Knowledge Acquisition

As previously described in this section, the commonsense knowledge present in a knowledge base is traditionally acquired by computer systems through a process of knowledge engineering. This process is tedious and costly. There have been numerous approaches to attempt to deal with this problem. These approaches fall into two general categories. The first of these categories is the use of user interfaces that allow the general public to add to the knowledge of the system. The second of these approaches is to use natural language processing systems to learn facts from web pages. Both of these categories are reviewed in turn in this subsection, and then the relationship of this thesis to knowledge acquisition shall be discussed.

3.2.2.1 Knowledge Acquisition Interfaces

The research into knowledge acquisition interfaces focuses on attempting to allow untrained or minimally trained users to encode and express commonsense knowledge. By reducing the skill-level needed to encode knowledge, it allows for the acquisition task to be distributed among a larger workforce, such as volunteers using a collaborative system over the world-wide web. This reduces the problem of the knowledge acquisition bottleneck in three ways: firstly by reducing the cost of the workforce required, secondly by increasing the speed at which a piece of knowledge can be encoded and thirdly by allowing the task to be distributed among a larger number of people.

One of the first and by-far the largest contribution to layperson knowledge acquisition is that of the Open Mind Common Sense project, which directly aimed to create a knowledge base with a layperson knowledge acquisition method. The earliest and primary foundation work to do with this project is that by Singh et al. (Singh, 2002; Singh et al., 2002; Singh & Barry, 2003).

The key to this work is that it uses the English language itself as the internal representation for the knowledge of the system. Instead of using a machine-readable representation, the project has created rules of inference that work on the English language. Therefore, in this project, people are asked to enter simple facts in English such as “A cat is a mammal”. When inference on the knowledge is conducted, the natural language parsing techniques match various templates such as “X is a Y” and when a template is matched, it allows for various rules of inference to be used, such as disambiguating a rule, paraphrasing the rule, splitting and merging of facts and other heuristics.

The Open Mind Common Sense project was then expanded by Liu &

Singh (2004), introducing the ConceptNet. ConceptNet itself was later ex-panded upon by Havasi, Speer and others (Havasi et al., 2007; Speer, 2007;

Speer et al., 2009) This work applies the various relationships in the Open Mind Common Sense knowledge base into a network of concepts. In the net-work, the nodes represent compound concepts such as “full stomach” and

“eat breakfast”. The connections in the network represent the English

lan-guage relationship. The relations are the parsing templates, so the nodes “full stomach” and “eat breakfast” have the connection labelled “effect of”. By representing the relationships as a network of concepts, it allows for easier computation of reasoning over multiple connections, such as matching pat-terns of relationships for the creation of analogies and quick deduction based upon the transitive nature of some relationships such as “is a”. One exten-sion to this by Speer is to ask the user yes or no questions based on distant relations found within the network such as “Would you find shampoo in the living room?” based on both being found within a house. This allows for new relationships to be found that the user may not think to write down.

A further contribution from the Open Mind Common Sense project is the work by Gupta & Kochenderfer (2004). This work extended the base of the project in two main ways. Firstly, the work focused on indoor commonsense objects, by limiting the scope of the knowledge it was hoped that it would allow for a denser level of knowledge. Secondly, this work looked at the inference of actions using the English knowledge representation.

There has been some work that is not directly a part of the Open Mind Common Sense project but based upon it. The first of these is the system known as Learner (Chklovski, 2003; Chklovski & Gil, 2005). The system uses a small set of initial seed statements and the user selects an object to discuss.

The Learner system then uses the properties of similar concepts as questions to whether they apply to the selected concept. In this way, new commonsense knowledge is prompted and gained. The work on Learner can be seen to be a prototypical version of ConceptNet.

The second piece of work in the area of using an interface for layperson knowledge acquisition is the Verbosity game by von Ahn et al. (2006). This presented the task of creating new knowledge in the form of a computer game.

In the game, two people play cooperatively with each other. In turn, one person selects a word to be guessed and the other has a predetermined set of questions to ask about that word in a “fill in the blank” style. The person who initially selected the word then fills the blank in but must not use the selected word. Points are scored if the guesser guesses the word. Naturally, the questions that are asked are in the knowledge format used by the Open Mind Common Sense knowledge base and so each statement created by the game could be added to the knowledge base, if not already present.

The third piece of work related to the Open Mind Common Sense project is the work of Kuo & Hsu (2011). This work looked at creating a Chinese language version of the Open Mind Common Sense project, leveraging the English language version to aid the knowledge acquisition process. Kuo, Hsu

& Shih (2012) more recently did a piece of work in the area looking at using content from social networks to increase the size of the knowledge base.

While the work surrounding the Open Mind Common Sense project rep-resents the bulk of the work done in the creation of interfaces for layperson commonsense knowledge collection, there are other pieces of work in this area.

The earliest of which is by Witbrock et al. (2005). In their work, they review some methods for layperson knowledge acquisition that generate knowledge for the CYC knowledge base. The work demonstrates four graphical user in-terface systems that allow the user to input different kinds of commonsense knowledge. The first is an interface that takes simple natural language sen-tences and splits the individual facts into key-value relationships based on a chosen concept, further key-value relationships are then prompted for, based on concepts that have a similar set of relationships. The second interface al-lows for the user to match subjects to objects of a given type. The user is given a set of subjects and objects that have been observed to appear together on a web page and the found subjects and objects satisfy various filtering crite-ria. The third interface presents hypothesised statements based on abducing² from the existing knowledge within the knowledge base and then presents the statement to a user. The user then rates the statement for comprehensibil-ity, appropriateness, truth, interest value and plausibility. The final interface uses inductive logic programming to create generalised rules that are then pre-sented to the user with examples and the user is expected to state whether the rule is correct.

3.2.2.2 Knowledge Acquisition through Natural Language Processing

While at least some of the methods of layperson knowledge acquisition inter-faces use natural language processing to process the input of the system, this was only for simple single-line facts. This section looks at the approach of using large natural language corpora from sources such as web pages to obtain commonsense knowledge.

The first work of this sort is by Gao & Sterling (1997) where a limited handcrafted knowledge base was used along with a natural language process-ing system. The knowledge base guided the processor to allow for further knowledge to be acquired. The study was very limited to the narrow use-case of understanding estate agent advertisements. The next work found that looks at mass corpora knowledge acquisition, by Wyatt et al. (2005) uses descrip-tions of activities from the web to recognise the activity from RFID sensor data.

Matuszek et al. (2005) produced an important study into using web corpora for commonsense knowledge acquisition. That study looked at the methods that are used to populate CYC with commonsense knowledge extracted from web pages. As with the earlier work by Gao and Sterling, the CYC knowledge base itself was used to aid the parsing process, to both generate query and check the consistency of the results. The search was performed through the Google search engine and the final knowledge rules from the results of the

2Abduction is a mode of inference where the agent attempts to infer the most likely explanation for an observed consequence.

parser were also verified by searching for an English language version of the rule.

Another study in using web pages to acquire commonsense knowledge is by Hadidi et al. (2010). In this study, the Simple English Wikipedia was parsed into sentences. Of those sentences, those that followed the pattern “noun phrase – verb phrase – noun phrase” were extracted. Those sentences were then used to form a relational network with each verb as the relation type.

The final study looked at in this section, by Mancilla-Caceres & Amir (2010;

2011) combines both approaches, a user interface for laypeople and the use of a large natural language corpus. The approach used was to provide a computer game that requires the user to classify whether a commonsense knowledge rule makes sense and is true. The candidate rules were taken from Wikipedia in the same manner as the study by Hadidi et al. (2010). In their system, a candidate rule was randomly selected either from the existing known rules or from a list of previously used rules. Two human game players were each asked whether the selected rule is true, false, nonsense, or not known. A third computer player made the same choice, based on the previous answer if it is a previously used question, or selects “not known” if the answer is a known answer. After all players had selected an answer, the human players were asked to decide which of the other two players was a human based on what answer the other two players gave, as a form of pseudo-Turing test. Points were awarded if the guess was correct and lost if the guess was incorrect. The authors argue that the inclusion of a computer player stops human players from being able to cheat by agreeing a fixed strategy and so not necessarily provide correct answers, as only when the players adopt the strategy of giving the correct answer can the players have a chance of knowing which player is the computer.

3.2.2.3 Knowledge Acquisition in Relation to this Thesis

The system proposed by this thesis does not fall into either of the existing categories. Instead it learns by observing the environment to accumulate pat-terns of events. These event patpat-terns are encoded in a predicate logic in such a manner that they are usable for inference. For instance, if the system learns that a ball moving upwards will lead in short order to that same ball mov-ing downwards, then if the existmov-ing knowledge base contained facts such as

“people sometimes catch moving balls that move within their reach”, then the system could possibly qualitatively infer the prediction that a ball moving in an arc towards a person may be caught, even though the ball is currently moving away from the person in the vertical axis. Admittedly, this is a rather tortured example due to the current limitations of what can be learned by the system presented. However, this does not discount the fact that the knowl-edge being learned is able to be used within a reasoning context and so it is argued that this system can be seen to be a prototypical knowledge acquisition system.

In the literature, only a single discussion was found of a system that looks at learning commonsense knowledge from purely observing the environment.

This discussion was of the BabyExp project by Poesio et al. (2010; 2011). This was a voluntary research project that last reported its progress in 2011 with an unknown status after this point. The project reports stated that the project aimed to produce work that looked to analyse an automated transcription of an audio and video recording of the first three years of a baby’s life. From this the group wished to develop algorithms that acquired commonsense knowledge from the transcription by attempting to exploit the same “training data” that a child uses to learn its linguistic knowledge. The status of the project in the latest report had carried out an initial look into the automated transcription of the video data. At that point, the project had not produced any methods to learn from the transcribed data.