edges, the precise definition of a ‘variant graph’, and a method of compactly storing it (ultimately stored in XML). The test case as with a poem from Valerio Magrelli.

The proceedings will be put online soon (I presume), is also available on CD (contact the WS organizer Luciana Bordoni), and probably several of the articles are online on the author’s homepages.

[1] A. Chella, I. Macaluso, D. Peri, L. Riano. RoBotanic: a Robot Guide for Botanical Gardens. Early Steps.
[2] G. Adorni. 3D Virtual Reality and the Cultural Heritage.
[3] M.C.Baracca, E.Loreti, S. Migliori, S. Pierattini. Customizing Tools for Virtual Reality Applications in the Cultural Heritage Field.
[4] E. Bonini, P. Pierucci, E. Pietroni. Towards Digital Ecosystems for the Transmission and Communication of Cultural Heritage: an Epistemological Approach to Artificial Life.
[5] A. Calabrese, B. Como, B. Discepolo, L. Ganguzza , L. Licenziato, F. Mele, M. Nicolella, B. Stangherling, A. Sorgente, R Spizzuoco. Automatic Generation of Maintenance Plans for Historical Residential Buildings.
[6] A.Bonomi, G. Mantegari, G.Vizzari. Semantic Querying for an Archaeological E-library.
[7] A. D’Andrea, G. Ferrandino, A. Gangemi. Shared Iconographical Representations with Ontological Models.
[8] L. Bordoni, A. Gisolfi, A. Trezza. INTOUSYS: a Prototype Personalized Tourism System.
[9] D. Magro. Integrated Promotion of Cultural Heritage Resources.
[10] D. Schmidt, D. Fiormonte. Multi-Version Documents: a Digitisation Solution for Textual Cultural Heritage Artefacts

The ontology-driven unifying metamodel of UML class diagrams, ER, EER, ORM, and ORM2

Metamodelling of conceptual data modelling languages is nothing new, and one may wonder why one would need yet another one. But you do, if you want to develop complex systems or integrate various legacy sources (which South Africa is going to invest more money in) and automate at least some parts of it. For instance: you want to link up the business rules modelled in ORM, the EER diagram of the database, and the UML class diagram that was developed for the application layer. Are the, say, Student entity types across the models really the same kind of thing? And UML’s attribute StudentID vs. the one in the EER diagram? Or EER’s EmployeesDependent weak entity type with the ORM business rule that states that “each dependent of an employee is identified by EmployeeID an the Dependent’s Name?

Ascertaining the correctness of such inter-model assertions in different languages does not require a comparison and contrast of their differences, but a way to harmonise or unify them. Some such models already exist, but they take subsets of the languages, whereas all those features do appear in actual models [1] (described here informally). Our metamodel, in contrast, aims to capture all constructs of the aforementioned languages and the constraints that hold between them, and generalize in an ontology-driven way so that the integrated metamodel subsumes the structural, static elements of them (i.e., the integrated metamodel has as them as fragments). Besides some updates to the earlier metamodel fragment presented in [2,3], the current version [4,5] also includes the metamodel fragment of their constraints (though omits temporal aspects and derived constraints). The metamodel and its explanation can be found in the paper in An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2 [4] that I co-authored with Pablo Fillottrani, and which was recently accepted in Data & Knowledge Engineering.

Methodologically, the unifying metamodel presented in An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2 [4], is ontological rather than formal (cf. all other known works). On that ‘ontology-driven approach’, here is meant the use of insights from Ontology (philosophy) and ontologies (in computing) to enhance the quality of a conceptual data model and obtain that ‘glue stuff’ to unify the metamodels of the languages. The DKE paper describes all that, such as: on the nature of the UML association/ORM fact type (different wording, same ontological commitment), attributes with and without data types, the plethora of identification constraints (weak entity types, reference modes, etc.), where can one reuse an ‘attribute’ if at all, and more. The main benefit of this approach is being able to cope with the larger amount of elements that are present in those languages, and it shows that, in the details, the overlap in features across the languages is rather small: 4 among the set of 23 types of relationship, role, and entity type are essentially the same across the languages (see figure below), and 6 of the 49 types of constraints. The metamodel is stable for the modelling languages covered. It is represented in UML for ease of communication, but, as mentioned earlier, it also has been formalised in the meantime [5].

Types of elements in the languages; black-shaded: entity is present in all three language families (UML, EER, ORM); darg grey: on two of the three; light grey: in one; while-filled: in none, but we added it to glue things together. (Source: [6])

Types of elements in the languages; black-shaded: entity is present in all three language families (UML, EER, ORM); dark grey: on two of the three; light grey: in one; while-filled: in none, but we added the more general entities to ‘glue’ things together. (Source: [4])

Metamodel fragment with some constraints among some of the entities. (Source [4])

Metamodel fragment with some constraints among some of the entities. (Source [4])

The DKE paper also puts it in a broader context with examples, model analyses using the harmonised terminology, and a use case scenario that demonstrates the usefulness of the metamodel for inter-model assertions.

While the 24-page paper is rather comprehensive, research results wouldn’t live up to it if it didn’t uncover new questions. Some of them have been, and are being, answered in the meantime, such as its use for classifying models and comparing their characteristics [1,6] (blogged about here and here) and a rule-based approach to validating inter-model assertions [7] (informally here). Although the 3-year funded project on the Ontology-driven unification of conceptual data modelling languages—which surely contributed to realising this paper—just finished officially, we’re not done yet, or: more is in the pipeline. To be continued…

 

References

[1] Keet, C.M., Fillottrani, P.R. An analysis and characterisation of publicly available conceptual models. 34th International Conference on Conceptual Modeling (ER’15). Springer LNCS. 19-22 Oct, Stockholm, Sweden. (in press)

[2] Keet, C.M., Fillottrani, P.R. Toward an ontology-driven unifying metamodel for UML Class Diagrams, EER, and ORM2. 32nd International Conference on Conceptual Modeling (ER’13). W. Ng, V.C. Storey, and J. Trujillo (Eds.). Springer LNCS 8217, 313-326. 11-13 November, 2013, Hong Kong.

[3] Keet, C.M., Fillottrani, P.R. Structural entities of an ontology-driven unifying metamodel for UML, EER, and ORM2. 3rd International Conference on Model & Data Engineering (MEDI’13). A. Cuzzocrea and S. Maabout (Eds.) September 25-27, 2013, Amantea, Calabria, Italy. Springer LNCS 8216, 188-199.

[4] Keet, C.M., Fillottrani, P.R. An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2. Data & Knowledge Engineering. 2015. DOI: 10.1016/j.datak.2015.07.004. (in press)

[5] Fillottrani, P.R., Keet, C.M. KF metamodel Formalization. Technical Report, Arxiv.org http://arxiv.org/abs/1412.6545. Dec 19, 2014. 26p.

[6] Fillottrani, P.R., Keet, C.M. Evidence-based Languages for Conceptual Data Modelling Profiles. 19th Conference on Advances in Databases and Information Systems (ADBIS’15). Springer LNCS. Poitiers, France, Sept 8-11, 2015. (in press)

[7] Fillottrani, P.R., Keet, C.M. Conceptual Model Interoperability: a Metamodel-driven Approach. 8th International Web Rule Symposium (RuleML’14), A. Bikakis et al. (Eds.). Springer LNCS 8620, 52-66. August 18-20, 2014, Prague, Czech Republic.

Quasi wordles of isiZulu online newspaper articles from this weekend

Every now and then, I get side-tracked from what I was (supposed to be) doing. This time, it was a result of the combination of preparing ICPC training problems, preparing for a statistics tutorial for the postgraduate research methods, and a conversation from last week on an isiZulu corpus with Langa Khumalo from UKZN’s ULPDO (and my co-author on several papers on isiZulu CNLs). To make a long story short, I ended up sourcing some online news articles in isiZulu and writing a little python script to count the words and top-k words of the news articles to get a feel of what the most prevalent topics of the articles were.

 

Materials and data

10 Isolezwe, listed on the front page on August 8, 2015 (articles were from Aug 6 and 7—no updates in the long weekend)

10 News24 in isiZulu articles, listed on the front page on August 8, 2015 (articles were from Aug 8)

10 News24 in isiZulu articles, listed on the front page on August 9, 2015 (articles were from Aug 9, a Sunday, and Women’s Day in South Africa)

Simple basicCorpusStats.py that one can make already just by going through the first part of ThinkPython (in case you’re unfamiliar with python).

Note: ilanga doesn’t have articles online, and therefore was not included.

Note 2: for copyright issues, I probably cannot share the txt files online, but in case you’re interested, just ask me and I’ll email them.

 

Some general stats

Isolezwe had, on average, 265 words/article, whereas news24 had about half of that (110 and 134 on Saturday and Sunday, respectively). The top-20 of each is listed at the end of this post (the raw results of News24 had “–” removed [bug], as well as udaba and olunye [standard-text noise from the articles]).

Comparing them on the August 8 offering, Isolezwe had people saying this that and the other (ukuthi ‘saying/to say’ had the highest frequency of 60) and then the police (amaphoyisa, n=27), whereas News24 had amaphoyisa 27 times as most frequent word, then abasolwa (‘suspects’) 11 times that doesn’t even appear in Isolezwe’s top-20 most frequent words (though the stem –solwa appears 9 times). The police is problematic in South Africa—they commit crimes and other dubious behaviour under investigation (e.g., Marikana)—and more get killed than in may other countries (another one last week), and crime happens. But not on a public holiday, apparently: News24 had only one –phoyisa on Aug 9.

While I hoped to find a high incidence of women, for it being Women’s Day on August 9, none of –fazi appeared in the News24 mini-corpus of 1353 words of the 10 front page articles; instead, there was a lot of saying this that and the other (ukuthi had the highest frequency of 37), and little on suspects or blaming (-solwa n=3).

 

On that quasi wordle

While ukuthi is the infinitive, there are a gazillion conjugations and things agglutinated to it that is barely clear to the linguists on how it all works, so I did not analyse that further. Amaphoyisa, on the other hand, as a noun (plural of ‘police’), has fewer variations. In the Isolezwe mini-corpus, –phoyis– (the root of ‘police’) appeared 47 times, including variants like lwamaphoyisa, ngamaphoyisa, yiphoyisa, i.e., substantially more than the 27 amaphoyisa. If I were to create a wordle, they’d be missed unless one uses some stemmer, which doesn’t happen to be available[1] and I didn’t write one (just regex in the txt). By the same token, News24’s mention of the police on August 8 goes up to 28 with –phoyisa, and as close second the blaming and suspects (-solwa, n=27).

The lack of a stemmer also means missing out on all sorts of variations on imali (‘money’, n=11) in the isolezwe articles, whereas its stem –mali pops up 29 times, due to, among others, kwemali (n=5), mali (n=3), yimali (y- functioning as copulative in that sentence, n=1), ngezimali (n=1) and others. Likewise on person/people (-ntu) for which n=17 that are distributed among abantu (plural) umuntu (singular), nabantu (‘and people’), among others.

Last, the second most frequently used word in News24 on August 9 was njengoba (‘as’, ‘whereas’, ‘since’), primarily due to the first article on the sports results of the matches played.

So, with all that background knowledge, Isolezwe’s wordle would be, in descending order (and in English for the readers of this blog): say, police, money, people. News24 on August 8: police, suspect/blame, say (two variations, n=9 each). News24 on August 9: say, as/since (and then some other adverbs).

 

In closing

This dabbling resulted in more problems and questions being raised than answered. But, for now, it’s at least still a bit of a peek into the kitchen of news in a language that I don’t master as well as I want to and should. It wasn’t useful either for the ICPC problem setting or the stats tutorial, nor is a 5123-word corpus of any use, but it was fun with python at least and satisfying at last a little of my curiosity, and perhaps it spurs someone to do all this properly/more systematically and on a grander scale. For the isiZulu speakers: it’s surely still up to you to read whichever news outlet you prefer reading.

 

 

References

[1] Pretorius, L., Bosch, S.E. (2010). Finite-state morphology of the Nguni language cluster: modelling and implementation Issues. In A. Yli-Jyrä, Kornai, A., Sakarovitch, J. & Watson, B. (Eds.), Finite-State Methods and Natural Language Processing 8th International Workshop, FSMNLP 2009. Lecture Notes in Computer Science, Vol. 6062, pp. 123–130

[2] Spiegler, S., van der Spuy, A., and Flach, P. A. (2010). Ukwabelana – an opensource morphological zulu corpus. In Proceedings of the 23rd International Conference on Computational Linguistics (COLING’10), pages 1020-1028. Association for Computational Linguistics. Beijing

 

 

Top-20 words Isolezwe on Aug8 Top-20 words News24 on Aug8 Top-20 words News24 on Aug9
ukuthi 60 amaphoyisa 19 ukuthi 37
amaphoyisa 27 abasolwa 11 njengoba 13
uthe 17 uthe 9 ngemuva 12
ngoba 17 ukuthi 9 ngesikhathi 8
lokhu 16 ubudala 9 lo 8
kuthiwa 16 njengoba 9 uthe 7
kusho 16 kusho 9 uma 7
kodwa 12 lo 8 futhi 7
imali 11 oneminyaka 7 uzakwe 6
uma 10 ngokuthakatha 7 usnethemba 6
ngesikhathi 10 ngokusho 7 kodwa 6
yakhe 9 omphakathi 6 johannesburg 6
ukuba 9 okhulumela 6 yakhe 5
njengoba 9 kanti 6 united 5
nje 9 endaweni 6 ukuba 5
lo 9 yohlobo 5 ukomphela 5
khona 9 ngesikhathi 5 ufaku 5
abantu 9 ngemuva 5 ubudala 5
umphakathi 8 le 5 rhythms 5
umnuz 8 imoto 5 ngokubika 5

 

 

 

[1] There is some material on that (among others, [1,2]), though, but it’s mostly theoretical or very proof of concept, rather than the easy reuse of tools like for English, and the example rule in [1] isn’t right (it’s umfana, not umufana; the longer prefix with the extra –u– is used when the stem is one syllable, like –ntu -> umuntu).

An orchestration of ontologies for linguistic knowledge

Starting from multilingual knowledge representation in ontologies and an eye on linguistic linked data and controlled natural languages, we had developed a basic ontology for the Bantu noun class system [1] to link with the lemon model [2]. The noun class system is alike gender in, e.g., German and Italian, but then a bit different. It is based on semantics of the nouns and each Bantu language has some 12-23 noun classes. For instance, noun classes 1 and 2 are for singular and plural humans, 9 and 10 for animals (singular and plural, respectively), 11 for inanimates and long thin objects (e.g., a telephone cable), and class 14 has abstract nouns (e.g., beauty). Each class has its own augment or augment+prefix to be added to the stem. None of the other linguistic resources, such as ISOcat or the GOLD ontology, dealt with them, so, lemon did not either, but we needed it. The first version of the ontology we introduced in [1] had its limitations, but it mostly did its job. Mostly, but not fully.

Lemon needs that morphology module and then some for the rules. The ontology did not fully satisfy Bantu languages other than Chichewa and isiZulu. With the knowledge of the latter only, it was more alike a merged conceptual data model, for it was tailored to the two specific languages. Also, it wasn’t aligned to other models or ontologies, thus hampering interoperability and reuse. We didn’t have any competency questions or cool inferences either, because our scope then was just to annotate the names of the classes in an ontology. Hence, it was time for an improvement.

Among others, we don’t want just to annotate, but, given that Bantu languages are underresourced, see what we can add to derive implicit information, which could help with tagging terms. For instance

  • if you know abantu is a plural and in noun class 2 and umuntu is the singular of it, then umuntu is in noun class 1, or
  • when it is declared that inja is in noun class 9, then so is its stem -ja (or vv), or
  • language specific, which singular (plural) noun class goes with which plural (singular) noun class: while the majority neatly has a pair of successive odd and even numbers (1-2, 3-4, 5-6 etc), this is not always the case; e.g., in isiZulu, noun class 11 does not have noun class 12 as plural, but noun class 10 (which has its own augment and prefix).

Then, besides the interoperability and reuse requirements, we’d needed to distinguish between language-specific axioms and those that hold across the language family. To solve all that, we developed a framework, reusing the pyramid structure idea from BioTop [3] and the so-called “double articulation principle” of DOGMA [4], where the language-specific axioms are at the level of DOGMA’s conceptual model, for they add specific constraints.

To make a long story short, the framework/orchestration applied to the linguistic knowledge of Bantu noun classes in general, and specific to some language, looks as follows:

framework applied to some linguistics ontologies (source: [5])

framework applied to some linguistics ontologies (source: [5])

More details are described in the recently accepted paper “An orchestration framework for linguistic task ontologies” [5], to be presented as the 9th Metadata and Semantics Research Conference (MTSR’15), to be held from 9 to 11 September in Manchester, UK. My co-author Catherine Chavula will be attending MTSR’15 and present our paper, hoping/assuming that all those last-minute things—like visa and money actually being transferred to buy that plane ticket—will be sorted this month. (Odd ‘checks and balances’ that make life harder and more expensive for people outside of a visa-free zone and tied to a funding benefactor is a topic for some other time.).

The set of ontologies (in OWL) is available in NCS1.zip from my ontologies directory. It contains the goldModule—a module extracted from the GOLD ontology for general linguistics knowledge and that is aligned to the foundational ontology SUMO—the NCS ontology, and three languages-specific axiomatizations for the noun classes, being Chichewa, isiXhosa, and isiZulu (more TBA). The same approach can be used for other linguistic features in other language groups or families; e.g., instead of the NCS, one could have knowledge represented about conjugation in the Romance languages (Italian, Spanish etc.), and then the more precise axiomatization (conceptual data model, if you will) for constraints unique to each language.

 

p.s.: Bantu languages is the term used in linguistics, so that’s why it’s used here. Elsewhere, they are also called African languages. They’re not synonymous, however, as the latter includes also other, non-Bantu, languages, as it can designate any language spoken in Africa that may have a wholly different grammar, hence, the difference linguists make to avoid misinterpretation.

 

References

[1] Chavula, C., Keet, C.M. Is Lemon Sufficient for Building Multilingual Ontologies for Bantu Languages? 11th OWL: Experiences and Directions Workshop (OWLED’14). Keet, C.M., Tamma, V. (Eds.). Riva del Garda, Italy, Oct 17-18, 2014. CEUR-WS vol. 1265, 61-72.

[2] McCrae, J., Aguado-de Cea, G., Buitelaar, P., Cimiano, P., Declerck, T., Gómez-Pérez, A., Gracia, J., Hollink, L., Montiel-Ponsoda, E., Spohr, D., Wunner, T.: Interchanging lexical resources on the Semantic Web. Language Resources & Evaluation, 2012, 46(4), 701-719

[3] Beißwanger, E., Schulz, S., Stenzhorn, H., Hahn, U.: Biotop: An upper domain ontology for the life sciences: A description of its current structure, contents and interfaces to obo ontologies. Applied Ontology, 2008, 3(4), 205-212

[4] Jarrar, M., Meersman, R.: Ontology Engineering The DOGMA Approach. In: Advances in Web Semantics I, LNCS, vol. 4891, pp. 7-34. Springer (2009)

[5] Chavula, C., Keet, C.M. An Orchestration Framework for Linguistic Task Ontologies. 9th Metadata and Semantics Research Conference (MTSR’15), Springer CCIS. 9-11 September, 2015, Manchester, UK. (in print)