University of Arkansas – CSCE DepartmentCSCE 4613 Artificial Intelligence – Final Report – Fall 2009

Product Sustainability Ontology Project /Virtual World Ontology Project

Keith Eddy, Matt Hardy, Aaron McGinn

Abstract

In this day and age, making products in a sustainable manner is more important than ever. Data which could assist retailers in this goal exists but is contained within different ontologies. This project consists of first steps toward establishing a method to easily map between these various sources of knowledge so that the retailer can more easily make use of it, then present this information to a consumer within a virtual world such as Second Life. An “ontology engine” will help facilitate this mapping by making use of existing technologies such as Wordnet to automate the mapping the process.

1. Introduction

1.1 Problem

It is desirable for companies to produce their products in a sustainable manner. To this end, various databases have developed to classify products and present sustainability information, such as carbon dioxide released, about these products. However manufacturers and retailers classify products through either UNSPC or GPC which do not keep track of sustainability information. The ontologies that contain the sustainability information do not mirror either of these classification schemes. Therefore, a mapping between the two must be performed. No uniform ontology exists for representing product data and industry categories nor is there a way to augment such an ontology with aspects such as sustainability. Nor is such a resulting “lower level” ontology tied to concept level ontologies.

Virtual worlds do not support an ontology layer of architecture that can be extended to add knowledge to the virtual world. So an object can be labeled “chair” but no additional knowledge can be associated or derived from this label. Expanding on this elementary labeling capability would allow virtual worlds to be more closely linked to the real world. Specifically, Second Life does not support an ontology layer of architecture that can be extended to add real life knowledge to the virtual world. In order to map an ontology to Second Life, we have to be able to somehow display the information. Unfortunately, Second Life lacks direct support for displaying dynamic text through an HUD (Heads up display).

Similarly, reality is not augmented with a knowledge base so we cannot explicitly associate knowledge with places and things. It would be desirable to augment reality with an overlay of

additional knowledge. The ontology base in both the real and virtual world can be closely related.

Because there are a limited number of different objects in Second Life that would need a linked ontology structure, a more limited ontology could be created based only upon the labels of objects within. The main problem with creating an ontology for the objects in a virtual world is that the labels added to an object are solely based on the owner. For example, a user might create an object that looks and interacts like a door, but could be labeled “chair.” Therefore the ontology would be incorrect due to the false data

1.2 Objective

The objective of this project is to begin developing a way to create and efficiently maintain mappings between various retail product ontologies and the sustainability ontologies.

As part of this overall goal, there is a perceived need for semantic matching capabilities in order to assist in automating mapping between ontologies. A logical place to start with this in mind is the lexical ontology, Wordnet. Therefore, research must done into Wordnet’s capabilities and operating procedures.

Another objective was to create a working display system using an HUD and link it with an external database/ontology, so when an object in the virtual world was queried, the HUD would retrieve and display the corresponding information or "knowledge" about that object. This information, or at least an object label or name, would have to have been previously entered by someone in Second Life to correspond to the specific object. The ontology would then find the label and return all of the necessary information associated with it, which would be displayed through the HUD.

Goals

Build a lower ontology (below the level of Wordnet) to account for SKU level product data and product categories and manufacturing processes. Augment above ontology with sustainability attributes and methods to compute a sustainability index. Understand rules of aggregation and inference involved (and record as reasons for decisions).

Attach product ontology to virtual world Second Life as a way to rapidly populate SL with an ontology layer.

Demonstrate large-scale augmented reality where the product ontology is available in the real world as it is in the virtual world simulation. This goal is out of scope for present except we can design it on paper with RFID and cell phones plus our ontology..

Link an external ontology or database to Second Life that can communicate with a HUD and can store information about the objects.

Create an index of all objects within Second Life along with the frequencies of the object labels and descriptions.

Sub-goals

Represent ontology information (e.g., RDF/OWL) and edit using an ontology editor (Protégé)

Represent Wordnet ontology in Protégé

Attach product ontology to bottom of Wordnet ontology to provide product/SKU level ontology

Attach sustainability attributes and information into product level ontology (leaving open the idea to add other aspects later, like cost, aesthetics,

Combine NAICS with …

Use Second Life search spiders to retrieve named objects. Analyze these and attach to the Wordnet ontology semi-automatically and/or manually.

Analyze SL ontology data by frequency (many chairs, fewer X-ray machines)

Analyze SL ontology data by semantic fields (hospital beds are near X-ray machines in a healthcare facility)

Request ontology information from an object or avatar in Second Life and display or query it

Create an HUD that can display dynamic text within Second Life

1.3 Context

This project takes place within the long-term Everything is Alive (EiA) project. This project’s aim is to develop technologies and methods to facilitate pervasive computing, the idea that all objects within the world around us can have an identity and can be interacted with through network connections. This would essentially give the real world all of the benefits of a 3d virtual world like Second Life, where anything can be interacted with. Not only does our project plan to extend Second Life giving it access to sustainability information of products represented within it, but it could become the basis of providing a true representation of knowledge of all things within Second Life.

1.4 Potential Impact

Within the next few years, this project could yield improved sustainability data for manufacturers, saving them money and helping preserve our environment. It will also allow for this data to be accessed through Second Life, providing an interactive and intuitive way for consumers to be aware of their own environmental impact. In the longer term, this project could help virtual worlds truly represent our own world by giving objects within them true identity. In addition to scripts, 3d models, and a label, we will be able to attach meaning to an object by relating it to its real world counterpart.

2. Related Work

2.1 Key Technologies

Ontology- For an AI to make decisions about a world, it must have knowledge of it. How this knowledge is represented is its ontology. Traditionally, “ontology” is the study of knowledge. Within computer science, it is the study of how a computer represents knowledge. These computer ontologies are usually hierarchical (a truck is a type of automobile, is a type of self-propelled vehicle, is a wheeled vehicle).

Product Sustainability – Product sustainability refers to manufacturing goods in such a way that it could be continued for and indefinite period of time without degrading the conditions of the natural world or permanently consuming resources. A sustainable product is one whose manufacturing process causes a minimum amount of pollution and uses a high percentage of renewable or recycled resources. For the purposes of this project, we are mainly focused on the emission of carbon during manufacturing.

Virtual Worlds- A virtual world is a 3D representation of some space which can model certain aspects of the real world. The most commonly used virtual world, and the one being used in this project is Second Life.

2.2 Related Work

Linking Lexicons and Ontologies: Mapping WordNet to the Suggested Upper Merged Ontology by Ian Niles and Adam Pease - Describes a project in progress to align SUMO and Wordnet [13]. Discusses such issues as determining the completeness of an ontology. [1]

Ontological mappings of product catalogues - Attempts to formulate easily maintainable mappings between enterprise level product ontologies. But, only addresses general ideas and techniques. [2]

LCA and LCC data semantics sharing across product lifecycle processes - Specifically addresses widespread sharing of lifecycle assessment data (LCA). The paper outlines a model for accomplishing this, but has no full implementation. [3]

Semantic knowledge management to support sustainable product design - Argues for the use of formal ontological and semantic markup languages to allow for efficient sharing of sustainability information. [4]

Life cycle assessment ontology. [5]

A formal approach to product semantics with an application to sustainable design - Proposes a framework with which to accurately and comprehensively describe products with sustainability in mind. Does not deal with mapping already existing ontologies. [6]

Modeling considerations for product ontology - Addresses operational concerns developing a new product ontology. [7]

Practical issues for building a product ontology system - Reports on an attempt to construct an operational product ontology. Addresses how to search an ontology engine among other things. [8]

Design of product ontology architecture for collaborative enterprises - Explores using a generic ontology to facilitate interoperability between enterprises. Includes work on maintaining mappings to cope with updates to the ontologies. [9]

Product configuration knowledge modeling using ontology web language - Presents a way to represent products using OWL and SWRL. Uses a general ontology to define commonalities between various product domains. [10]

Protégé - Protégé is an open source java program that allows the user to create, edit, and navigate ontologies. It provides functionality for helping to merge ontologies limited to allowing the ontologies to be edited side by side and manually mapped. It was our intention to use Protégé as

part of the workflow to connect Wordnet to a lower ontology. However, Protégé is incapable of handling the sheer size of Wordnet. [11]

DAML/OWL - a family of languages for representing ontologies developed by DARPA. Programs such as Protégé use OWL as a possible file format. [12]

Wordnet - A lexical ontology that maps the English language. Wordnet uses a hierarchal system to classify words as well as sets of cognitive synonyms (sysnets). It is meant to be a useful tool for computational linguistics and natural language processing research. Over time, several API’s [14] have been created for Wordnet, allowing it to be used in a variety of applications. [13]

WordNet::Similarity - Measuring the Relatedness of Concepts- Describes a fully implemented Perl API for Wordnet specifically meant to facilitate the comparison of words. Implements several proposed algorithms for measure word relatedness. [15]

SUMO - The Suggested Upper Management Ontology attempts to form the ultimate upper level ontology to bridge all ontologies. It is currently mapped to Wordnet but is lacking in lower level implementations that would make it useful for the sustainability project. [17]

In Spring 2009, Anh Chu and Khanh Viet developed an annotation form that enables any user to select an object in Second Life and enter properties about it, which are stored in a remote MySQL database. The user enters commands on a specific channel according to a menu, which is displayed through the chat message box, and can prompt the menu to display properties about specific objects to the chat box (not through a HUD). [18]

Using WordNet-based Context Vectors to Estimate the Semantic Relatedness of Concepts- Describes the use of the vector method to determine if two words within the Wordnet ontology are closely related. [19]

Extended Gloss Overlaps as a Measure of Semantic Relatedness - Describes the “lesk” method for measuring the relatedness of two words within Wordnet. [20]

Creating a search-bot to traverse the worlds of Second Life to amass a database of all objects within the virtual world. [21]

2.3 Related EiA Projects

Our project relates to these other EiA projects, e.g.,

Mirror Worlds project – our project’s work on ontologies could help objects being mirrored within a mirror world behave appropriately by associating that item with it’s proper attributes and behaviors.

Search Spider Project- Searching in SL can provide us information on objects (like chairs and castles), the location, ownership, composition, and behavior (scripts) of such objects, objects that occur near other objects (hospital beds near IV drip machines) – and can populate our instance level ontology. Attaching SL objects to the ontology adds a knowledge layer that could be used in deeper searching.

Soft Controller project – an ontology will eventually be needed to classify the proper actions the soft controller can take with the smart devices being controlled. The API interfaces of a smart device could be stored as part of the device ontology.

Smart Devices – will eventually be classified into an ontology.

Chatbots – A chatbot must parse text and make an appropriate reply. Ontologies are important to chatbots because they determine what the chatbot can recognize and respond to.

3. Architecture

3.1 Requirements or Use Cases

Retailers can access, through some interface, sustainability information on products being manufactured.

o Will require a mapping between NAICS, UNSPC, and the sustainability ontologies.

o Automate this process so that when changes or updates to occur within any of the involved ontologies, the system can automatically update the corresponding mappings

A model of a real-world retail store with models of real items (a mirror world)

o User can ask the items to display their sustainability information.

A real-world retail store with real items

o User can ask the items to display their sustainability information.

3.2 Architecture or Design Space

An “ontology engine” will facilitate the mapping between the various ontologies. All of the ontologies will be connected by this engine, a user will be able to request information from it without dealing with each ontology individually. A virtual world such as Second Life can also draw information from the engine to display it for consumers.

Information on the ontologies, as well as initial mappings and general guidance is handled by the Walton business students (Evan, Callan, and Satheesh). Matt Hardy is working on the usefulness of Wordnet to help the ontology engine determine compatibility between categories in the various ontologies. Keith Eddy is working on retrieving the final information and displaying it within Second Life. Aaron McGinn assisted in analyzing data gathered by a Second Life search spider in order to glean semantic knowledge about objects within Second Life.

Second Life HUD for displaying sustainability information (Keith Eddy):

An HUD is basically a prim that you attach to the interface. There is some code that exists on the web that allows you to display actual text (with a font) on a prim, but it is over 500 lines long, extremely difficult to follow, and ineffective for our purposes in attaching it as an HUD.

(I followed their example and produced this, in which the text disappears once attached as an HUD)

I'm sure it is possible to modify their code to fit our goals, but given the complexity of it and my limited understanding of Second Life scripting, I eventually decided to go with a simpler solution.

I started off by creating a 5x5 grid of pixels, each with the smallest possible dimensions (attaching an object as an HUD takes up much more space on the screen than one would think). After drawing out every letter, number, and practical symbol on grid paper, I manually scripted each of the 25 pixels to display either black (part of the letter/number) or white (not part of the letter/number), depending on the position of the pixel in the grid. Each of the 25 scripts are listening to the same channel for the letter or number to be displayed.

I then duplicated this grid so that a row of 10 characters could be displayed. (Each grid is listening to a different channel, so different letters can be displayed.)

The background behind the grids is the root prim, and it contains the main script that listens for the information, breaks it up into letters, and sends each letter to the corresponding grid of pixels.

I only implemented one row of grids because of the lack of actual information storage and retrieval. (As of now, you have to type a word to channel 33790 to see the results.)

However, adding more is really just a matter of copying and pasting, with a few alterations to the code in both the pixels (the new grids have to be listening to new channels) and the main script (sending the additional letters to the new channels).

So basically, the ultimate idea for this ongoing project is that you keep this HUD "info" object in your inventory, and when you want some information about an object you're walking by, you attach the "info" object as an HUD and then click on the other object. This is assuming that the other object has a record in the external database and a modified "on touch" function, so that when touched it gets information from the database and sends that information to the channel that the "info" object is listening to. There may be a few problems and inefficiencies with this model, but at least the path is more carved out than it was before for future projects to encounter and solve them (especially now that they have a working, albeit primitive, modifiable display system).

3.3. Tasks

Investigate Wordnet as a means to at least semi-automate mapping: Matt Hardy

Display information in Second Life: Keith Eddy

Analyze labels from Second Life search engine and map to Wordnet or product ontology: Aaron McGinn

3.4 Testing

No testing has been completed.

4. Results and Analysis

4.1 Wordnet as a tool for semantic mapping (Matthew Hardy)

Wordnet uses an is-a hierarchy to classify words. For instance:

Here we can see chair traced from its definition all the way down to “entity”, the base node for nouns.

Note that Wordnet also lists other definitions for chair, and presents a separate tree for that definition. In fact, Wordnet stores many other kinds of relationships:

Depending on the exact nature of the analysis being performed in the ontology engine, any of these types of relationships could conceivably be useful in determining relatedness.

Wordnet can also do basic stemming. Note that a search for “running” also returns the base form, “run”:

The first logical step in using Wordnet in a programmatically seemed to be loading it into Protégé. Attempting to do this directly was a fruitless endeavor. Protégé suffers from scalability issues and Wordnet is a massive ontology. The latest version of Protégé, Protégé 4.0, actually has a hard coded limit of 65,000 entities. Earlier versions do have the limit hard coded into them, but Protégé merely went unresponsive during the import process. During discussion, it was determined that Protégé may not be an optimal tool for a component within an ontology engine, as it is only meant as an editor or creation tool.

Luckily other ways of using Wordnet as a semantic matching tool exist besides Protégé. The most commonly mentioned is the Wordnet::Similarity module [16] for Perl which uses Wordnet’s is-a hierarchy to determine the relatedness of words. The module also implements several other methods proposed by various researchers for determining the similarity of two words. One of the simplest ways to determine relatedness is find the lowest node shared between the words being compared. A more accurate algorithm in [16] makes use of the GlossFinder module (a “gloss” is a definition of a word in Wordnet). It uses a “lesk” to measure the overlap of concepts between the words being compared, and a “vector” to measure the difference of definitions (see [19] and [20]).

4.2 Results for Second Life Sustainability HUD (Keith Eddy)

Attach the "info" object as an HUD,

type 10 characters or less to channel 33790,

and the characters will be displayed as block text on the HUD.

4.3 Second Life object data analysis (Aaron McGinn)

The related work previously and currently being done by Josh Eno creates a large database of all the objects within Second Life. This data can then be indexed to create a frequency map of the labels of objects. The only properties that were used were the object’s name and description. A large problem with just directly creating an index of the names and descriptions is that either the name or description could be – and likely is – more than one word long. The indexing program must filter this out and index by individual words within both the name and description in order to produce more accurate results.

Another problem with directly indexing the objects is that the description could be different for the same type of object. For example “chair” would index separate from “chairs”. In order to solve this issue, a stemming program must be implemented to find the base word and then index by it. In this example, both of the terms “chair” and “chairs” would index as “chair” and would again provide a more accurate frequency map.

The program must then use a hashmap to create a count based on the indexed word. Because the object name and description are pulled in the same pass of the program, the same code must be run twice – once for the name, and once for the description. This is the code used to check for an entry in the hashmap (named tokenCount) and increment the count or to create a new index with that word:

if (!stemmedName.equals("")) {words = stemmedName.split(" ");for (int j = 0; j < words.length; j++) {

myword = words[j];

if (tokenCount.containsKey(myword)) {count = tokenCount.get(myword);count++;

tokenCount.put(myword, count);

} else {count = 1;

tokenCount.put(myword, count);}

}}

Results: This hashmap then contains an index of all the words in the objects’ name and description. The hashmap is then written to a file in the format of {indexedword1=frequency1, indexedword2=frequency2, …} The name “object” is the default name when an object is created in Second Life. This indexing found the word “object” 516,370 times in the objects in the database. The most common actual object name is a tree with 105,151 entries. “Chair” is 35th on the list with 24,128 entries. There are 97,541 different words indexed from the database. Of these, 68,523 have five or fewer occurrences of the object. This means that only around 30% of the types of objects in Second Life occur more than five times. There are only 5,151 entries of objects that occur at least 100 times. This would allow an ontology to be created for the most frequent items as a higher priority than the items that are rare.

5. Conclusions

5.1 Summary

Wordnet as a semantic matching tool (Matthew Hardy): Wordnet has the potential to be a very powerful tool for semantic matching as part of an automated or semi-automated ontology mapping tool. API’s exist with something close to this exact goal in mind. More details about the engine itself need to be worked out before a complete implementation of the Wordnet component can be created.

Second Life ontology HUD (Keith Eddy): Second Life's lack of support for dynamic text on HUDs slowed down my progress quite a bit. I didn't get nearly as far into the virtual world ontology sub-project as I had hoped, but I did learn a lot through the problems I encountered along the way. Before this course, I knew nothing about Second Life, but now I am fairly familiar with the virtual world and its scripting environment.

Second Life object data analysis (Aaron McGinn): Creating an index of the words used to describe objects in Second Life gives a good starting point for ontology creation. Since there are more chairs than tables in Second Life, a chair ontology would likely have higher priority than a table ontology.

5.3 Future Work

Usefulness of Wordnet as a semantic matching tool (Matthew Hardy): A clearer picture of the overall structure of the proposed ontology engine is needed to determine the best way to integrate Wordnet into the system. There are several implemented API’s with the Perl module [16] being the most promising. A detailed study of the various similarity functions needs to be carried and each algorithm needs to be evaluated for its usefulness within the ontology engine.

Second Life HUD (Keith Eddy): Now that there is the base of a display system, and since linking an external database to Second Life has been done in a previous semester, the future

work involving ontologies within Second Life seems pretty straightforward. The pieces are there for future projects to connect together. A few tasks include adding more lines to the "info" object/HUD so more information can be displayed, going through and storing information for each object, modifying the touch script for each object, and actually setting up the link between Second Life and an external database or ontology.

Second Life object data analysis (Aaron McGinn): Creating an index was merely the starting point from this angle of ontology. This allows for an object in Second Life to dynamically use this data to find its ontology, instead of having duplicate ontologies in a database for copies of an object. Another useful future development could be to create an ontology based on the indexes. This would allow someone to compare related objects and their frequencies within the virtual world.

Bios

Aaron McGinn <almcginn@uark.edu> – McGinn is a graduate student in CSCE.

Keith Eddy <keddy@uark.edu> – Eddy is a undergraduate student in CSCE.

Matthew Hardy <mkhardy@uark.edu> – Hardy is an undergraduate student in CSCE.

Dr. Craig Thompson, Mentor – Thompson is a professor in the Computer Science and Computer Engineering Department. He leads the Everything is Alive research project that is currently focusing on how to simulate pervasive computing using 3D virtual worlds. See http://vw.ddns.uark.edu.

References

[1]http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.10.4486&rep=rep1&type=pdf

[2]http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.142.8596&rep=rep1&type=pdf

[3]http://books.google.com/books?hl=en&lr=&id=BoqYILA2HuEC&oi=fnd&pg=PA190&dq=%22LCA+and+LCC+data+semantics+sharing+across+product+lifecycle+processes%22&ots=neeQZLfPjP&sig=-ZhtZFxGlmjjo9KAKeYKMQwz3eQ#v=onepage&q=%22LCA%20and%20LCC%20data%20semantics%20sharing%20across%20product%20lifecycle%20processes%22&f=false

[4] http://www.computer.org/portal/web/csdl/doi/10.1109/IACSIT-SC.2009.34

[5] http://isko2007.unileon.es/presentaciones/brascher.pdf

[6]http://library.uark.edu:4550/resserv?sid=google&auinit=L&aulast=Feijs&atitle=A+formal+approach+to+product+semantics+with+an+application+to+sustainable+design&id=doi:10.1162/0747936054406717&title=design+issues&volume=21&issue=3&date=2005&spage=67&issn=0747-9360

[7] http://cs.sookmyung.ac.kr/~jshim/publication/LSLL_OIS06_camera.pdf

[8] http://ids.snu.ac.kr/w/images/e/e9/IC-2005-01.pdf

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[10] http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V03-4SHF4F5-4&_user=1942757&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1126547560&_rerunOrigin=scholar.google&_acct=C000055513&_version=1&_urlVersion=0&_userid=1942757&md5=652a0feaea8d36cbe0616fa9b6930b44

[11] http://protege.stanford.edu/

[12]http://www.daml.org/services/owl-s/

[13] http://wordnet.princeton.edu/

[14] http://wordnet.princeton.edu/wordnet/related-projects/#local

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[16] http://search.cpan.org/dist/WordNet-Similarity/

[17] http://www.ontologyportal.org/

[18] http://www.csce.uark.edu/~cwt/COURSES/2009-01--CSCE-5043--AI/TERM-PROJECTS/2009.htm#_Ontologies_for_Second

[19]http://docs.google.com/viewer?a=v&q=cache%3ATWcmlAyCZpkJ%3Awww.d.umn.edu%2F~tpederse%2FPubs%2Feacl2006-vector.pdf+gloss+vector&hl=en&gl=us&sig=AHIEtbTojRMTDa2yxAF1H5jskzATs6B2Dw&pli=1

[20] http://docs.google.com/viewer?a=v&q=cache:s3uzrU-2bIIJ:www.d.umn.edu/~tpederse/Pubs/ijcai03.pdf+gloss+lesk&hl=en&gl=us&pid=bl&srcid=ADGEEShw0GbLv-lNzFG7Q0nZzEdobU8ibciQO6SNWqCA1zBIB3m9C8eWdjrU05o2rMIfBfcZjdCvL2QKyG90Lg1pNFsVK82ONGiEIbZyLd0gti04pPnGdAWqBxBiFC3yGTo78ni4bbEt&sig=AHIEtbTDlH7h-1-30S-EsyDOpTzY6AdgAA

[21] “Searching for the Metaverse,” Joshua Eno, Susan Gauch, Craig Thompson, ACM Symposium on Virtual Reality Software and Technology (VRST 2009), Kyoto, Japan, November 18-20, 2009, 223-226.

Appendix A – Deliverables Manifest

Protégé based ontologies for NAICS, BEA, UNSPSC, … others TBD plus cross ontology mappings

Protégé based ontology for Wordnet and mapping between Wordnet and sustainability ontologies.

Sustainability ontology service (a web service)

Code to tie sustainability ontology service into Second Life

Code for Second Life indexing program

Results of object indexing program

Demo in Second Life showing querying retail objects for sustainability index labels

o YouTube video

o Demo for Sustainability Research Center

o Demo for RFID Research Center tied into Mirror World demo

Conference paper describing results