Testing prompt engineering methods for knowledge extraction from text

2.1.Generative knowledge extraction

Extracting knowledge triples from text has been a continuous research topic for the Natural Language Processing (NLP) and Semantic Web (SW) communities [20]. Traditionally this task has been approached as a two-step problem. First, the entities are extracted from text as in Named Entity Recognition (NER). Second, Relation Classification (RC) checks whether there exists any pairwise relation between the extracted entities [35,37]. This two-step approach requires additional annotations to identify which entities share a relation.

Recent approaches tackle both tasks simultaneously as a sequence-to-sequence learning problem referred to as End-to-End Relation Extraction (RE). In this approach, a model is trained simultaneously on both NER and RC objectives [9,12,13]. Training both tasks simultaneously in multi-task setups results in improving performance on the end-to-end RE task without additional annotation. However, it still requires significant amount of training data.

Scaling up language models has improved task-agnostic few-shot performance [3]. ICL is an emergent capability of Large Language Models (LLMs) in particular those with billions of parameters such as GPT-3 [3], LLaMDA [26], PaLM [5], LLaMA [27], and GPT-4 [22]. The capability became prominent in GPT models particularly starting from GPT-3 [3]. ICL is a way to use language models to learn tasks given only a few examples without any additional training or finetuning. The model performs a task just by conditioning on prompts, without optimizing any parameters. Our approach focuses on ICL (i.e. prompting, few-shot learning), which combines the capabilities of LLMs with the contextual information available in the text.

2.2.Prompt engineering

Prompts serve as instructive cues for LLMs, directing them to generate desired outputs. They can range from straightforward direct questions, such as “What are the capital cities in the European Union?” to more instructional formulations like “List the capital cities in the European Union.” It is crucial to recognize that LLMs, being inherently context-sensitive, may produce different responses based on the specific formulation of the prompt. Prompt engineering focuses on the development and optimization of prompts [19]. This practice aims to enhance the efficiency of LLMs across a diverse spectrum of tasks and applications. By carefully crafting and refining prompts, researchers and practitioners seek to harness the full potential of LLMs, tailoring their responses to align with the intricacies of various tasks and objectives.

A comprehensive prompt comprises multiple elements, including a task description, context, a request or question, and examples. Certain components of a prompt, such as the task description, can be expressed in various ways, introducing a layer of flexibility. On the other hand, the selection of context and/or examples is inherently dynamic, involving a retrieval mechanism for optimal relevance. In order to incorporate different components in a coherent way, designing a prompt template is essential [19]. For a more in-depth exploration of prompting strategies and associated architectures in NLP, the reader is directed to the survey conducted by Min et al. [21].

2.3.Evaluation challenges in generative approaches

The open-ended nature of open information extraction using generative approaches creates a challenge for automatic evaluation [7]. The expressiveness of LLMs as illustrated in Fig. 1 coupled with the open-endedness of extraction of knowledge triples makes evaluation non-trivial. The reliance on exact matches to predefined targets becomes impractical due to the variability in expression present in generated text. Prior research has predominantly employed a strict evaluation methodology, necessitating exact matches between generated triples and references. This approach proves suitable for evaluating smaller conditional generation models designed for RE such as REBEL [9] that is based on BART [15]. These models, having undergone extensive finetuning on large datasets, and hence consistently produce standardized outputs.

In contrast, larger and more expressive language models like Llama 3 exhibit the capacity to generate a diverse array of output formats that convey similar content to targets as demonstrated in Fig. 1. Unlike traditional approaches where NER and RE models classify or label input tokens, generative language models such as Mistral 7B generate new tokens from a large vocabulary, rather than selecting from a pre-defined set of classes [29]. Given the open-ended nature of outputs from such models and the challenge of aligning them with predefined standards, practitioners often opt for human evaluation. Although this approach is time-consuming and more expensive, it provides a qualitative assessment that better displays the performance of the generative model and the quality and correctness of the generated triples [17,29].

3.1.Simple instruction: Zero-shot, one-shot, few-shots prompts

LLMs acquire knowledge from text corpora during training, as outlined in [23]. The pre-training objective for LLMs typically involves minimizing contextual word prediction errors on extensive corpora. However, achieving an understanding of user intent and effectively following instructions represents a more intricate Natural Language Understanding (NLU) task. This level of understanding does not automatically emerge as a result of scaling; rather, it necessitates a deliberate alignment with user intent, as highlighted in [36].

Prompting with a simple instruction without any task demonstration leverages a LLM’s acquired NLU capabilities and, to some extent, its internal knowledge to execute a specified task. We call this type of instruction only prompts “zero-shot” prompts. Typically, a zero-shot prompt comprises a direct instruction. However, ICL performs well when a prompt is combined with a task description and an example showing the execution of the described task. ICL allows for an expansion in the number of examples within the constraints of the context window. This approach enhances the model’s ability to execute tasks within a given context. In our zero-shot test setting, there is no task description or any task demonstration incorporated into the prompt; instead it is only a simple direct instruction followed by the input text and a signal phrase, i.e. Your answer. The signal phrase is included to demarcate the conclusion of the prompt. The following example illustrates a zero-shot prompt.

Then, we add task demonstration into the prompts, and name versions according to the number of examples used. One-shot prompts have one canonical example along with the instruction. The example, ie. “Text: The Amazon River flows through Brazil and Peru. {“Triples”: [[“Amazon River”, “country”, “Brazil”], [“Amazon River”, “country”, “Peru”]]}” remains the same for all of the inputs. The following example illustrates a one-shot prompt.

One-shot prompting with a fixed example provides a reference point to understand the intended task and output format, and execute it accordingly. Few-shot prompts retain the same instruction and the first example, but the number of examples is extended to three (canonical examples) for the few-shot test. Much like the one-shot setting, these examples remain uniform across all data points. The following example displays a few-shots prompt.

3.2.Selecting examples for task demonstration

LLMs shows significant capability in learning from the context within prompts, underscoring the importance of incorporating relevant examples. One-shot and few-shot prompts exploit canonical examples that remains the same for all of the input instances regardless of their relevance. However, using relevant examples contributes ICL capabilities of LLMs. One notable advantage of ICL is its diminished dependence on extensive amounts of annotated data. In scenarios such as our test setting, training data remains untapped. With a retrieval mechanism, however, training data can be a valuable source for finding contextually relevant examples to input instances, as a result of improving the performance of ICL.

The retrieval of examples from the training set for a generation task can be facilitated through several search mechanisms. The choice of a specific example retriever is non-trivial, as it significantly impacts the retrieved examples and their subsequent contribution to task execution. For our experiments, we employ a state-of-the-art example retrieval mechanism based on maximal marginal relevance [34]. The Maximal Marginal Relevance (MMR) [4] approach selects exemplars that are both relevant as well as diverse. The underlying rationale for the selection of MMR is reducing redundancy in the selected examples. Additionally, a diverse set of exemplars is more likely to showcase complementary signal that is required to extract accurate triples from the input text.

All examples in the following prompt engineering methods are selected by the MMR example retriever [34].

3.2.1.Incorporating retrieved examples into the prompts

The ability of LLMs accessing to knowledge and precisely manipulating it remains limited. To address this limitation, a differentiable access mechanism to explicit non-parametric memory can be employed. Retrieval Augmented Generation (RAG) is a general-purpose fine-tuning recipe for LLMs which combines pre-trained parametric and non-parametric memories for language generation [16]. The RAG formulation involves the convergence of key components, namely a knowledge base/documents, an embedder, a retriever, and a generator. In this process, the knowledge base undergoes an embedding operation facilitated by the embedder. Then, the retriever conducts search on the embedded knowledge base and retrieves the context similar to the input query. Finally, the retrieved context combined with the input is fed into the generation module.

Our approach does not apply RAG formulation directly but instead it augments prompt content by retrieving an example/examples from a randomly selected subset of training data. For that, we randomly sample 300 instances from training data. Test inputs and training sample for example retrieval are embedded using an open source state-of-the-art instruction-finetuned text embedding model [25]. We distinguish this type of prompts which contains task demonstrations selected by a retrieval mechanism from those with canonical examples, and call this type of prompts RAG prompts. For example, an example retrieved by the MMR [34] gets incorporated into the prompt as the task demonstration for one-shot RAG prompts, and three examples for few-shot RAG prompts. The following example shows a one-shot RAG prompt.

The difference between one-shot/few-shot and one-shot RAG/few-shot RAG prompts lays in the incorporation of examples. The retrieving module dynamically selects the most relevant examples from the sample. The following example displays the effectiveness of the retrieval module on a few-shots RAG prompt.

3.3.Chain-of-thought prompts

A chain of thought is a series of intermediate natural language reasoning steps that lead to the final output, this approach is referred as chain-of-thought prompting (CoT). CoT prompting aims to enable LLMs’ complex reasoning capabilities through intermediate reasoning steps. Empirical evidence suggests that this technique facilitates improved performance across diverse domains, including arithmetic problem-solving, commonsense reasoning, and symbolic logic tasks. The efficacy of CoT prompting in enhancing the reasoning capabilities of LLMs is studied in recent research [31].

The exploration of CoT prompting within the context of KE represents a novel strategy aimed at enhancing the ability of LLMs in extracting structured information from text. The premise underpinning this methodology is that by guiding LLMs to reason explicitly about entities, their respective types, and the relations between them, the quality of the extracted knowledge triples may be improved.

In a zero-shot CoT setting, along with the direct instruction the task description of KE is also presented: “A knowledge triple consists of three elements: subject – predicate – object. Subjects and objects are entities and the predicate is the relation between them.” This can also be considered as a concept definition which is different from a direct instruction: “Extract knowledge triples from the text”. Then, the LLM is instructed to “think step by step,” thereby engaging in a sequential reasoning process without prior exposure to explicit examples. In one-shot and few-shot settings, we only employ retrieved examples to demonstrate the execution of the task in three intermediate steps since empirical evidence suggest that retrieved examples contributes to model performance more than fixed canonical examples. These three steps are widely used in traditional KE pipelines, i.e., entity extraction, entity typing, and relation extraction. RED-FM contains entity type annotation along with entities and relations. We use a template to convert entity type annotation into natural language descriptions. An example of our CoT application on KE is demonstrated below.

3.4.Self-consistency prompts

CoT’s initial implementations rely on naive greedy decoding, which does not account for exploring alternative reasoning pathways or correcting misconceptions during the reasoning process. To improve upon this, the concept of self-consistency prompting has been introduced [30]. Self-consistency prompting allows LLMs to consider multiple reasoning paths and offers a mechanism for identifying and correcting errors. This method aims to enhance the decision-making process of LLMs by shifting from the original CoT’s simplistic decoding strategy to a more comprehensive reasoning approach. The advantages and implications of self-consistency prompting are further explored in [30].

In the application of self-consistency prompting to KE, we enhance prompts by incorporating an additional layer of reflection into the task execution sequence. In zero-shot setting, the following instruction is used: “First, think about entities and relations that you want to extract from the text. Then, look at the potential triples. Think like a domain expert and check the validity of the triples. Filter out the invalid triples. Return the valid triples in JSON format.” In one-shot and few-shot settings, execution of the task is demonstrated on the retrieved examples.

The main distinction between CoT and the self-consistency prompts lies in the request for the creation of a preliminary list for potential extractions and then critically evaluating this list. In one-shot/few-shot self-consistency prompts, an erroneous triple in the drafts is deliberately included. Then, the incorrect triple is shown to the LLM, accompanied by an explanation. The following example shows the full application of self-consistency prompting on KE.

3.5.Generated knowledge prompts

Generated knowledge prompting involves leveraging a language model to create knowledge, which is then used as supplementary context when addressing a query. This methodology is noteworthy for its independence from task-specific oversight during knowledge integration and its lack of reliance on a structured knowledge base. The application of the generated knowledge prompting has been found to enhance the performance of large-scale, cutting-edge models across a variety of commonsense reasoning tasks. Notably, this approach has attained leading results on benchmarks encompassing numerical commonsense (NumerSense), general commonsense (CommonsenseQA 2.0), and scientific commonsense (QASC), indicating its effectiveness in strengthening model reasoning capabilities in the absence of traditional knowledge sources [18].

The implementation of generated knowledge prompting for KE seeks to harness the parametric knowledge embedded within LLMs regarding entities and their relationships prior to the actual extraction process. The premise underpinning this methodology is that by using LLMs parametric knowledge about entities such as their type and potential relations, the quality of the extracted knowledge triples may be improved. In our zero-shot setting, the task sequence is augmented with instructions that includes the task description, directing LLM to first generate knowledge concerning the entities mentioned in the text and any potential relations among them. This pre-generated knowledge serves as a primer for the subsequent extraction of knowledge triples.

In one-shot and few-shot experiments, examples for task demonstration are selected by the retriever. The entity type annotation from the dataset which is verbalized with the help of a template is utilized as the demonstration of generated knowledge. The following example shows the full adaptation.

3.6.Reason and act prompts

Combining task-oriented actions with verbal reasoning, or inner speech, is a distinct feature of human intelligence. It is believed that this capability play an important role in human cognition for enabling self-regulation or strategization and maintaining a working memory [33]. Inspired from human intelligence, Yao et al. explore the use of LLMs to generate both reasoning traces and task-specific actions in an combined manner, fostering a cooperative dynamic between the two: Reasoning and Acting, or ReAct, prompt engineering method. ReAct involves generating thoughts, action points, and observations iteratively until a task is completed, making it well-suited for interactive agents. Unlike chain of thought and chain of thought with self-consistency algorithms, which do not involve user interaction, ReAct engages users by breaking queries into intermediate steps and developing thoughts and action plans in an interactive environment. This user involvement leads to more meaningful interactions, helping agents achieve their goals and respond to user queries more satisfactorily.

Our experimental setup does not involve users, so our results do not fully capture the real potential of the ReAct method. ReAct’s strength lies in its ability to generate thoughts, action points, and observations iteratively in an interactive environment, engaging users throughout the process. Without user interaction, our evaluation may not accurately reflect the method’s effectiveness in achieving goals and responding to queries in a more satisfactory manner. However, we demonstrate how the ReAct method can be implemented in the context of knowledge extraction. Despite the absence of user interaction in our experimental setup, we illustrate the method’s capability to generate thoughts, action points, and observations iteratively.

The ReAct prompting method is applied to KE by incorporating additional instructions. After the task description, LLM is asked to generate thoughts and make an action plan until knowledge triples are extracted. For one-shot and few-shot experiments, the execution of the task is demonstrated to LLM on the retrieved example as below.

4.1.Data

We utilize RED-FM, which stands for a Filtered and Multilingual Relation Extraction Dataset, as detailed by Huguet Cabot et al., 2023 [10]. This dataset, refined through human review, encompasses 32 relations across seven different languages and is designed to facilitate the assessment of multilingual RE systems. RED-FM derives its content and structure from both Wikipedia and Wikidata [28]. The reader is referred to the original paper [10] for further details about the dataset.

For the assessment of our selected prompt engineering methods, our focus is concentrated on the human-annotated segment of the RED-FM dataset, specifically RED-FM, English. This subset contains a total of 3,770 data points. Our evaluation process of the different prompting methods utilizes the designated test split, which includes 446 instances. Furthermore, to select examples for task demonstrations for one-shot and few-shot prompts, a random selection of 300 instances has been extracted from the training section of the dataset. The chosen instances within this sample serve as exemplars to illustrate the implementation of the tasks within the prompt.

4.2.LLMs

To evaluate the effectiveness of our chosen prompt engineering strategies, we put them to the test on three different state-of-the-art LLMs: GPT-4, Mistral 7B, and Llama 3.

4.3.Parsing extracted triples

In this study, we observed that extracted triples can be presented in various formats, including lists of lists, lists of dictionaries, enumerated dictionaries, enumerated lists, enumerated strings with a separator (e.g., “-”), enumerated lists of tuples, JSON strings, and dictionaries of triples. The format variability necessitates a robust post-processing approach, as the outcomes may differ based on the method employed.

Our post-processing approach is designed to parse all extracted triples effectively. We operate under two key assumptions:

This dual-assumption approach ensures comprehensive parsing of the generated text, accommodating a wide range of formats and improving the reliability of our post-processing results.

4.4.Wikidata as reference

Wikidata operates as the structured data counterpart of Wikipedia, serving as a collaborative platform where users collectively curate and manage a knowledge graph. Knowledge within Wikidata is systematically transformed into a Semantic Web representation, ensuring that it adheres to standards suitable for integration and interoperability across various web technologies [28]. The test dataset employed in this research is sourced from Wikipedia and Wikidata, including the Wikidata identifiers of the entities and relations. While the dataset is structured to align with the specific entities and relations identified in RED-FM, inputs may semantically contain a variety of triples such as event triples, as exemplified in Fig. 1, that do not fall into RED-FM schema. This implies that the input text could potentially feature extraneous relations, not explicitly captured or categorized by the RED-FM framework. This creates an environment that includes entities and relationships outside the defined scope of the target triples in RED-FM.

The evaluation protocol used in this study involves a post-processing phase for the responses produced by the LLM in response to the prompts. As detailed in Section 4.1, these responses are parsed to identify segments that correspond to linearized triples by using Python’s JSON and REGEX (regular expressions) modules. These segments are then transformed into lists that organize the data into distinct triples, each comprising a subject, a predicate, and an object. After the parsing phase, each component of the triple is validated against Wikidata through an API call, employing a greedy keyword search methodology for the purpose of entity linking. Entity linking is a prerequisite for the implementation of our evaluation framework. Upon successful linking of a triple’s subject, predicate, and object to their respective Wikidata counterparts, a SPARQL “ASK” query is executed. This query serves to ascertain the existence of the triple within Wikidata. If the query returns True, it validates the LLM’s output as a factual piece of knowledge. In cases that the query returns False, it indicates a novel extraction that may be added to Wikidata.

4.5.Ontology-based triple assessment

Fig. 2.

An example of ontology based triple assessment.

To navigate the challenges of open extraction and the expressiveness of LLMs, this study introduces an ontology-based method for assessment of the extracted triples. Ontology based triple assessment aims to reduce dependence on human evaluation. By leveraging the underlying data semantics and property restrictions present in Wikidata, it is possible to automate the validation process for the extracted triples.

The evaluation process starts by querying Wikidata for predicates to obtain their domain, identified by the subject type constraint (Q21503250), and range, identified by the value-type constraint (Q21510865). Additionally, type information for entities is extracted from Wikidata up to four hierarchical levels using the instance of (P31) and subclass of (P279) properties. The method then checks whether the predicate’s domain and range align with the subject’s and object’s types at any level of the extracted hierarchy. This verification ensures that the subject and object types are consistent with the properties of the predicate according to Wikidata constraints. Figure 2 illustrates the assesment of the following triple: “Porsche Panamera”, “manufacturer”, “Porsche”. The first step is checking Wikidata to find corresponding Wikidata identifiers for the triple components. In this case, all components of this triples have a Wikidata identifier: “Q501349”, “P176”, “Q40993”. Then, we query Wikidata to check whether the predicate “P176” has a defined domain and range. “P176” is a well-defined predicate that has both domain and range restrictions.

In a triple where the predicate is “P176”, the subject should be a member of one of the following classes: “software”: “Q7397”, “physical object”: “Q223557”, “model series”: “Q811701”, “concrete object”: “Q4406616”, “product model”: “Q10929058”. Additionally, the object of the triple should be a member of one of the following classes: “human”: “Q5”, “animal”: “Q729”, “profession”: “Q28640”, “organization”: “Q43229”, “factory”: “Q83405”, “fictional character”: “Q95074”, “industry”: “Q268592”, “artisan”: “Q1294787”, “group of fictional characters”: “Q14514600”. The third step is checking the subject and object types up to the fourth level in depth, then comparing those classes with the domain and range of the predicate. The subject “Porsche Panamera”: “Q501349” is a “Model Series”: “Q811701” that aligns with the domain of the predicate “P176”. The property path to “Model Series”: “Q811701” is a follows: “Automobile Model Series”: “Q59773381” » “Vehicle Model Series”: “Q29048319” » “Model Series”: “Q811701”. The object “Porsche”: “Q40993” is an “Organization”: “Q43229” that aligns with the range of the predicate. The property path to “Organization”: “Q43229” is as folows: “Racecar Constructor”: “Q15648574” » “Organization”: “Q43229”. According to our ontology based evaluation framework, this extraction is deemed correct.

5.1.Comparing prompting methods across LLMs

Table 2 encapsulates the major outcomes of our experiments where we try to answer which prompt engineering method performs the best for KE from text. The first column denotes the abbreviation of the applied prompting strategy. For each of the three LLMs, the table reports the performance metrics for all the prompt engineering methods summarized in Table 1. The highest scores in each column are highlighted in bold font. The table includes the following metrics for each prompting method: F1 Score, Domain & Range Match, and Statement Match.

Key observations from the table reveal that all tested LLMs perform poorly on the task when evaluated against the target triples annotated in the RED-FM test set, with the highest F1 score recorded at 0.11. However, the F1 score alone does not sufficiently indicate the quality of the extraction because of the language variation comes with generative language models in open information extraction setting as discussed in detail in previous sections and illustrated in Fig. 1. The Domain & Range Match and Statement Match metrics indicate the ontological alignment of the extraction with reference to Wikidata, providing insights into the quality and correctness of the extraction. These two metrics display more variation than the F1 score, offering deeper insights into the efficacy of the implemented prompting strategy.

Incorporating task demonstrations consistently improves performance, particularly for the Domain & Range Match and Statement Match metrics. The best performing prompt is task demonstrations selected by a retrieval mechanism (RAG few-shot), which achieves the highest F1 scores and Statement Match rates across a majority of models, indicating superior extraction with this method. Notably, Domain & Range Match scores show an improvement of +0.5 for Mistral 7B and +0.1 for GPT-4, although Llama 3’s performance on this metric is negatively impacted by the addition of retrieved examples. Llama 3 seems to benefit more from canonical examples as it is reflected on Domain & Range, and Statement Match metrics. The Self-Consistency and Generated Knowledge methods show significant improvements across all metrics when task demonstrations are incorporated into the prompts, particularly Llama 3. However, these methods exhibit moderate improvements or some degradation in few-shot scenarios, as seen in the performance of Mistral 7B with Generated Knowledge or Llama 3 with Reasoning and Acting prompts in the Domain & Range Match metric. Finally, Reasoning and Acting prompting demonstrate competitive performance with Chain of Thought or Self consistency methods in one-shot and few-shot approaches compared to zero-shot scenarios.

5.2.Ontological alignment

Ontologies serve as the backbone of knowledge representation within knowledge graphs, providing a structured framework that delineates the classes, properties, and interrelationships of the included information [8]. Taking this into account, an extensive analysis of the extracted triples, entities and relations is undertaken. The methodology employed harnesses the hierarchical structure of Wikidata, examining entity types up to the fourth level in depth. Domain and range specifications of relations are also taken into account to ascertain their designated function within the knowledge graph.

The findings, detailed in Table 3, encapsulate the major outcomes of our ontology-based assessment for the extraction performed by Mistral 7B. Results for the other two LLMs can be found in Appendix C as in Table 6 and Table 7. These models demonstrate comparable trends. Notably, both GPT-4 and Mistral achieve a Domain & Range matching rate of 0.61. Upon examination of the aggregate results, GPT-4 exhibits a marginally superior performance. However, for the purpose of demonstrating overall ontology-based triple assessment results, we have opted to focus on Mistral 7B.

The initial column in Table 3, Number of extracted triples enumerates the total count of extracted triples by Mistral 7B for each prompting strategy, computed across the 446 test instances. The second column Triples with a well-defined predicate denotes the quantity of triples linked to Wikidata and featuring a well-defined predicate, i.e. rdfs:domain and rdfs:range. The third column articulates the proportion of such triples relative to the overall extracted set. Additionally, the table includes further details to provide a comprehensive overview. The fourth and fifth columns Triples with a well-defined predicate and both entity types known detail the number of triples with a well-defined predicate and both subject and object are also linked to Wikidata, as well as the percentage relative to the total triples with a well-defined predicate. The last two columns Domain and Range matching triples provide insights into the alignment between the well-defined predicates and the types of entities which form the triple under assessment. Specifically, the seventh column details the number of triples where the type of the subject matches the domain of the predicate, and the range of the predicate aligns with the type of the object. The last column presents the percentage of such domain and range-matched triples relative to all triples with a well-defined predicate. These statistics offer a refined evaluation of the semantic coherence and structural alignment within the extracted triples.

Analyzing Table 3 consolidates our analyses in Table 2. Consistent lowest performance of zero-shot prompts emphasizes the challenges in generating accurate extractions without specific task demonstrations. Zero-shot prompts are designed to consist solely of a brief directive, instructing the model to extract knowledge triples from the text. Interestingly, empirical findings indicate that this minimalist prompting approach yields performance comparable to that of the Self-consistency method, which employs a more intricate strategy of generating multiple outputs and selecting the most consistent answer. Moreover, this direct zero-shot technique demonstrates a performance advantage, outpacing other zero-shot methods by 4-6% in Domain & Range Match. These alternative methods, such as Chain of Thought (CoT), Self consistency (Self-cons), and Generated Knowledge (GenKnow) incorporate additional context or steps to guide the language model. The effectiveness of the straightforward instruction employed in the zero-shot prompt underscores the capability of advanced language models to execute tasks with minimal instructions.

In-context Learning (ICL) leverages the concept of learning by demonstration. This phenomenon enables LLMs to adjust their generated responses to align with the demonstrated examples, emulating the showcased task-specific behavior [14]. Our experimental results supports the phenomena since the introduction of task demonstrations into the prompts significantly enhances performance, with a notable increase, i.e. from 36% (CoT 0-shot) up to 50% (CoT 1-shot), compared to the zero-shot approaches as shown in Table 3. Incorporating a single canonical example notably elevates the model’s performance, evidenced by an increase from a baseline of 0.43 to an enhanced measure of 0.48. However, incorporating an additional pair of canonical examples within a few-shot learning scenario yields a marginal enhancement in model performance, indicated by a mere 2% increment. The marginal impact of additional examples suggests a saturation point in the model’s ability to benefit from increased prompt complexity, in some cases adding more examples negatively impacts the performance as reflected on the results of Chain of Thought and Generated Knowledge prompts.

The selection of demonstration examples in ICL is important due to several reasons that influence the performance of the model. The examples prime the model with an indication of what the task entails. Following the incorporation of examples retrieved from the training sample, observed decrease in the number of extracted triples, e.g, from 2839 to 2498 in Table 3 while F1 score is increasing in RAG few-shot prompts may suggest that the selected examples are influencing the extraction process. This could potentially indicate a refinement in the model’s focus or an alignment of its extraction criteria more closely with the characteristics of the provided examples, consequently impacting the volume of extracted triples. The reported decrease in the total amount of extracted triples, coupled with the observed increase in triples conforming to the Wikidata ontology (Domain&Range match), implies a qualitative enhancement in the extraction method subsequent to the incorporation of the retrieved examples.

Intriguingly, the adaption of prompting methods such as Chain-of-Thought (CoT), Self-consistency, and Reasoning and Acting (ReAct), devised to improve model’s reasoning aptitude does not appear to yield any substantial enhancements in the performance of open knowledge triple extraction. This pattern intimates that the integration of reasoning-focused cues within the prompts does not markedly improve the model’s proficiency in formulating correct responses or tackling intricate problems within the examined experimental framework. However, it can be argued that reasoning about entity types may assist humans in extracting more accurate triples from text.

When examining the efficacy of these three reasoning-oriented prompting methods individually, it is noted that their performance metrics cluster within a similar range. Across the methods tested, there is no recognizable performance gap substantial enough to distinguish any single method as being superior. This lack of significant differentiation in performance further reinforces the findings that, within the experimental parameters set for open knowledge triple extraction, reasoning-enhanced prompting does not markedly benefit the model’s output.

5.2.2.Matching level

Fig. 3.

Analysis of domain & range matching levels of Mistral 7B.

Another aspect that we investigate for a deeper understanding of ontological alignment of extractions is the Domain & Range matching level. The studies presented in Fig. 3 detail the distribution of domain and range matching levels for each prompting method. We have opted to focus on Mistral 7B in this section to ensure consistency and facilitate easier comparison, as we previously showed the Domain & Range match rate in Table 3. The Domain & Range matching level analyses for the other two models exhibit similar trends and can be found in Appendix C, specifically in Fig. 5 and Fig. 6.

The matching levels include 0-Hop (direct match), 1-Hop (match one level above), 2-Hop (match two levels above), and 3-Hop (match three levels above). These levels provide insights into the depth of semantic alignment between entities and predicates. The majority of matches occurs on the upper levels, particularly on the 3-hops that corresponds to the 4th level, underscores the necessity of using different levels of granularity and hierarchy in an ontology.

5.3.Novelty of the extraction

Our empirical results suggest that LLMs possess the capability to mine novel information in the form of knowledge triples from Wikipedia text, which align with the existing schema of the Wikidata ontology. These novel extractions represent the potential for improving knowledge graphs in terms of comprehensiveness. The novelty evaluation, presented in Table 5, also provides a comprehensive overview of the extracted triples by GPT-4 and their alignment with Wikidata. The other two novelty evaluations show similar trends and can found in Appendix C as they are presented in Table 8 and Table 9. We have chosen to present the results of GPT-4 in this section because it yields the highest percentage of triples (26%) that are already in Wikidata, with Mistral 7B closely following with a mere 1% difference.

The structure of Table 5 follows a similar pattern with Table 3. Triples linked to Wikidata columns highlights the number of triples with all components linked to Wikidata, and the percentage compared to the total extractions. Triples already in Wikidata columns provides the count and percentage of the triples that already exist in Wikidata as statements, obtained through SPARQL “ASK” queries, this metric is also referred as Stament Match in Table 2. The central point of interest is the last two columns Novel Triples, showcasing the number of extracted triples whose components are all linked to Wikidata but are not present in Wikidata as a statement. This number reflects the LLM’s capability to extract novel triples that could contribute to completion of knowledge graphs like Wikidata. These novel triples represent potential additions to the existing knowledge graphs.

Examining Table 5, it becomes evident that the LLM demonstrates the capacity to extract a substantial amount of triples that can easily be linked to Wikidata. A significant proportion of the extracted triples, i.e. ranging between 40% to 54% in few-shot settings, can be easily linked to Wikidata. It is particularly notable that within this subset of triples, maximum 29% is already present in Wikidata. The novel extractions present an opportunity to expand the knowledge graph. From the extraction quality perspective, it can also be argued that a higher Statement Match (Triples already in Wikidata) score may indicate superior extraction capabilities. This is because a higher Statement Match score suggests that the model can generate triples that closely resemble those already present in Wikidata. The ability to produce such similar triples implies that the model effectively captures the structure and semantic relationships inherent in the data. Consequently, a higher Statement Match score reflects the model’s proficiency in accurately replicating the factual information found in a reliable and comprehensive knowledge base like Wikidata. This metric, therefore, may serve as an indicator of the model’s overall extraction quality and its effectiveness in generating precise and contextually relevant information.

5.4.Reviewing the best performing prompt method

Fig. 4.

Reviewing RAG few-shot evaluation results.

Among the all tested prompt engineering methods and LLMs, RAG (Retrieval Augmented Generation) few-shot emerges as the most effective strategy, achieving an F1 score of 0.11 by GPT-4, closely followed by Mistral 7B and Llama 3 with an F1 score of 0.10. Precision and recall scores can be found in Appendix C as they are presented in Table 12.

Figure 4 summarizes all evaluation metrics obtained by employing RAG few-shot prompting on Mistral 7B, Llama 3, and GPT-4. Each metric reflects a different aspect of the models’ performance in the task of knowledge triple extraction. Linked Entities measures the proportion of entities linked to an entry in Wikidata. Mistral achieves a score of 0.80, Llama 3 scores 0.78, and GPT-4 scores 0.75. Linked Predicates measures the proportion of predicates linked to entries in Wikidata. Mistral scores 0.46, Llama 3 scores 0.55, and GPT-4 scores 0.48. Statement Match measures how well the extracted triples match the statements in Wikidata. Mistral has a statement match score of 0.28, Llama 3 scores 0.19, and GPT-4 scores 0.29. Domain & Range Match measures how well the extracted triples match the expected domain and range of the predicates. Mistral and GPT-4 both score 0.61, while Llama 3 scores 0.47. In summary, GPT-4 generally performs better in terms of F1 score and Statement Match, while Mistral performs better in precision, Linked Entities and Domain & Range Match. Llama 3 shows relatively higher recall and Linked Predicates.